Response to Peer Review of: Ricardo 

Computer Simulation of Light-Duty 

Vehicle Technologies for Greenhouse 

Gas Emission Reduction in the 

2020-2025 Timeframe 
Response to Peer Review of: Ricardo 

Computer Simulation of Light-Duty 

Vehicle Technologies for Greenhouse 

Gas Emission Reduction in the 

2020-2025 Timeframe 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

Prepared for EPA by
 

Ricardo, Inc.
 

and
 

Systems Research and Applications Corporation (SRA)
 

EPA Contract No. EP-C-11-007
 

Work Assignment No. 0-12
 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments. 

EPA-420-R-11-021 

December 2011 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Peer Review Response Document November 29, 2011 

Introduction 

As the U.S. Environmental Protection Agency (EPA) develops programs to reduce greenhouse gas 

(GHG) emissions and increase fuel economy of light-duty highway vehicles, there is a need to evaluate 

the costs of technologies necessary to bring about such improvements. Some potential technology paths 

that manufacturers might pursue to meet future standards may include advanced engines, hybrid electric 

systems, and mass reduction, along with additional road load reductions and accessory improvements. 

One method of assessing the effectiveness of future light duty vehicle (LDV) technologies on future 

vehicle performance and GHG emissions in the near-term timeframe is through modeling assessments. 

Ricardo, Inc. developed such simulation models and documented the relevant technologies, inputs, 

modeling techniques, and results of the study in its April 6, 2011, Draft Report, “Computer Simulation of 

Light-Duty Vehicle Technologies for Greenhouse Gas Emission Reduction in the 2020–2025 Timeframe” 

contained in the supplement of this document. Ricardo performed this work under a subcontract to 

Systems Research and Applications Corporation (SRA) under EPA contract EP-W-07-064. The report 

documented both LDV technologies likely to be available within the specified timeframe and the 

development of a visualization tool that allows users to evaluate the effectiveness of such technology 

packages in both reducing GHG emissions and their resulting effect on vehicle performance. The 

technologies addressed including conventional and hybrid powertrains, transmissions, engine 

technologies and displacement, final drive ratio, vehicle weight, and rolling resistance were examined for 

seven light-duty vehicle classes. 

EPA then contracted with ICF International (ICF) to coordinate an external peer review of the inputs, 

methodologies, and results described in this report. The review was broad and encouraged reviewers to 

address the adequacy of the model’s inputs and parameters, the simulation methodology, and its 

predictions as well as the report’s completeness and adequacy for the stated goals. Through this process, 

EPA was able to conduct a thorough peer review with reviewers representing subject matter expertise in 

advanced engine technology, hybrid vehicle technology, and vehicle modeling. 

The following five individuals agreed to participate in the peer review: 

1. Dr. Dennis Assanis, University of Michigan 

2. Mr. Scott McBroom, Fallbrook Technologies, Inc. 

3. Dr. Shawn Midlam-Mohler, The Ohio State University 

4. Dr. Robert Sawyer, University of California at Berkeley 

5. Mr. Wallace Wade, Ford Motor Company (Retired) 

ICF provided reviewers with the following materials: 

• Draft project report by Ricardo (2011); 

• The Ricardo Computer Simulation tool; 

• The Peer Reviewer Charge to guide their evaluation; and 

• A template for the comments organized around the Peer Reviewer charge. 

The consensus of the first review based on these materials was that reviewers needed more information 

than was provided in the Ricardo report to complete their review. EPA then requested a second round of 

peer review in which the peer reviewers were provided more detailed information. Ricardo provided 45 

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Peer Review Response Document November 29, 2011 

additional PowerPoint presentations and documents, which included more clarity on assumptions, 

pictures of engine maps, and other pertinent information. Only three of the original reviewers were 

available to participate in the second round of peer review: 

1. Mr. Scott McBroom, Fallbrook Technologies, Inc. 

2. Dr. Shawn Midlam-Mohler, Ohio State University 

3. Dr. Robert Sawyer, University of California, Berkeley 

More detail about the review is available in the ICF report entitled: Peer Review of Ricardo, Inc. Draft 

Report, “Computer Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas Emission 

Reduction in the 2020-2025 Timeframe” (September 30, 2011) contained in the supplement of this 

document. In response to this peer review, EPA issued a follow-on work assignment to SRA (and Ricardo 

as SRA’s subcontractor) to address the peer review comments. The response to the peer review involved: 

• Significant revisions to the draft report 

• A user’s guide to the Data Visualization Tool referenced in the report 

• Specific responses to each of the peer review comments 

The final version of the report includes numerous changes, especially in Sections 4 and 6 of the report, 

and new appendix and attachment materials. The revised report serves as the primary response to the 

overall peer review input. The final report with all revisions is dated November 14, 2011. In addition, 

Ricardo, Inc., as a subcontractor to SRA, is preparing a separate user’s guide to the tool. The final guide 

will be made available to the public by EPA upon final approval of that document. 

Finally, this companion report presents item-by-item responses to each individual comment raised in the 

peer review. The responses reflect discussions about each of the comments between EPA, SRA, and 

Ricardo. Many of the responses refer to the specific revisions within the report that represent the decision 

on how best to address the comment. Others provide a brief response in the event that the comment was 

handled through the general process of revising the report, where the comment can be answered with a 

clarifying response but without any corresponding report revision, or where EPA and the project team 

determined that no revision was warranted given the nature of the comment within the context of the 

study. 

The comments in the following Table 1 are the same as those presented in Table 2 to ICF’s report of the 

peer review findings. In developing the responses, we added a column with a report section reference, if 

applicable. Where no specific report section applies to the specific comment, we used “General” in that 

column. We then sorted the comments based on this column. 

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Other Comments 14 Including the membership of the advisory committee would be

appropriate. 

The Advisory Committee is d

1. 

Completeness Section 3.3 124 Who is on the Advisory Committee? Is it independent? How did The Advisory Committee is d

Technology the program team come up with the comprehensive list of  1. 

SelectionProcess

potential technologies? (From the phone call it sounded like it wasbased on what models Ricardo had in their library. This isconcerning.)

Inputs and Section 3.2 63 The vehicle and technology selection process needs further EPA and Ricardo appreciate 

Parameters Ground Rules for discussion.  My experience in these large simulation studies is section 3 of the final report. N

Study that the vast majority of the time needs to be spent on the is required. 

selection and once selected agreeing upon the model/data. 

Completeness Section 3.3 123 How did the group arrive at the seven vehicles? While it show Some overlap is expected as

Ground Rules comprehensiveness, it’s possible to see that there could be some

overlap.  If one looks at the engine and transmissions packages

vehicles varies based on vehcenter vehicle classes are ca

available in these vehicles already you can see the overlap.   previous work and were used

Reducing the number of vehicles might save on the number of runs you’ll need to make.

moving forward into the futuresmallest class was added to segment and the class 3 trucEPA bridge the gap betweenduty analysis.

Completeness 128 Regarding “Current (2010) maturity of the technology”, how was

maturity ranked?

Ricardo subject matter exper

rankings for the various techn

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Table 1: Response to Individual Peer Review Comments

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation Section 3.4 CSM 77 Is the CSM approach used in other applications? If so it would be In the final report, Ricardo ha

methodology Approach helpful to give citations.  If it was developed by Ricardo, that 

should be stated.  The discussion refers to physics based models, but other than that very little about the type of modeling isdiscussed.  I recall on the phone call that lumped parametermodels were mentioned. There is no discussion of that.

details of the modeling and pillustrate a number of the issuis a standard approach to ansystems with many variablestool for CSM has been used applications, including rocketas well as automotive designapplications.  The report focuof the study, and not the validapproach.  

Other Comments 19 The characterization of the modeling methodology as objective

and “scientific” suggests that the simulation is composed of rigorous, first­principle expressions for the various phenomenawithout using “correlations”, “empirical formulas”, and“phenomenological models”. Are these conditions truly met? Forinstance, in many cases, steady­state dyno test data are the basisof an engine map featuring a certain technology. In other cases, available data were scaled based onempirical/proprietary factors and modifiers. The report should not characterize the study as “scientific” unless data uncertainty isdiscussed and shown in appropriate situations. For example, 

Table 7.1 presents comparisons between simulated and actualvehicle fuel economy performance. Given the various subjectiveassumptions involved in the analysis, the authors shouldcomment whether the noticeable differences in certain cases aresignificant. 

Complex systems modeling i

scientific­based approach to systems, so the language useremains in the final report. Hotaken that the study takes thimodeling approach, and appassumptions and other factorempirical considerations, somqualitative and potentially sub

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

70 No mention or consideration of cylinder deactivation technologies. 

This seems like pretty low hanging fruit, even on downsizedboosted engines, especially if you deploy DVA.

Ricardo subject matter exper

study group and engine man

justify cylinder deactivation o

engines at this time due to sigdurability issues.  Cylinder deincluded in the previous study

Completeness 126 Why wasn’t HCCI technology considered? From the publications

this seems to be a candidate for production in the next 10 yrs.

Ricardo subject matter exper

study group could not justify tfull range vehicle applicationsincluded in the previous study

Completeness Section 4. 

TechnologyReview andSelection

127 Regarding qualitative evaluation of technology “Potential of the

technology to improve GHG emissions on a tank to wheels basis”, since this was a qualitative assessment I think it would be betterto include well to wheels.

A well to wheels analysis was

of this study.

Completeness 129 Citations required for statement “ SI engine efficiency to approach

CI efficiency in the time frame considered”  This representsrelatively large gains in SI technology compared to CI, howeverEU and Japanese engine companies are making bigimprovements on CI as well.

The technology details in Sec

for this general expectation, wthe study focused significant category.  Ricardo's professiothat, given the emission standstatement is a reasonable exstudy time frame. 

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Table 1: Response to Individual Peer Review Comments

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Excerpt

No.  Comment Respons

Other Comments Engine Models 256 The description of the derivation of the engine models in the

report was, at best, vague, as illustrated by the two examplesbelow:Example 1:  Stoichiometric DI TurboThe current research engines of this configuration were reportedto be the Sabre engine developed by Lotus and the downsizedconcept engine developed by Mahle.  Since the engine modeledin the Ricardo report had a peak BMEP of 25­30 bar and usedseries­sequential turbochargers, the Sabre engine is not applicable since it only had a peak BMEP of 20 bar and used asingle stage turbocharger (Coltman et a., 2008; Turner et al., 2009).   On the other hand, the Mahle engine appeared to be directlyapplicable, since it had a peak BMEP of 30 bar and used seriessequential turbocharging (Lumsden et al., 2009).  Since Lumsden

et al. provided the BSFC map for this engine, shown below, it isnot clear why the Ricardo report could not have shown this map, or a map derived from this one, and then described how it wasderived and/or combined with other maps to provide the modelused in the report. (See Exhibit 3)

See revised section 4 for add

engine technology examples

Other Comments Engine Models 258 The report should explain whether the engine model is only a map

of BSFC vs. speed and load, or if the engine model includesdetails of the turbocharger, valve timing, and control algorithms forparameters such as air/fuel ratio, spark/injection timing, EGR rate, boost pressure, and valve timing.

All of these parameters are in

engine map.  See revised sec

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Other Comments Engine Models 259 Advanced valvetrains were included in many of the advanced

engines (page 12). However, the method for applying theseadvanced valvetrains to the engine maps was not provided.  Also, no description of the control strategy for these valvetrains wasprovided.  The report did not provide a description of how thereduction of pumping losses with an advanced valvetrain wasapplied to a downsized engine that already had reduced pumpinglosses.  Therefore, no assessment of how the model handledsynergies could be made.

Section 4 has been revised w

information. 

Recommendations Engine Models 311 Describe what the “other inputs” are to the engine maps. See Chapter 4. Inputs and

Parameters

Section 4 64 There was no model data provided. Engine maps, transmission

efficiency maps, battery efficiency maps etc need to be in the

Appendices.  The black box nature of the inputs is disconcerting. 

The final report adds detail o

issues; see especially change6.8.   

Inputs and

Parameters

Engine Models 306 The engine model is the most important element in successfully

modeling the capability of future vehicles, since it is theresponsible for the largest loss of energy.  It is also one of themost difficult aspect to predict since it involves many complicatedprocesses (i.e. combustion, compressible flow) which must beconsidered in parallel with emissions compliance (i.e. in­cylinderformation, catalytic reduction.)  Because of this, this sub­modelmust be viewed with extreme scrutiny in order to ensure qualityoutputs from the model. 

See revised section 4.1. 

Inputs and

Parameters

SI Engine Maps

and DieselEngine Maps

395 For the 2020 engine maps, there is insufficient detail in this

presentation on how the maps were generated.  Getting accuratesimulation requires careful validation of the model as well as thedata in the model – these engine maps are not sufficiently welldocumented for me to make a judgment on their suitability for theoverall goal of the simulator.  I am well aware that these futureengines do not exist, but there had to be some process of generating these engine maps.  Without more information on thisprocess it is simply not possible to comment on their accuracy. 

See revised section 4.1. 

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Completeness Sections 4.1 and 130 There’s no descriptions of the models. There are only descriptions The final report adds details o

4.2 of the technologies and their perceived benefits.  The reader has

to assume that the same modeling approach was used to model

modeling approach.  See secwhich also reference chapter

each technology, but I know from personal experience this is very is described in Section 6.3.  T

difficult and most likely not the case.  6.1 provides an overall vehic

Recommendations Specific 238 Provide descriptions of the algorithms used for engine control,  See revised sections 4.1 and

recommendations transmission control, hybrid system control, and accessoryfor improvements control.  

Simulation Engines and 31 Specific suggestions regarding models that need more detailed The final report adds detail o

methodology Engine Models

(Sections 4.1 and

coverage: The report lacks detail on the specifics on the different engine design and operating choices. For instance, what was the

ratio, and the use of 0 for LBDdetails the range of EGR use

6.3) compression ratio (and limit) that was used? What is the the NOx treatment/constraint

equivalence ratio, or range considered, for the lean burn engine?How much EGR has been used across the speed and load

also adds a chart for the switincludes text concerning the e

range? What constraints, if any, were applied to the simulations toaccount for combustions limitations such as knock and

temperatures.  These factorsthe fueling maps.  See revise

flammability limits? The NOx aftertreatment/constraints section through 4.2.3 and 4.2.6. could also be expanded.

Simulation Engines and 32 Specific suggestions regarding models that need more detailed The fueling maps were adjus

methodology Engine Models coverage:   the number of cylinders and t

(Sections 4.1 and6.3)

In cases where engine models have been used to generatedmaps, how was combustion modeled? For instance, discussion is

displacement. Detailed combnot within the scope of the st

made as to the heat transfer effect resulting from surface to maps were based on experim

volume changes connected to downsizing. More detail on the heat transfer assumptions that go into the applied heat transfer factor

experience with the incorpora

would be helpful. Was heat transfer modeled based on Woschni’scorrelation? What about friction scaling with piston speed? This

would change with stroke at a constant RPM. Also friction would

change with the number of bearings and cylinders.

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Assumption/ 

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Comment

Excerpt

No.  Comment Respons

Other Comments Advanced

Valvetrains(Section 4.1.1)

56 The report states that advanced valvetrain systems improve fuel

consumption and GHG emissions mainly by improving enginebreathing. Other benefits cited are in supporting enginedownsizing and faster aftertreatment warm­up. Beyond improvingvolumetric efficiency and reducing pumping losses, advancedvalvetrains can enable compression ratio variation to increase fueleconomy and avoid knock, alter the combustion process bymodulating trapped residual, and enable cylinder deactivation toreduce pumping losses. From the report, it is not clear which of the possible benefits of the advanced valvetrain packages havebeen harnessed in each case. A more systematic analysis of technology package combinations is warranted as several aresynergistic but not additive.

The discussion in section 4.2

improvements expected in thuse of a CPS system in the 2timeframe versus the currentother possible benefits of advnoted by this reviewer were nfinal report, as Ricardo, basebelieves these are less importhan the elements included in

Simulation

methodology

Section 4.1.1

Advanced

Valvetrains

82 There is no explanation of how CPS and DVA systems were

modeled. There was only a description of what CPS and DVA is.

See revised section 4.1.1. 

Inputs and

Parameters

Advanced

Valvetrains(Section 4.1.1)

318 Two types of advanced valvetrains were included in the study, 

cam­profile switching and digital valve actuation.  Both of thesetechnologies are aimed at reducing pumping losses at part­load.  The impact of these technologies is difficult to predict usingsimplified modeling techniques and typically require considerationof compressible flow and a 1­D analysis at a minimum.  Even withan appropriate fidelity model, these systems require significant amounts of optimization in order to determine the best possibleperformance across the torque­speed plane of the engine.  It isunclear how these systems were used to generate accurateengine maps given the level of detail provided in the report. 

The final report shows how a

profile switching and digital vimprove the fueling map. Seematerial in Section 4.1.1, inclhelp show the physical approrange of improvement.  

Recommendations Advanced

Valvetrains(Section 4.1.1)

319 Describe how variable valve timing technologies were applied to

the base engine maps.

See response to Comment E

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Recommendations Advanced 320 Describe the process of determining the extent of the efficiency See response to Comment E

Valvetrains improvement. (Section 4.1.1)

Recommendations Advanced 321 Describe how optimal valve timing was determined across the See response to Comment E

Valvetrains variety of engines simulated.(Section 4.1.1)

Completeness Section 4.1.2 DI  131 No discussion of DI control strategy. How was it selected? Was DI controls were not modeled

Fuel Systems there a separate optimization of DI control or was it one size fits sections 4.1.1 and 4.1.2.

all?

Inputs and 21 Some examples of the types of inputs and parameters that would To address this concern, the 

Parameters be helpful to include the following in the report: Any published fuel

economy maps, or other related data, with actual numbers. For

public fueling maps conceptsthe technical transformation o

proprietary maps and data, a normalized representation would be technologies to the future. Seuseful, as well, without the actual bsfc values shown on the map.  Sections 4.1 and revised Sec

Section 4.2.6 provides case sTurbo and Atkinson engines.sections (especially section 6expanded as well. 

Inputs and

Parameters

24 Some examples of the types of inputs and parameters that would

be helpful to include the following in the report: Details of EGRmodeling parameters, such as maps showing percentage of EGRbeing used at various loads.

To address this concern, the 

public fueling maps conceptsthe technical transformation otechnologies to the future. SeSections 4.1 and revised SecSection 4.2.6 provides case sTurbo and Atkinson engines.

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Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Engine Models 255 The report states that engines used in the model were developed

using two main methods (page 14).   

1.  The first method assumed that “reported performance of 

current research engines” would closely resemble productionengines of the 2020­2025 timeframe.  

2.  The second method began with current production engines

and then a “pathway of technology improvements over thenew 10­15 years that would lead to an appropriate engineconfiguration for the 2020­2025 timeframe” was applied.

Both of these approaches are reasonable if: 1.  Appropriate references are provided,  2.  The reported performances for the research engines used are

documented in the report, 

3.  The technology improvements are documented in the report, 

and

4.  The methodology of incorporating the improvements is fully

documented. 

To address this concern, the 

public fueling maps conceptsthe technical walk to the futurSection 4.1.1 and revised SeSection 4.2.6 provides case sTurbo and Atkinson engines.references have also been pr

Inputs and Engine Models 308 The report outlines two methods were used to produce engine To address this concern, the 

Parameters models.  The first method was used for boosted engines and

relied upon published data on advanced concept engines whichwould represent production engines in the 2020­2025 timeframe.  The second method was used with Atkinson and diesel enginesand somehow extrapolated from current production engines to the2020­2025 time frame.  The description of both of these methodsin the report is unsatisfactory.  It also fails to address how the

various technologies are used to build up to a single engine mapfor a specific powertrain.  Validation, to the extent possible withfuture technologies, is also lacking in this area. 

public fueling maps conceptsthe technical walk to the futurSection 4.1.1 and revised Se

Section 4.2.6 provides case sTurbo and Atkinson engines.

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Section 4.1.1.1 65 How were the profiles selected? Was there an optimization See the revisions to sections

Parameters CPS process for each engine size of a given engine type? generally.  Section 4.2.6 prov

fuel map development, and saddresses the engine modelsquestions raised in this commappropriate to answer by add4.1.1.1. 

Inputs and Section 4.1.1.2 66 Was the actuation power requirement accounted for?  What were See the revisions to sections

Parameters DVA the timing/lift profiles and what control strategy was used to select 

the timing/lift profile? Was this an active model or was the

generally.  Section 4.2.6 provfuel map development, and s

timing/lift profile preset and then unchangeable. I would expect  addresses the engine modelsthat as the engine size changes and the boost changes thetiming/lift profile will have to change with it.

Ricardo to add to report that accounted for in Figure 4.4. 

Inputs and Direct Injection 322 Because of the availability of research and production data in this The approach to this is simila

Parameters Fuel Systems area, it is expected that performance from this technology was

used to predict performance rather than any type of modeling

taken to the similar commentSee revisions to section 4.1.2

approach.  That being said, the report does not describe where orhow this data might have been used to develop the fuelconsumption map of the engines simulated nor what data sourceswere used. 

Inputs and Section 4.1.3 67 What about superchargers?  Eaton’s AMS supercharger systems The selection was based on 

Parameters Boosting

Systems

offer high efficiency supercharges that are comparable to turbo’sand don’t have the lag problem.

matter expert judgment for thseries­sequential turbochargemodeling of all boosted engindetails the boosting system a

Completeness Section 4.1.3 132 It says that other boosting systems were included in the study, but  Other boosting systems were

Boosting only turbocharging is discussed. study but turbocharging was Systems system chosen for modeling.

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Engine 172 The feasibility of the following assumptions for the engines See revised section 4.1.3. 

Parameters technology

selection

modeled should be re­examined as indicated below: Turbocharger delays of the magnitude assumed in the model willresult in significant driveability issues for engines that aredownsized approximately 50%.  Although Ricardo assumed aturbocharger delay of approximately 1.5 seconds, the comparabledelay published for a research engine was significantly longer at 2.5 seconds (Lumsden et al., 2009). 

Other Comments Boosting 272 The report states that “various boosting approaches are possible,  The series­sequential turboch

Systems such as superchargers, turbochargers, and electric motor­driven

compressors and turbines.” (page 13).  However, elsewhere the

the modeling of all boosted e4.1.3 details the boosting sys

report states “series­sequential turbochargers” will be used on theStoichiometric DI Turbo engine (page 15). 

Other Comments Boosting

Systems

273 It is not clear in the report how the series­sequential turbocharger

was selected from the variety of boosting devices that were

The selection was based on 

matter expert judgment for th

introduced.  Models for the turbochargers with compressor and series­sequential turbochargeturbine efficiency maps were not provided, so the appropriatenessof these model cannot be assessed. 

modeling of all boosted engindetails the boosting system a

Other Comments Boosting

Systems

274 Comment:  The model should include a single turbocharger

system with less extreme downsizing as advocated by the SabreEngine (Coltman et al., 2008; Turner et al., 2009) as a lower cost alternative to series­sequential turbochargers. 

The selection was based on 

matter expert judgment for thseries­sequential turbochargemodeling of all boosted engindetails the boosting system aaffirmed the recommendationsequential turbos.

Other Comments Stoichiometric DI 

Turbo Engine

280 The foregoing table indicates several significant issues: 2.  The

turbocharger response time for the Mahle engine is 2.5 seconds, whereas Ricardo assumed a time constant of 1.5 seconds.  Such

See revised section 4.1.3. 

turbocharger delays are expected to result in significant driveability issues for engines that are downsized approximately50%. (see Exhibit 7)

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Boosting System 326 Boosting was applied to many of the different powertrain The final report includes addi

Parameters (4.1.3 and 6.3) packages simulated.  Beyond stating what maximum BMEP that 

was achievable, very little is mentioned in how the efficiency of 

to boosting. See revisions in 4.2.6, and 6.3. 

the boosted engines were determined.  Among other factors, boosting often creates a need for spark retard which costsefficiency if compression ratio is fixed.  These complex issues aretied to combustion which is inherently difficulty to model.  Thisaspect of the engine model is not well documented in the report.

Other Comments Stoichiometric DI  283 Turner et al. (2009) indicates that the Sabre engine with a single The selection was based on 

Turbo Engine stage turbocharger provides an attractive alternative to extreme matter expert judgment for th

downsizing with series­sequential turbochargers.  series­sequential turbocharge

modeling of all boosted engindetails the boosting system a

Simulation Turbocharger 33 Specific suggestions regarding models that need more detailed Turbocompressor system eff

methodology systems (Section

4.1.3)

coverage: There is no discussion of turbocharger efficiencies andtheir range. Did the simulations assume current boosting

the torque curve fueling mapspecifics of efficiency, boost 

technologies? Were maps used for this simulation or some other relevant to model. The final rerepresentation? Was scaling used? What were the allowed boost levels?

figure based on a relevant, pand more detailed discussion

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Other Comments Boosting System 57 A two­stage system is indeed promising for advanced Sections 4.1.3, 4.2.6, 6.2, an

(4.1.3 and 6.3) turbocharging concepts. A distinction should be made between

series and sequential configurations. Air flow manipulation can

additional discussion and graturbo lag and the two­stage s

make it a series system (two­stage expansion and compression) how it was applied in this studor a sequential system (turbos activated at different rpm). Variablegeometry or twin­scroll turbines can be good options for the low orhigh pressure stages, respectively. A two­stage turbochargingsystem like this would take advantage of the lean SI exhaust enthalpy, reduce pumping work (or even aid pumping), avoidmechanical work penalties, improve engine transient response, enable high dilution levels (if desired) and probably help keep incylinder compression ratio below 12:1, since significant compression would be done before the cylinder. EGR flow couldbe driven through a low pressure loop (after the turbines) or anintermediate pressure loop (between the turbines). The resultingturbo lag will depend on the details of the configuration and thecontrol logic used. Note that the assumption of a time constant of 1.5 seconds (as stated in the report) to represent the expecteddelay may not hold true in all cases. 

Inputs and 22 Some examples of the types of inputs and parameters that would See figures and text added to

Parameters be helpful to include the following in the report: Baseline maps including section 4.1.3 and 4

used to represent turbomachinery, in actual or normalized form. 

Recommendations Boosting System 327 Describe the process of arriving at the boosted engine maps. The final report includes addi

(4.1.3 and 6.3) to boosting. See revisions in 

4.2.6, and 6.3. 

Recommendations Boosting System 328 Describe how factors like knock are addressed in the creation of  The engine knock strategy its

(4.1.3 and 6.3) these maps.  be similar to today's methods

reflect the effect of knock mit

Inputs and Turbo Lag 391 The data and methods used in modeling turbo lag are appropriate EPA and Ricardo appreciate 

Parameters and there is sufficient explanation and data to support the model.  further response is required. 

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and 403 Engine Model: The trend in engine technology is forced induction The selection was based on 

Parameters (engine downsizing). I think the selection of turbo only is too

limiting. I anticipate variable speed supercharging and other

matter expert judgment for thseries­sequential turbocharge

combination of forced induction. I think the study would do well to modeling of all boosted engininclude this. details the boosting system a

Inputs and 406 Diesel Technology: Curious about the author’s comment  See response to Comment E

Parameters regarding supercharging, “advances to avoid variable speed”. 

Why not variable speed?

Inputs and Section 4.1.4 68 regarding global engine friction reduction, what value(s) was Friction reduction improveme

Parameters Other Engine

Technologies

assigned to that? Was it the same across all engines? If so, why? to be 3.5% across all engine 

extrapolated from the benefit2008 EPA study for 2012­2014.2.6.1)

Inputs and 69 How was the FEAD electrification energy balance accomplished? The load of the electrical coo

Parameters Was additional load placed on the alternator? in the base electrical loads.  M

included in the engine map.

Inputs and

Parameters

Section 4.2

Engine

71 Quantification needed ...“The combinations of technologies

encompassed in each advanced engine concept provide benefits

Use of proprietary data was a

study.  However, in the final r

Configurations to the fueling map....” added a great deal of detail u

available references and soufurther understanding of thesthe study addressed them. 

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation Engines and 30 Specific suggestions regarding models that need more detailed The final report adds text on 

methodology Engine Models

(Sections 4.1 and6.3)

coverage:  It is not clear whether the engine maps in the simulation tool weregenerated based on simulations or existing experimental data, somehow fitted and scaled to the various configurations. In

general, the explanation on how maps were obtained is vague for

such an important component. In one section, the report statesthat the fueling maps and other engine model parameters used inthe study were based on published data. If so, it would be nice tohave a list of the published materials that have been used as theresource. In Section 4.2, the report states that the performance of the engines in 2020­25 were developed by taking the current research engines and assuming the performance of the 2020production engines will match that of the research engine underconsideration. Does this assumption take into account theemission standards in 2020, and do the current research enginesmatch those emission standards? What is the systematicmethodology that has been adopted to scale the performance andfuel economy of current baseline engines to engine models for2020­25? Also, the report lacks detail concerning themethodology of extrapolating from available maps to mapsreflecting the effects on overall engine performance of thecombination of the future technologies considered.

standards to confirm that the LEVIII=SULEV II.  The dieselmaps account for these standreport includes more descriptmethodology, and explains hpublications inform the modesections 4.2, 4.2.5 and 4.2.6.

Other Comments Engine Models 254 Engine models provided the torque curve, fueling map and other

input parameters (which were not specified in the report) (page25).  Since the report stated that “The fueling maps and otherengine model parameters used in the study were based onpublished data and Ricardo proprietary data” (page 26), theiradequacy and suitability could not be assessed.

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them.

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Engine Models 260 In summary, the Ricardo report provided insufficient descriptions

of the derivation of the maps used for all of the engines in thisstudy, which included:

­ Baseline­ Stoichiometric DI Turbo­ Lean­Stoichiometric Switching­ EGR DI Turbo­ Atkinson Cycle­ Advanced Diesel

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them.

Recommendations Engine Models 310 Provide fuel and efficiency map data for all engines used in

simulation.

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of the mrelated issues, and how the sthem. 

Recommendations Engine Models 312 Provide specific references of which published data was used to

predict performance of the future engines.  Some references aregiven, however, it is not clear how exactly these references areused. 

Multiple public references we

were used by the study groupverify the final engine maps bresearch engine data. 

Recommendations Engine Models 313 Wherever possible, provide validation against data on similar

technologies.

Please refer to the revised re

technology/model validation.

Recommendations Engine Models 314 Describe in detail the approach used to “stack up” technologies

for a given powertrain recipe. 

This is inherent to Ricardo's p

models. 

Recommendations Engine

technologyselection

343 Describe in greater detail the approach used to model technology

stack­up on the advanced vehicles. 

This is inherent to Ricardo's p

models. 

Recommendations Engine

technologyselection

344 Provide some form of validation that this approach is justified. Engineering judgment was us

EPA, and the advisory commengines suitable for the vario

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Simulation Section 4.2.1 83 Quantify how did the cooled exhaust manifold/lower turbine inlet  See Figure 4.6 for zone of en

methodology Stoich DI Turbo temp improved the BSFC map.  This is a good example of 

technology interaction...how did the radiator size grow to

where enrichment for in­cylinremoved. The effect on fuel e

accommodate the additional heat rejection; how did the frontal modest, since the Stoichiomearea of the vehicle change to accommodate the larger radiator? engine only has a few operat

range over the US06 cycle. Itspecific heat rejection issuesof advanced technologies wowithout affecting fuel economspace considered, for exampof vehicle mass and frontal aaerodynamic drag.  

Inputs and Engine 171 The feasibility of the following assumptions for the engines See revisions in section 4.2.1

Parameters technology modeled should be re­examined as indicated below: None of the including addition of Schmuc

selection Stoichiometric DI Turbo engines listed as references by Ricardo

limited the turbine inlet temperature to a value as low as the 950C

(2011) reference.  Water­coomanifolds were a technology 

limit in the Ricardo model (Coltman et al., 2008; Turner et al.,  establishing of the 950C limit2009; Lumsden et al., 2009).  Reducing the turbine inlet 

temperature to reach this limit is expected to result in BMEP

made adjustments to the mapaccount for the 950C constra

levels below the assumed 25­30 bar level in the model (which them to incorporate. were obtained in the referenced engine with a turbine inlet temperature of 1025C). 

Other Comments Stoichiometric DI 

Turbo Engine

275 The table below compares several attributes of the Ricardo

Stoichiometric DI Turbo Engine with the Mahle Turbocharged, DI Concept Engine. (See Exhibit 7)

EPA and Ricardo acknowledg

the reviewer's comments.

Other Comments Stoichiometric DI 

Turbo Engine

276 Key content of the Mahle Turbocharged, DI Concept Engine:

­ Two turbochargers in series

EPA and Ricardo acknowledg

the reviewer's comments.

­ Charge air cooler­ Dual variable valve timing­ High energy ignition coils­ Fabricated, sodium cooled valves­ EGR cooler

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Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Other Comments Stoichiometric DI  277 Lumsden et al. (2009) describing the Mahle concept engine stated EPA and Ricardo acknowledg

Turbo Engine that lowest fuel consumption that usually occurs around 2000 rpm

had moved out to 4000 rpm for the series­sequential

the reviewer's comments.

turbocharged engine.  

Other Comments Stoichiometric DI  279 The foregoing table indicates several significant issues: 1.  The It is not possible for an apple

Turbo Engine turbine inlet temperature of the Mahle engine is significantly comparison of today's Mahle 

higher than the limit assumed for the Ricardo engine (1025C vs. 950C).  Reducing the turbine inlet temperature is expected to

advanced engines. Too manyturbocharger efficiency can c

result in lower BMEP levels where the temperature is limited. (see for a given turbine inlet tempeExhibit 7)

Other Comments Stoichiometric DI 

Turbo Engine

281 The table below compares several attributes of the Ricardo

Stoichiometric DI Turbo Engine with the Lotus Sabre Engine. (seeExhibit 8)

EPA and Ricardo acknowledg

the reviewer's comments.

Other Comments Stoichiometric DI  282 The paper on the Sabre engine (Turner et al., 2009) indicates that  See excerpt 279 

Turbo Engine operation at lower turbine inlet temperatures results in a reduction

in BMEP.  However, the turbine inlet temperature for the Sabreengine is still 40C above Ricardo’s assumption.

Other Comments Cooled Exhaust  284 The Ricardo report states, “The future engine configuration was The limit on turbo inlet tempe

Manifold assumed to use a cooled exhaust manifold to keep the turbine

inlet temperature below 950C...” No explanation was provided of 

to avoid prohibitively expensiand is accounted for in the m

how the limit on turbine inlet temperature would affect boost pressure and power. 

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

References Used References 292 References used to establish the basis for the Stoichiometric DI  EPA and Ricardo acknowledg

(Used for thisReview that are

Turbo engine assumptions (page 15 of the report):

1.  Coltman, et al. (2008), “Project Sabre:  A Close­Spaced Direct 

the reviewer's comments.

also listed in the Injection 3­Cylinder Engine with Synergistic Technologies to

Report) Achieve Low CO2 Output”, SAE Paper 2008­01­0138

2.  Turner, et al. (2009), ‘Sabre: A Cost­Effective Engine

Technology Combination for High Efficiency, HighPerformance and Low CO2 Emissions”, IMechE conferenceproceedings

3 Lumsden, et al. (2009), “Development of a Turbocharged

Direct Injection Downsizing Demonstrator Engine”, SAE Paper2009­01­1503

Recommendations BSFC Map

Comparisons

396 I reviewed this but do not have any substantive comments.  All of 

the figures compare pseudo­virtual engines with other pseudoPlease refer to Coltman et al

et al. (2009) for publically dis

virtual engines.  A comparison back to a known, experimentallyvalidated engine current engine would have been more useful for

examples for comparison.

me as it would allow one to see the magnitude of improvementsthat were assumed for the 2020 engines and where on the mapthese improvements were made. 

Recommendations Direct Injection 323 Cite sources of data used to predict DI performance. Predictions were based on R

Fuel Systems with research and production

which is proprietary.

Recommendations Direct Injection

Fuel Systems

324 Describe how this data was used to develop the future engine

performance maps. 

See response to Comment E

Recommendations Direct Injection

Fuel Systems

325 Provide validation of modeling techniques used. See response to Comment E

Other Comments Stoichiometric DI 

Turbo Engine

278 Issue:  The Ricardo report did not discuss the concern that the

lowest fuel consumption in a series­sequential turbocharged

This is true of the referenced

the study.  See significant rev

engine had moved out to 4000 rpm, rather than the usual 2000 4.2.1 and addition of 4.2.6.1.rpm and did not discuss how this concern was handled.

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Other Comments Lean­ 58 The mixed­mode operation considered in the report seems to EPA and Ricardo appreciate 

StoichiometricSwitching

switch between stoichiometric and lean SI direct injectionoperation. There are several multi­mode combustion efforts under

Future analyses could expaninclude these technologies.

(Section 4.2.2) development that encompass several more combustion modes, 

including HCCI and Spark assisted compression ignition withamounts of EGR dilution.

Simulation Section 4.2.2 84 This type of tech points to one of the dangers of optimizing EPA and Ricardo appreciate 

methodology Lean Stoich configuration/technology/control strategy to the drive cycles; that  further response is required. 

Switching is that it has the potential to over constrain the design and effect 

the “real world” performance/fuel economy. 

Other Comments Lean­ 288 The report states that this engine will use a lean NOx trap or a The fuel penalty varies with v

StoichiometricSwitching Engine

urea­based SCR system (page 15).  The use of fuel as a reducingagent was also suggested in the report (page 16).  However, the

other factors and is accounteproprietary model. 

fuel economy penalty associated with regenerating the NOx trapor the reducing agent for the SCR system was not provided.

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Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Aftertreatment/  315 Based on the report, it seems that emissions solutions are The lean­stoich switching poi

Parameters Emissions

Solutions

assumed to be available for all powertrain technology packagesselected.  The report discusses this in some qualitative detail insection 4.2.2 with respect to lean­stoichiometric switching.  Thisdiscussion is somewhat incomplete, in that the way it is written it 

assumes operating at stoichiometry lowers exhaust gastemperature.  In reality, switching from lean to stoichiometric

operation at constant load results in higher exhaust gastemperatures.  Despite this factual inconsistency, it is indeedgenerally better to operate a temperature sensitive catalyst hot and stoichiometric or rich rather than hot and lean – so theconcept of lean­stoich switching is valid even if the explanationprovided is not.  Even without this factual inconsistency, someadditional discussion of aftertreatment systems would be of benefit given that lean­burn gasoline engines are at present awell­known technology for many years that is still problematic withrespect to emissions control.  A separate issue is the topic of fuel

enrichment for exhaust temperature management which will havean important impact on emissions and, if emissions are excessive, reduce the peak torque available from an engine. 

determined to maintain exhauand catalyst operating limits. section 4.2.2.

Simulation Section 4.2.5 86 Why were only the benefits of improved pumping losses or friction Friction and pumping losses 

methodology Advanced Diesel considered? What improvements were assigned to these

benefits? Was it across the board or regional? What about advanced boosting technology for these engines?

targets to increase the efficieAdvanced boosting technolog

stage turbocharging for the aengines. In addition, combus(such as the lean boost enginconsumption.

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation

methodology

87 Ricardo’s expectation for pace and direction: I thought there was

an advisory committee making these decisions.  I’m surprised that they think boost will be limited to 17­23bar. 

The Advisory Committee and 

on many elements of the studRicardo's expertise and expestudy retains this language aexpectation for advanced diethe study timeframe.  The finaraised to 27 bar. 

Other Comments Engine Models 257 The description of the derivation of the engine models in the

report was, at best, vague, as illustrated by the two examplesbelow: Example 2:  Advanced DieselFor the advanced diesel, the report provided the followingdescription:  “...the LHDT engine torque curve and fueling mapswere generated by starting with a 6.6L diesel engine typical forthis class and applying the benefits of improvements in pumpinglosses or friction to the fueling map”.  No description of theimprovements in pumping losses or friction reduction wasprovided and the variation of these improvements over the speedand load map were not provided.  In addition, the baseline 6.6Lengine map was not provided, the 6.6L friction map was not provided and the methodology for applying the improvements tothe 6.6L engine map was not provided. 

As described in Section 4.2, t

and Atkinson engines used thmethodology to translate currfueling maps to the 2020–202methodology is described in d4.2.6.2, with the example of asince a published map can bestarting point. The Diesel engbased on Ricardo ConfidentiaInformation.  

Inputs and

Parameters

Input Data

Review

397 The documentation on the Diesel engine maps was helpful; 

however, it did not discuss how the 2020 engine maps weredeveloped.  This is critical for having confidence in the predictionsmade for the Diesel powertrains in 2020. 

See Section 4.2.5. 

Inputs and

Parameters

405 Diesel Engine Fuel Maps: The presentation shows the

technologies to be deployed, but doesn’t discuss how the 2020bsfc maps were arrived at. It might be helpful to also use thesame method for comparison that the authors used to show LBDI vs EGR.

See Section 4.2.5. 

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Simulation

methodology

Section 4.2.4

Atkinson Cycle

85 How do the 2020­2025 maps differ from the 2010 maps? See new section 4.2.6. 

Inputs and 408 EBDI Engine: Couldn’t find fuel economy benefit discussion in EBDI results shown are for "E

Parameters presentation. Should be done as gasoline or energy equivalent. I 

know CO2 is proportional, but....

Simulation 6.3 Engine 92 Two methods to develop engine models were discussed. It is not  EPA and the program team d

methodology Models disclosed which approach was used for which engine. I 

recommend that one approach be developed for all engines or

approach in designing this streport provides further detail 

both approaches be applied to each engine to converge to a approaches (see 6.3 plus a nsolution. in section 4.2, especially 4.2.

The option used was recommand intended to be an appropgiven the current data availabresearch engine was used bean appropriate starting point,a current production engine wbe the most appropriate start

References Used References

(Used for this

294 References containing supporting information for the hybrid

powertrains: 

EPA and Ricardo acknowledg

the reviewer's comments.

Review that arealso listed in the

5.  Hellenbroich, et al. (2009), "FEV's New Parallel Hybrid

Transmission with Single Dry Clutch and Electric Torque

Report) Support"

6.  Staunton, et al. (2006), "Evaluation of 2004 Toyota Prius

Hybrid Electric Drive System", ORNL technical report TM2006/423

Completeness Section 4.3.1

Micro Hybrids

134 It is implied that electrified accessories aren’t used in this

configuration. I don’t see that as the case. 

This case includes electrified

assumes no electrified coolinarticle cited in revised report expectations for addressing c

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Accessory load 185 The accessory selections listed in Table 5­2 (page 22) appear to The study runs assumed belt

Parameters assumptions be adequate except for the following issue: Belt driven air

conditioning for the stop­start powertrain configuration is not 

situation.  Also see Weissier revised report on recent expe

acceptable for driver comfort.  Electrically driven air conditioning is addressing cooling needs. EPrequired for the stop­start powertrain configuration to providedriver comfort for extended idle periods. 

consider this issue for future 

Inputs and Accessory load 189 Recommendation:  Both mechanically driven and electrically See accessory power require

Parameters assumptions driven accessory power requirements should be clearly provided

in the report.  

Other Comments P2 Parallel

Hybrid (Section

59 P2 refers to pre­transmission parallel hybrid, where an electric

machine is placed in between the engine and the transmission. 

EPA and Ricardo appreciate 

further response is required. 

4.3.2) While the report does not discuss details, there are two possible

configurations: (i) a single clutch, located in between the engineand the electric machine, such as in the Hyundai Sonata, and (ii)two clutches, one in between the engine and the motor, and theother one in between the motor and the transmission, such as inthe Infiniti M35 HEV. The P2 system looks promising to achievegood efficiency, but remaining barriers include cost, drive quality, durability and to a lesser extend packaging. Careful considerationof details is needed to properly assess benefits compared to asingle mode power split. Early reports have indicated that Nissangot 38% mpg increase out of their P2 and Hyundai got 42%, bothwith higher horsepower, as well. However, the P2 Touaregdoesn't seem to meet EPA 2012 CAFE standards.

Completeness Section 4.3.2 P2 135 No discussion of why DCT was only transmission used for P2 DCTs were chosen as curren

Hybrid hybrids instead of CVT and AMT. and to simplify the study scop

representative technology.

Other Comments Transmission 60 What about automatic transmissions with automated clutch This technology was not part

Technologies replacing the torque convertor and lock­up clutch? This is also a and Ricardo appreciate the c

(Section 4.4) possibility. response is required. 

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Completeness 4.4 Transmission 136 What types of CVT’s were in the original mix? Toroidals, push­ CVTs were not part of this stu

Technologies belts, Miller? section 4.4.

Inputs and Transmission 173 The transmission technologies selected for this study, listed in EPA and Ricardo appreciate 

Parameters technology Table 5.3 (page 23) are appropriate.  further response is required. 

selection

Inputs and 6.4 Transmission 76 no efficiency maps, no description of the efficiency maps. What  Efficiency assumptions added

Parameters Models was efficiency a function of?  Typically it’s gear ratio, torque and revised section 4.4 and 6.4.

speed. 

Simulation 4.4 Transmission 88 How were the gear ratios selected? What about shift logic? Gear ratios added to report a

methodology Technologies detailed in section 6.4 of the 

Inputs and Transmission 175 The report mentions that the transmissions include dry sump,  See revisions to section 4.4 a

Parameters technology improved component efficiency, improved kinematic design, super report. 

selection finish, and advanced driveline lubricants (page 22). 

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Transmission

Models

261 Similar to engine models, the description of the derivation of 

transmission models was also vague.  Using the automatictransmission model as an example, “For the 2020­2025timeframe, losses in automatic transmissions are expected to beabout 20­33% lower than in current automatic transmissions fromthe specific technologies described below.”  The specifictechnologies that could provide these reductions appeared toinclude:­ Shift clutch technology ­ to improve thermal capacity of the

shifting clutch to reduce plate count and lower clutch lossesduring shifting.

­ Improved kinematic design – no description of these

improvements was provided.

­ Dry sump – to reduce windage and churning losses.­ Efficient components – improvements in seals, bearings and

clutches to reduce drag.

­ Super finishing ­ improvements expected were not specified.­ Lubrication­ new developments in base oils and additive

packages, but improvements were not specified.

In­house efficiency calculatio

overall average transmissionon benchmarking data, with sbased on the expected improadvanced technologies.

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Transmission

Models

262 In addition to not specifying the improvements expected from

these technologies, no indication was provided of how thesetechnologies were applied to the transmission models.  Forexample,  ­ The report stated that losses in automatic transmissions are

expected to be about 20­33% lower than in current automatictransmissions (page 19).  However, the baseline losses werenot provided for reference and the means to achieve thesereductions were not described.

­ The report stated that energy losses in DCTs are expected to

be 40­50% lower than in current automatic transmissions(page 19).  The details of this reduction were not provided andreferences describing these reductions were not provided.

­ Bearing and seal losses have a greater effect on efficiency at 

light loads than at heavy loads.  The report did not describehow these losses were incorporated in the model.  In contrast to the lack of descriptions of details in the report, PQA andRicardo (2008), as an example, provided the following map of bearing losses in a transmission as a function of shaft diameter and speed.  Similar details for the relevant aspects of the transmission models in this report would have beenexpected. (See Exhibit 4)

Efficiency assumptions added

revised section 4.4 and 6.4.

Other Comments Transmission

Models

263 In summary, the Ricardo report provided insufficient descriptions

of the derivation of the maps for the following transmissions:­ Advanced automatic­ Dry clutch DCT­ Wet clutch DCT­ P2 Parallel hybrid transmission­ PS Power Split hybrid transmission

The transmission model only

and torque/speed for each geefficiency for each gear was dcomponent test data and nora "typical" transmission.

Other Comments Transmission

Models

264 In addition, the models for the automatic transmissions of the

baseline vehicles were not provided, so that their adequacy couldnot be assessed.

The transmission model only

and torque/speed for each ge

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

Transmissions 360 This peer reviewer is not as well­practiced in transmissions as in

other areas in this review.  Because of this, a more limited reviewwas conducted of this aspect of the report.  As with the otherareas of the report, the general concern in this area is theinadequacy of documentation of the modeling approach andvalidation.

See revisions to section 4.4 a

report. 

Recommendations Transmissions 361 Cite data sources used in modeling. See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 362 Validate models wherever possible. See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 363 Fully describe transmission models/maps and processes used to

generate them. 

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 364 Fully describe clutch/torque converter models/maps and

processes used to generate them. 

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 365 Fully describe the process used to generate shift maps and the

operation of the shift controller. 

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 366 Fully describe the lockup controller (i.e. how soon can it enter

lockup after shifting?).

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 367 Fully describe the process for modeling torque holes during

shifting.

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

Recommendations Transmissions 368 Fully describe the model used for the final drive (i.e. 

inputs/structure/outputs). 

See revised Sections 4.4, 6.4

(lock­up). Includes Figure 4.9

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Completeness 4.4.1 Automatic

Transmission

138 No logical explanation for the 20­33% improvement...how was

this number arrived at?

Reference is made to loss re

efficient trans has 12% loss).from approximately 88% to 9gear).  Improvements considand trans experts included reclutch plates, reducing rotatinbetween components, dry sulockup clutch dampers, supeseals and bearings.  The prothese improvements were distechnology committee and aran improved efficiency torqueassumed by EPA based on thsuppliers.  The ZF 8HP transfor the 2012 Chrysler alreadydesign features. 

Completeness 4.4.3 Wet clutch 139 It said these were expected to be heavier, cost more and be less

efficient than DCT’s so why where they included?

Technology selected during s

the study by EPA with input ftext in 4.4.3 which discusses damp clutch systems.

Results Section 4.4.6

Shifting ClutchTechnology

101 “The technology will be best suited to smaller vehicle segments

because of reduced drivability expectations” – not in the USmarket. 

Disagree.  Reduced drivabilit

versus shift efficiency (and imeconomy) will tend to exist mvehicle segment, for the US bdrivability that the reviewer susmall cars are not consideredrepresentative of the class av

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Results Section 4.4.7

ImprovedKinematic Design

102 Assumes a sweeping improvement without identifying a clear

rationale...doesn’t appear to describe a scientific or objectiveapproach. 

Section 4.4.7 could repeat th

reducing the number clutch protating speed differences becomponents is part of improvdesign.  This is similar to the has attained in the 8HP transChrysler 300. 

Other Comments Efficient  61 Efficient components should also include gears since rotating Gears are included in 4.4.10 

Components gears are also a major source of drag. Designing a better profile could also be added to the co

(Section 4.4.9) for gear teeth can reduce drag losses. 4.4.9. 

Completeness 4.4.10 Super 140 How much improvement is attributed to super finishing? This is not attributed to separ

Finishing the suite of improvements in 

revised discussion in section 

Results Section 4.4.11

Lubrication

103 Assumes a sweeping improvement without identifying a clear

rationale...doesn’t appear to describe a scientific or objectiveapproach. 

Technology options were pre

Advisory Committee, and the

based on EPA input. This tecacross vehicles classes as stImprovements in transmissiobased on Ricardo Confidentia

Information.  

Simulation 90 There are several types of rolling resistance models, what type Standard rolling resistance m

methodology was used? into the MSC.Easy5 libraries 

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Completeness 144 There are several types of rolling resistance models, what type

was used?

As stated in Section 4.5: "Sev

technologies were also consito the extent that they help suof vehicle mass, aerodynamicresistance for each of the vehstudy. The potential levels of these "road load reduction" tenot explicitly quantified; ratheincluded as independent inputhe complex systems modelin

Recommendations 158 Where lumped improvements are made, I recommend using

historical results to publish technology improvement curves. Forexample, the parasitic losses (Cd, Crr) should be quantifiable. Vehicle mass reductions as well.

As stated in Section 4.5: "Sev

technologies were also consito the extent that they help suof vehicle mass, aerodynamicresistance for each of the vehstudy. The potential levels of these "road load reduction" tenot explicitly quantified; ratheincluded as independent inputhe complex systems modelin

Simulation

methodology

415 Accessories: I don’t see any discussion on the treatment of 

accessories. I believe from my review of the previous material, that the authors assume that all accessories will be electric. I thinkthat engine driven accessories will play a key role in 2020.

See revised section 4.5. 

Completeness 4.5 Vehicle

Technologies

141 No values for mass, rolling resistance or drag given.  No

discussion of the improvement possibilities.  This would be a goodplace to use historical trends for vehicle mass reduction, aeroimprovements and parasitic loss improvement. 

These were not a focal part o

complex systems tool allows a range of changes (on a perthese various parameters. Seand information in Section 5.2

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Simulation Intelligent Cooling 34 Specific suggestions regarding models that need more detailed See revised section 4.5.1. 

methodology Systems (Section

4.3.1)

coverage: The report describes intelligent cooling systems, but does not provide any estimates of the anticipated reductions infuel consumption over the FTP cycle, though related papers havebeen published in the open literature. 

Simulation Intelligent Cooling 35 Specific suggestions regarding models that need more detailed See revised section 4.5.1. 

methodology Systems (Section

4.3.1)

coverage: Sizing of various cooling components plays a verycrucial role in fuel economy predictions. The report does not provide any detail on how the optimum cooling flow required for a

given engine­ transmission combination was determined. Thiswould significantly affect the oil, coolant and transmission oilpump RPMs, which would in turn significantly change theaccessory loads.

Simulation Intelligent Cooling 36 Specific suggestions regarding models that need more detailed See revised section 4.5.1. 

methodology Systems (Section

4.3.1)

coverage: In addition, the report does not have any discussion onhow modified cooling components (radiator, condenser, etc.)would be sized for more efficient powertrains. For instance, amore efficient engine that would reject less heat would likely need

a smaller radiator and lesser airflow through the radiator; hence, the grill opening could be reduced to cut down on aero drag. Ahigh efficiency transmission will not reject a lot of heat to thetransmission oil; thus, a smaller transmission oil cooler could beused.  

Inputs and

Parameters

23 Some examples of the types of inputs and parameters that would

be helpful to include the following in the report: The baseline

After reviewing the overall co

and EPA did not believe that 

vehicle cooling system and accessory schematic vs. cooling detail to the cooling and othe

system and accessory load schematics of the future enginesconsidered in the simulation.

discussions in the final reportoverall presentation of the stu

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation

methodology

Constraints 41 Specific suggestions regarding models that need more detailed

coverage:  There is no discussion in the report that discusses theconstraints on the combinations that can be implemented in reallife. For example, would a multi­air system that is currentlydesigned for small size engines work for a full size car?

See revised section 5. 

Results Issue with CSM 218 Issue:  The technology “package definitions” (page 22 and 23)

precluded an examination of the individual effects of a variety of 

technologies.  

EPA and Ricardo acknowledg

As with any study, there is a ability to evaluate each variabto contain the study to a man

Results Other issues 220 The Advanced Diesel does not appear to be modeled for the

Standard Car and Small MPV (page 46 and 47), yet no reasonwas provided.

Many technology combinatio

less popular were not modelescope of the study to a reasomaintaining sufficient fidelity.advisory committee precludecertain vehicle classes basedand in a desire to contain the

Results Other issues 221 The P2 and PS hybrid system was not modeled for the LHDT

(page 47), yet no reason was provided. 

Hybrids requiring towing were

Many technology combinatioless popular were not modelescope of the study to a reasomaintaining sufficient fidelity.vehicles are also not in the lig

Recommendations 246 Recommendation:  A default weight increase/decrease should be

added for each technology.  If weight reductions are to be studied, then the user should have to input a specific design change, withthe appropriate weight reduction built into the model, rather that having an arbitrary slider for weight. 

The mass of technologies wa

this study due to the evolvingcomplex opinions regarding tof the RSM tool is responsiblmass from the baseline vehicmass of technologies.

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and 1 The vehicle classes and baseline exemplars are reasonably EPA and Ricardo appreciate 

Parameters chosen, within the constraint that vehicle size, footprint, and

interior volume for each class be locked to the 2010 base year. It is likely that new vehicle classes will emerge by 2025 and/or that these “locking” restraints will be relaxed. 

Future analyses could considthe locking constraints noted 

Inputs and

Parameters

26 The engine technology selection appears somewhat limited in

terms of the selected combinations. For example, why is the

Atkinson engine not boosted as well? Moreover, a variable valve

actuation technology, as common and important as variable camphasing, is not included. As already stated in the introductorycomments, advanced combustion technologies, such as HCCI, are worth considering. More flexibility in the engine and vehicleparameters would also allow better understanding of theimprovements obtained for individual technologies and possiblyeven show some potential synergies not currently identified.

The technology selections an

were selected to provide a reof combinations that reflect thprogram team of some of theexpected combinations acrosduty classifications.  The full considered is set forth in Attafinal report. While EPA agreecombinations are of interest, was a significant undertakingbudget and time, with the optreport is one of the technical EPA's ongoing rulemaking efwas designed to support thatanticipates that others and pecontinue to explore these issstudies that add scope. 

Results 5.2 Vehicle

Configuration andtechnologycombinations

105 Also there is no scientific or objective reason given for the DoE

ranges.  It appears that I can make any vehicle 60% less mass, 70% less rolling resistance etc....This will skew the resultstowards that end of the DoE, when they may not be practicallyachievable.

See edits in section 5.2: "Tab

show the ranges of the continexpressed as a percentage ovalue—used in the DoE studyconventional and hybrid powerespectively. The ranges werbroad, to cover the entire spapowertrain design options, wmargin to allow a full analysistrends."

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Completeness 5.2 Vehicle 142 While the tables show the vehicle configurations, more discussion EPA believes the significant a

Configuration andtechnology

regarding the selection criteria for each vehicle is warranted.  Insome cases this discussion was attempted in the technology

figures added to the final repodescribe the vehicle configura

combinations sections, but I don’t think it should go there.   modeled as part of this study

in section 5.2, as well as expthroughout sections 4 and 6.

Inputs and Hybrid 177 The hybrid technologies selected for this study, listed in Table 5.2 EPA and Ricardo appreciate 

Parameters technology (page 22) are appropriate. further response is required. 

selection

Inputs and DOE ranges 192 The following DOE ranges for Baseline and Conventional Stop­ The reason for the 1.04L nom

Parameters Start (page 23) appear to be appropriate, with the exception of  for the Standard Car was to k

Engine Displacement. Since the default for the Stoichiometric DI Turbo engine appears to be greater than 50% reduction in

metrics equal to today's modof the study kept boost levels

displacement (Standard Car baseline of 2.4L is reduced to 1.04L constant with displacement cfor the Stoichiometric DI Turbo (page 46)), the opportunity shouldbe provided to start with a displacment near the baseline engine

apples to apples comparisonchange. Furthermore, the adv

(2.4L) and progressively decrease it to approximatly 50% (1.04L).  This would require an Engine Displacement upper range of over

engines are already running 

200%.  The model should also have the capabilty of increasingthe boost pressure as the displacement is reduced. (See Exhibit 1). 

Inputs and DOE ranges 193 The following DOE ranges for P2 and PS hybrid vehicles (page EPA and Ricardo appreciate 

Parameters 24) appear to be appropriate (See Exhibit 2) further response is required. 

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and 2 The design of experiment (DoE) ranges, Tables 5.4, 5.5, 8.1, and EPA and Ricardo appreciate 

Parameters 8.2, are reasonable and do not exclude likely sizings. The

assumed alternator baseline and advanced alternator efficiencies

Future analyses could considassess more aggressive redu

are reasonable. The assumed reduction in automatic transmission transmission losses and imprlosses is reasonable, but not aggressive for 15 development years from the baseline year. Similarly the state­of­charge swing

technology.

for hybrid modeling of 30­70% is reasonable, but does not reflect improved battery technology for the 2020­25 period, which shouldallow a greater swing for reduced battery size, weight, and cost.

Simulation Major 202 Descriptions of the algorithms used for engine control,  See revised section 6. 

methodology deficiencies in the transmission control, hybrid system control, and accessory control

report  were not provided.  

Simulation Vehicle model 209 Although the report described the major powertrain subsystems See revisions to section 6, in

methodology issues included in the vehicle models (page 24), a description of the Figure 6.1. 

vehicle model was not provided.

Recommendations Specific

recommendations

234 Provide an overall schematic and description of the powertrain

and vehicle models.

See revised section 6. 

for improvements a.  Show all of the subsystem models/maps used in the overall

model.  

b.  Show the format of the information in each of the subsystem

models (including input, subsystem model, output). 

Inputs and

Parameters

302 The simulation methodology is generally not described in the

report in sufficient detail to assess the validity and accuracy of the

See revised section 6. 

approach.  The models and approach are described qualitatively; however, this is insufficient to truly evaluate the ability of themodeling approach to perform the desired function.  The followingsubsections address specific issues with the models, inputs, andparameters and suggest possible corrective actions to addressthese issues.

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Recommendations Vehicle model 304 List the dynamic equation describing the longitudinal motion of the Dynamic equations for longitu

issues vehicle. those incorporated within the

libraries.  

Inputs and Engine 342 There are a host of different technologies superimposed to create This is the purpose of the em

Parameters technology

selection

the future powertrain technologies.  There is not a clear processdescribed on how this technology “stack­up” is achieved.  For

model and BSFC maps ­ to a"stackup".  These have been 

instance, an advanced engine technology may allow for greatly Ricardo has based maps on 

improved BMEP.  Greatly improved BMEP often comes at theexpense of knock limits which are difficult to model even with

much of this content already.redundant with other comme

sophisticated modeling techniques.  In this simulation, manylayers of powertrain technology are being compounded upon each

responses). 

other which will not simply sum up to the best benefits of all of thetechnologies – there are simply too many interactions.  At thelevel of modeling described, which are maps which are altered invarious unspecified ways; it is not clear how the technology stackup is captured. 

Recommendations Vehicle model

issues

381 List the dynamic equation describing the longitudinal motion of the

vehicle

See Excerpt 1. 

a.  NOT ADDRESSED IN SUPPLEMNTAL MATERIAL

REVIEWED

Recommendations Vehicle model 382 List all parameters used for each vehicle class for simulation Please see expanded baselin

issues a.  NOT ADDRESSED IN SUPPLEMNTAL MATERIAL appendix

REVIEWED

Simulation 91 Was coast­down data from the baseline vehicles obtained or See revised Sections 6.1 and

methodology where the coefficients of rolling resistance and Cd modified to get 

the data to match?

Results 6.1 Baseline

Conventional

106 Results were compared to the EPA Vehicle Certification

Database.  These results often include correction factors and

The report accurately describ

for this study. The Certificatio

Vehicle Model allowances that aren’t documented on the sticker.  Recommend

that actual testing be run to perform the benchmark. 

information comes from actuaon the baseline vehicles usinresults. 

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Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations 154 Should use coast down data for baseline vehicles to model

parasitic losses.

See revised Sections 6.1 and

Simulation

methodology

Major

deficiencies in thereport 

199 An overall schematic and description of the powertrain and

vehicle models and the associated subsystem models/maps werenot provided.  Only vague descriptions were included in the text of the report. 

See revised report Section 6.

Figure 6.1.  

Simulation

methodology

Vehicle model

issues

210 Issue:  A description of how aerodynamic losses, tire rolling

losses and weight are handled in the model was not provided.

The starting point for the veh

use the existing road­load coEPA Test Car List, which aretarget terms for the chassis dKnown as target A­B­C termswere used to derive the physrolling resistance, linear losseaerodynamic drag. These proused in the simulation to provload on the vehicle at any givrevised Sections 6.1 and 7.1

Simulation

methodology

297 The vehicle model is reported as “a complete, physics­based

vehicle and powertrain system model” ­ which it is not.  The

modeling approach used relies heavily on maps and empiricallydetermined data which is decidedly not physics­based.  Thisnomenclature issue aside, the model is not described in sufficient detail in the report to make an assessment in this area.  Anexcellent example of this is the electric traction drives and HEVenergy storage system for which the report mentions no details, even qualitative ones, on the structure of the models.  

See revised section 6.1. 

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Vehicle model 303 The vehicle model is described as “a complete, physics­based See revised Section 6.1. Bas

Parameters issues vehicle and powertrain system model” developed in the

MSC.Easy5TM simulation environment.  This description is not particularly helpful in defining the type of model as portions of themodel are clearly not physics based, such as the variousempirical maps used or sub­models like the warm­up model whichis by necessity an empirical model due to the complexity of thewarm­up process compared to the expected level of fidelity of themodel.  It is assumed that a standard longitudinal model accounts

for rolling losses, aero losses, and grade is used to model theforces acting on the vehicle.  Input parameters for the vehiclemodel are not described.  The baseline vehicle platforms arelisted, however, the relevant loss coefficients are not provided(rolling resistance, drag coefficient, inertia.)

parameters are tabulated in A

Inputs and Baseline vehicle 163 Recommendation:  Since the baseline vehicles modeled were It is important to note that, fo

Parameters subsystem

models/maps

2010 production vehicles, the models/maps for the subsystemsused in these vehicle models should be included in the report before it is released. 

validation phase, baseline veestablished just using the givdata or the raw validated vehresults. Rather than using thevehicles and corresponding fresults, a new set of baseline determined to facilitate a unifbetween the advanced (futurtoday’s current technologies.

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations 240 Recommendation:  Since the baseline vehicles modeled were

2010 production vehicles, the models/maps for the subsystemsused in these vehicle models should be included in the report before it is released. 

It is important to note that, fo

validation phase, baseline veestablished just using the givdata or the raw validated vehresults. Rather than using thevehicles and corresponding fresults, a new set of baseline determined to facilitate a unifbetween the advanced (futurtoday’s current technologies.

Results 44 There is also no baseline hybrid configuration and no validation of 

the hybrid model. Due to the increased complexity of thesevehicle systems, it is important to ensure the parameters andassumptions are valid.

No validation was performed 

architectures as no P2 hybridproduction during the study. TP2 architecture was simulateroad loads to the Toyota Priueconomy figures were higherPrius. Section 6.2 presents thconfigurations.  The revised Sfully describes the hybrid appstudy. 

Inputs and Baseline vehicle 164 A major omission was that a baseline model of a hybrid vehicle,  No P2 hybrids in production n

Parameters subsystem

models/maps

which is significantly more complex than the baseline vehicle, wasnot developed and compared to available EPA fuel economy test data for production hybrid vehicles.  

validate against.  Any producbe optimized for specific engmachine/battery.  Study assuleave a generic, accurate concover the design space. Alsowas not part of the scope; thecompared to the 2011 HyundSee revised Section 7.1 for fu

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Baseline vehicle 165 Recommendation:  A baseline model of a hybrid vehicle should be No P2 hybrids in production n

Parameters subsystem

models/maps

developed and compared to 2010 EPA fuel economy test data forproduction hybrid vehicles.

validate against.  Any producbe optimized for specific engmachine/battery.  Study assuleave a generic, accurate concover the design space. Alsowas not part of the scope; thecompared to the 2011 HyundSee revised Section 7.1 for fu

Inputs and

Parameters

6.3 Accessories 73 I think the assumption that LDT cooling fans will be engine driven

is incorrect.  The new F150’s have electric fans.

This issue was not considere

to warrant considering re­runruns.  If the commenter is corlikely normal configuration in would be some modest gain reduced CO2 emissions. 

Simulation 93 Regarding engine downsizing, I’m not sure that the scaling Scaling method, including he

methodology approach applies to boosted engines, especially engine with

multiple compressors as well as DVT and CPS technology.

standard energy approach.  Ascaling curve. Methodology isTurbo engines based on RicaSee revised Section 6.3.

Simulation 94 Turbo lag applied as a first order transfer function with a time Time constant selected base

methodology constant.  How was the time constant selected? Was it validated?

How was the improvement attributed to turbo compounding

experience, and validated agFigure 4.5. See revised Secti

modeled? discussion on how the variou

were modeled. 

Inputs and Engine 167 Setting the minimum per­cylinder volume at 0.225L and the 3­cylinder engines have been

Parameters technology minimum number of cylinders at 3 is appropriate.  However,  many years, and Ford has pl

selection achieving customer acceptable NVH with 3 cylinder engines cyl in 2013.

continues to be problematic.

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Engine Scaling 289 The report states, “The BSFC of the scaled engine map is

...adjusted by a factor that accounts for the change in heat lossthat comes with decreasing the cylinder volume, and therebyincreasing the surface to volume ratio for the cylinder” (page 26).  This is a directionally correct correction.  However, specific valuesfor the correction should be provided, together with references tothe data and methodology used to derive the values used.

The correction factors are de

data from benchmarking andprograms.  

Other Comments Engine Scaling 290 Issue:  The report states, “...downsizing the engine directly scales

the delivered torque, ...” (page 26).  However, since there will beincreased heat loss from the smaller displacement cylinder, thetorque would be expected to be less than the directly scaledvalues for the same fueling rate. 

The fueling rate itself is modi

torque. It is never stated that same for a given fueling rate.

Inputs and

Parameters

Engine Models 307 The engine models are “defined by their torque curve, fueling

map, and other input parameters.”  This implies that the maps arestatic representations of fuel consumption versus torque, enginespeed, and other unknown input parameters.  Generally speaking,representing engine performance in this fashion is consistent withtypical practice for this class of modeling.  This comment dealsonly with the representation of the engine performance insimulation, the generation of the data contained within the map ismuch more challenging.

EPA and Ricardo acknowledg

the reviewer's comments.

Inputs and

Parameters

Engine

Downsizing

329 Engine scaling is used extensively in the report.  Basic scaling

based on brake mean effective pressure is common in modelingat this level of fidelity, thus, this does not need any specialdescription.  However, the report mentions some means of modeling the increased relative heat loss with small displacement engines which is not a standard technique.  The model or processused to account for this effect should be explicitly described giventhat engine size is one of the key parameters in the design space.

See revisions to section 6.3 a

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Recommendations Engine

Downsizing

330 Properly document the process of scaling engines. See revised section 6.3. 

Recommendations Engine

Downsizing

331 Validate the process used to scale engines. Engines were scaled linearly

displacement and torque. Thconsumption maps were modheat loss effect curve. The cuand simulation results that indBSFC with increased individu

Simulation Scaling 393 With one exception, the scaling methodology appears to be sound EPA and Ricardo acknowledg

methodology Methodology

Review

given the information provided in the presentation.  The curveused to adjust BSFC with displacement ratio is not supported with

the reviewer's comments. Sesection 6.3.

data or any citation of where it originated.  The motivation for thiscorrection seems valid, however, it needs to be supported withdata. 

Inputs and 402 Engine Model: I see data on the HEDGE engine technology but  The HEDGE is an example o

Parameters no mention of it in the list of engine technologies unless it’s the engine. 

high EGR DI gasoline engine.

Inputs and

Parameters

Engine

technology

168 Issue:  The description of the derivation of all of the engine

models/maps was insufficient.

Use of proprietary data was a

study.  However, in the final r

selection added a great deal of detail u

available references and sou

further understanding of thesthe study addressed them. Amaps relevant to the engine mthe effects of the valve actuasystem, and boost system wethe final torque curves and futherefore subsystem performturbine and compressor efficirelevant to this study. 

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Assumption/ 

Topic

Comment

Excerpt

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Inputs and

Parameters

25 Some examples of the types of inputs and parameters that would

be helpful to include the following in the report: Details of warm­upmodel parameters, such as ambient temperature; warm up frictioncorrection; cold start fuel consumption correction factor; generation of heat rejection maps for various combinations in thesimulation matrix.

After reviewing the overall co

and EPA did not believe that detail to the warm­up model dfinal report would assist in thepresentation of the study find

Simulation

methodology

Warm­up

methodology(Section 6.3.1)

37 Specific suggestions regarding models that need more detailed

coverage: This section talks about using engine warm­up profileduring the cold start portion to ascertain additional fuelingrequirements. It talks about a correction factor to account for thisadditional fuel. How was this factor determined? Has a different correction factor been used for various engines? For instance, fora lean­burn engine that reject less heat, the oil warm­up is slowercompared to a baseline engine. Was a new heat rejection mapgenerated to account for start­up enrichment while modeling thewarm­up? What is the ambient temperature that has beenconsidered while performing the FTP 75 fuel economy test? Havethe viscous effects of engine oil considered in the warm upsimulation? How have the friction losses for various valvetrainengine combinations been modeled?

See revised section 6.3.1 for 

assumptions.

Results Section 4.5.1

Intelligent CoolingSystem

104 The system as described seems more appropriate for regulated

emissions reduction opportunity rather than fuel economy orGHG.  I think these  systems enable engine control strategies that aren’t part of this study that would have a greater impact on fueleconomy than warming up the engine faster. 

See revised section 6.3.1. 

Other Comments Warm­Up

Methodology

285 “Ricardo used company proprietary data to develop an engine

warm­up profile” which was used to increase the fuelingrequirements during the cold start portion of the FTP75 drive cycle(page 26).  Since this data was proprietary, no assessment of itsappropriateness can be made. 

See revised section 6.3.1 

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Warm­Up

Methodology

287 Issue:  No explanation was provided to clarify when the “engine

warm­up profile” is used and when the “correction factor” is used.  Therefore, the appropriateness of the warm­up methodologycannot be assessed.

See revised section 6.3.1. 

Inputs and

Parameters

Warm­Up

Methodology

332 The report describes a 20% factor applied to bag 1 of the FTP­75

for baseline vehicles and a 10% factor applied to the advancedvehicles.  The motivation for these factors is describedqualitatively and is valid, as many organizations are currentlyinvestigating strategies to selectively heat powertrain componentsto combat friction effects.  However, the values for these factorsthat were selected are not backed up with any data or citation.  It is suspicious that the two values cited are such round numbers ­the data from which these numbers are derived should be cited.  Because of the complexity of this phenomenon, some type of 

empirical model is justified.  The model described in the report isnot sufficiently validated to judge its suitability.

See revised section 6.3.1. 

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation Cold Start  384 The correction used to adjust fuel economy for cold start is Section 6.3.1 details the warm

methodology Correction

Methodology

described in this presentation.  The method is based on twopieces of information:1.  A set of three tests from a single vehicle’s instantaneous fuel

multiplication correction factor

2.  A piece of EPA data which shows a fleet­wide average for

2007 of the instantaneous fuel multiplication correction factor

The instantaneous fuel multiplication correction factor is not described in the presentation, however, it is assumed to be thesum of the “short term fuel trim” and “long term fuel trim.”  If this isthe case, then this value doesn’t correlate to increased fuelconsumption, but rather, to errors in the injector characterizations, fuel property assumptions, and air estimation algorithm in theengine controller.  The engine controller is going to maintainstoichiometry based on oxygen sensor measurements, these trimvalues are the simply the feedback correction values required todo this based on the feedforward algorithm in the ECU.  By way of example, I could alter the fuel tables of an ECU by 15% which

would cause the feedback control system to correct by anopposite 15%.  This would not change the fuel consumption of thevehicle once the control system has corrected it, which wouldhappen in seconds.I don’t disagree necessarily with the magnitude of the outcomes, since they are based mostly on EPA bag fuel economy data.  If I am correct in my understanding of the correction factor then themethod is not valid.

for the study. 

Inputs and 401 Battery Model: Overall the battery model is sound; however, I  Cold testing was considered 

Parameters don’t understand why cold modeling is included.  The FTP testing

doesn’t include cold testing therefore only 25C and up should beincluded and the battery is consistent at those temps. 

this study.  See revised sectio

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Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation 6.3.1 Warm­up 95 How was the engine warmup modeled? Is it a first order transfer Engine warmup assumptions

methodology Methodology function with a time constant?  It said proprietary data was used, 

but how? Does the method allow for different warmup dependingon size and engine technology?

revised sections 6.3.1 and 6.

Results 6.3.1 Engine

WarmupMethodology

107 Were there hot and cold engine maps? No mention. Engine warmup assumptions

revised sections 6.3.1 and 6.

Results 6.7 Driver Model 115 How was the soak modeled? Were there hot engine maps and

cold engine maps?

Engine warmup assumptions

revised sections 6.3.1 and 6.

Simulation Accessories 38 Specific suggestions regarding models that need more detailed The report accurately portray

methodology Models (Section

6.3.2)

coverage: Alternator efficiency has been assumed to be constant around 55% for baseline. In the current baseline vehicles thealternator efficiencies do vary with the temperature and load. 

was handled in the study. EPconsider this issue for future 

Simulation Accessories 39 Specific suggestions regarding models that need more detailed The study is based on 2­cycle

methodology Models (Section

6.3.2)

coverage: Has AC compressor load been considered in any of thesimulations? In some of the new cycles being proposed by EPA, it is required that AC remains ON throughout the cycle. Hence, management of the AC load is very critical.

testing that does not include match current rulemakings. 

Inputs and

Parameters

74 Limiting the alternator to 200A is very conservative, particularly if 

the system voltage stays at 14V.

The use of a 200A alternator 

production trends while streamodeling process.

Simulation 6.3.2 Accessories 96 Constant alternator efficiency and load is not a very good See small edits to this section

methodology assumption.  New alternator technologies and higher alternator

loads due to electrification and increased electrical demands.  Willthe future still continue to use 14V or will higher voltages beused?

assumptions used in the modfor those assumptions.

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Accessory load 186 Input values The 55% to 70% alternator e

Parameters assumptions Alternator efficiency was increased from the current level of 55%

to 70% to reflect “an improved efficiency design” (page 26 and27).   

is a legacy of the 2007­2008 value for the baseline and addiscussed with Ricardo and wconfidential discussions with 

Inputs and Accessory load 187 Comment: Justification for the increase in alternator efficiency Electrical accessory loads we

Parameters assumptions from 55% to 70% should be added to the report with references

provided.  Alternator efficiency as a function of speed and loadmay be more appropriate than a constant value.

constant value throughout drefficiency map adds little valuover drive cycle are relativelystop/start and "smart" managconstant.  Assumptions werereasonable by committee andbest practice in industry.

Inputs and

Parameters

Accessory load

assumptions

188 Accessory power requirements were not provided, such as shown

in Figure 3­3 of PQA and Ricardo (2008), for example. 

See accessory power require

Recommendations 245 Recommendation:  Both mechanically driven and electrically

driven accessory power requirements should be clearly providedin the report.  

See Tables 6.3 and 6.4 in Se

Other Comments Accessory

Models

269 None of the accessory models were not provided for review, so

their adequacy and suitability cannot be assessed.

See accessory power require

Other Comments Accessory

Models

270 The accessory loads vs. engine speed for the conventional belt 

driven accessories were apparently removed from the enginewhen electric accessories were applied.  However, theconventional accessory loads as well as the alternatorloads/battery loads for the electric accessories were not provided.

See accessory power require

Other Comments Accessory

Models

271 In contrast, as an example, PQA and Ricardo (2008) provided the

following map of an electric water pump and AC compressor drive

efficiency.  Similar maps for all accessory models would beexpected in this report. (See Exhibit 6)

See accessory power require

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Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Accessory load 335 The accessory model is divided into electrical and mechanical The 55% to 70% alternator e

Parameters assumptions loads.  The electrical sub­model assumes alternator efficiency’s of 

55% and 70% for the baseline and advanced vehiclesrespectively.  Given the required simplicity of the model, a simple

model like this is likely acceptable, however, there is no source

described for the alternator efficiencies.  The base electrical loadof the vehicle is mentioned briefly, however, no numerical valuesare given for each vehicle class or any type of model described.

is a legacy of the 2007­2008 value for the baseline and addiscussed with Ricardo and wconfidential discussions with 

Inputs and Accessory load 336 The electrical system also includes an advanced alternator control The low voltage battery was 

Parameters assumptions which allows for increased alternator usage during decelerations

for kinetic energy recovery.  The control description given is valid

but simplistic, but seems to fit the expected level of accuracyrequired for the purpose.  There is an issue regarding with theapproach for modeling the battery during this process.  Whencharging the battery at the stated level of 200 amps, the chargingefficiency of the battery will be relatively poor.  During removal of the energy later, there will once again be an efficiency penalty.  There is no description of a low­voltage battery model in thereport nor any explicit reference to such charge/dischargeefficiencies.  Additionally, an arbitrary limit of a 200 amp alternatoris defined for all vehicle classes – it is unlikely that a future small

car and a future light heavy duty truck will have an alternator withthe same rating. 

conventional 12 volt automotefficiency of the battery itself modeled. Several OE's have alternator energy recovery stalternators already exist todapotential to recover all of the during normal operation, thenalternator would be a small in

Inputs and Accessory load 337 On the mechanical side, it is assumed that “required accessories” The mechanical fan (only use

Parameters assumptions (e.g. engine water pump, engine oil pump) are included in the

engine maps.  The mechanical loading of a mechanical fan ismentioned but no description of the model which, at a minimum, should be adjusted based on engine speed and engine power.

was indeed modeled based oSee accessory power requirereport.  

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations Accessory load

assumptions

338 Cite and/or validate the alternator efficiency values of 55% and

70%.

The 55% to 70% alternator e

is a legacy of the 2007­2008 value for the baseline and addiscussed with Ricardo and wconfidential discussions with 

Recommendations Accessory load

assumptions

339 Account for charge/discharge losses in the advanced alternator

control and/or describe the 12V battery model used for thesimulation.

See excerpt number 336. 

Recommendations Accessory load

assumptions

340 Describe, cite, and validate the accessory fan model used in the

simulation.

The load of the electrical coo

in the base electrical loads.  Mincluded in the engine map.

Recommendations Accessory load

assumptions

341 Justify the use of a 200 Amp advanced alternator across all of the

vehicle platforms.

The use of a 200A alternator 

production trends while streamodeling process.

Inputs and Alternator Regen 385 The alternator regeneration strategy is not well documented.  The See excerpt number 336. In a

Parameters Shift Optimizer key system specifications, such as max alternator output and

efficiency, are listed as assumptions without a data source forvalidation.  The efficiency of the battery is not mentioned in thisnor other presentations that this reviewer has read – batteryefficiency for a lead acid battery at high currents is poor, thiswould have an impact on the recovery of energy.  Strategies likethis are disruptive to drivability and this issue is not discussed inthe presentation. 

impact is minimal, as BMW atechnology on current produc

Inputs and 404 Rgen Alternator: Ricardo states ­ 70% efficient alternator;  As reader notes, 70% is toda

Parameters however, alternator efficiency is a function of temp, speed and

load. 70% is probably the best, but it’s highly unlikely that it willoperate there for the duration of the conditions.

scenario. It is a safe assumpt70% efficient alternators will from alternator manufacturer

Simulation

methodology

413 Regen Alternator: Alternator model is too simplistic. On average

the efficiency is too high as identified and it’s unrealistic toassume that the battery will be able to accept 100% of the charge. 

See response to Comment E

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation Transmission 40 Specific suggestions regarding models that need more detailed The warmup factor accounts 

methodology Models (Section

6.4)

coverage: The transmission efficiencies vary by almost 10­15%based on the transmission oil temperature. How have theseeffects been modeled?

transmission and final drive gduring bag1 and was derivedtest results.

Other Comments Transmission

Models (Section6.4)

62 It is claimed that gear selection will be optimized for fuel economy

for a given driver input and road load. Can this also be adaptive?Engine performance degrades with age. This strategy could alsolead to more gear shifts; the latter would increase hydraulic loadsand frictional power losses in the clutch, thus eroding some of the

possible fuel economy gains.

See revised text in section 6.

comparison of optimized shiftproduction vehicles. Adaptiveto account for engine or powewere not part of the scope of 

Simulation

methodology

Section 6 Vehicle

Models

89 No discussion of how driveline inertia is handled.  This is

important in forward­looking models. 

Addressed in revisions to sec

Results 6.4 Transmission

Models

108 Fig 6.1 appears to be a comparison of desired cvt ratio vs desired

6spd gear ratio. Should be stated as such.  The shift logiccontroller should take into account the time to shift and whether ornot the desired shift is achievable. 

Plots desired CVT ratio vs. de

ratios.  Shift optimizer does ashift and whether or not shift study also included a constrafrequency. See revised sectiocomparison of optimized shiftproduction vehicles.

Results 109 What are the shift optimizer inputs? What are it’s basic decision

criteria?

Shift optimizer inputs are disc

tries to keep engine & trans aSee revised section 6.4.

Results 110 There is no discussion of engine downspeeding. Engine downspeed not a first

some cases it was the result shift strategy.

Results 111 There is no discussion of gear ratio selection. Gear ratios are now includedCompleteness 137 No transmission data was shown. No mass, no inertia to

efficiency maps, no gear ratios.

The transmission models use

comparable to contemporaryrevised Section 6.4.

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Table 1: Response to Individual Peer Review Comments

Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Completeness Section 6 Vehicle

Models

143 No discussion of how driveline inertia is handled.  This is

important in forward­looking models. 

Addressed in revisions to sec

Inputs and Transmission 174 The forecast that current 4­6 speed automatic transmissions will EPA and Ricardo appreciate 

Parameters technology have 7­8 speeds by 2020­2025 is appropriate for all except the further response is required. 

selection smallest and/or low cost vehicles (page 19). 

Inputs and

Parameters

Transmission

technology

176 Recommendation:  The detailed assumptions showing how the

benefits of dry sump, improved component efficiency, improved

See revisions to section 4.4 a

report. 

selection kinematic design, super finish, and advanced driveline lubricants

were added to the transmission maps should be added to thereport before it is released. 

Simulation Transmission 207 A transmission shift optimization strategy is presented in the See revised text in section 6.

methodology optimization report and the results are shown in Figure 6.1 (page 28).  This comparison of optimized shift

figure shows very frequent shifting, especially for 4th, 5th and 6th production vehicles.gears. 

Simulation Transmission 208 Issue:  Optimized shift strategies of the type used by Ricardo See revised text in section 6.

methodology optimization have been previously evaluated and found to provide customer comparison of optimized shift

complaints of “shift busyness”.  Customers are likely to reject such production vehicles.a shift strategy. 

Recommendations 242 Recommendation:  The detailed assumptions showing how the

benefits of dry sump, improved component efficiency, improved

See revisions to section 6.4 i

kinematic design, super finish, and advanced driveline lubricantswere added to the transmission maps should be added to thereport before it is released. 

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

Shift Optimizer 386 Shifting strategy impacts efficiency, performance, and drivability.  

Manufacturers are aware of this and balance all three whencalibrating shift maps.  Changing baseline shift maps to improveefficiency will have an impact on the other metrics which are alsoimportant to the vehicle.  Additionally, it is not clear how theoptimized shift strategy was developed, what the shift strategy is, or how it will be applied to the range of transmissions in the study.  It is stated that is optimizes BSFC, however, there are otherconstraints that must be applied in addition to this.

Your points are valid.  The sh

had many constraints at the eeconomy.  The result was a sshifts over the cycle as compvehicle and improved fuel ecosection 6.4 for more detail. 

Inputs and

Parameters

Input Data

Review

398 The shift strategy is discussed qualitatively; however, it is not 

described in enough detail to understand exactly how it isaccomplished.  Shift schedules are shown, however, no validationis shown that would indicate that these shift schedules are optimalas claimed.

See revised section 6.4. 

Simulation

methodology

410 Transmission Model: Ricardo describes an approach that asserts

that using an average efficiency value vs a 3D efficiency mapyields insignificant differences over the CAFÉ drive cycles, but offers no results to validate the claim. 

See revised section 6.4. 

Simulation

methodology

411 Transmission Model: Ricardo offers no discussion of how inertial

changes are managed during shifts. This may have greatest impact on the shift strategies where the transmission shifts to put the engine at the best bsfc for the given load.

The transmission model only

and torque/speed for each gefixed and is already explainedHow "this may have greatest strategies" needs further expreviewer.  Completely ignorininertias in these light duty vehprobably affect the result by o

Results 6.5 torque

Converter models

112 The lockup strategy seems very conservative.  Large gains are

achievable with more sophisticated control and are in use today.

See revised text in section 6.

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Results 113 What was the basis for the minimum rpm’s for lockup sited?

Should be based on lugging the engine.  The controller shouldrecognize when it needs to unlock the TC based on the enginesability to keep up.

The transmission controller p

from extreme lugging. The tonever locks at operating poincannot keep up or drivability 

Inputs and

Parameters

Input Data

Review

399 The torque converter models are standard models, thus, the

provided documentation is adequate. 

EPA and Ricardo appreciate 

further response is required. 

Results 6.6 Final Drive

Model

114 Only discussed the baseline, what improvements for 2020 and

what final drive selection criteria for the future vehicles was used?

Final drive ratio was one of th

parameters in the Design of EThis allows the user to selectfinal drive ratios.

Results 5 Performance calculations tied to the FTP, HWFET, and US06 test 

cycles do not adequately capture vehicle behavior under realworld operation. Therefore, technologies that address improvingfuel economy under real­world operation are either excluded ortheir contribution not included. The application of a 20% reductionin fuel economy to the FTP75 bag 1 portion of the drive cycle for2010 baseline vehicles and 10% for 2020­2025 is crude, arbitrary, and treats only one of many problems with the driving simulationin the test cycles.  Test cycle difficulties carry over into thesimulation of hybrid control strategies. 

The 20% value was based on

EPA certification testing in thwhen it was applied.  Currentwater pumps exhibit an 11% penalty on bag1 mpg (2011 ECert data) and EPA felt that a1% to 2% further improvemeThe EPA test cycles were noas they are the basis for pastfuel economy standards.  The"real world" fuel economy is wdocumented by EPA but doethe legacy EPA Cert tests tha2020­2025 fuel economy regexcerpt 333. 

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Comment

Excerpt

No.  Comment Respons

Other Comments Warm­Up

Methodology

286 Elsewhere the report states, “A bag 1 correction factor is applied

to the simulated “hot” fuel economy result of the vehicles toapproximate warm­up conditions...”  The correction factorreduces the fuel economy results of the FTP75 bag 1 portion of the drive cycle by 20% on the current baseline vehicles and 10%on 2020­2025 vehicles that take advantage of fast warm­uptechnologies” (page 29).  No references or data are cited tosupport this significant reduction in correction factor. 

See excerpt 333. 

Recommendations Warm­Up

Methodology

333 Cite sources of data for 10% and 20% factors applied to the cold

bag fuel economy data.

The 20% value was based on

a legacy of the 2007­2008 sturetained for current technologelectric water pumps or othertechnologies that improve vewarmup.  Current BMWs withpumps exhibit an 11% to 12% on bag1 mpg (2011 EPA Tesand EPA felt that an assumptfurther improvement was attabased on the Ford Escape wmeasured by Argonne Natl Lbetter than Ricardo's model dtoday's Ford and BMW engin0.88 factor on bag 1, it seemsexpect future engines to achi

Inputs and

Parameters

75 Is there any accounting for the energy conversion on hybrids from

the high voltage bus to the low voltage?

The DC­DC converter has sp

characteristics.  See section 

Simulation

methodology

6.8 Hybrids 97 Were separate optimization runs to determine the best control

strategy done?  How are we assured the best control strategy isimplemented?

See revised Section 6.8, whic

significant additional text andthese concerns.

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Completeness Section 4.3 133 Don’t see any data on the battery technology, battery Ricardo and EPA decided on

Hybrids management, SOC control strategies. No discussion of regen battery technology that was e

braking strategies.  today.  See further discussion 

Completeness 6.8 Hybrid 145 Too much data is missing. What were the pack voltages? What  See revised Section 6.8, whic

Models were the battery technologies? Was there only one or more?

Other than improved resistance, what other future improvements

significant additional text andthese concerns.

were included, like improved power density, improved usableSOC range?  What was the control strategy for each type?

Completeness 146 Load leveling the engine by charging the batteries has been Load averaging was the appr

shown to not be a very good idea because the round tripefficiency hit is a killer. Should only be used when SOC falls

full study team. If the engine assumes that operate at mos

below a certain level. Ricardo made a side compar

issue; definitely better W/P2. section 6.8. 

Completeness 147 We’re left to assume that SOC leveling is accomplished, but there

is no description of how? Was an EPA/SAE method used.

See revised section 6.8 

Inputs and Hybrid 178 Issue:  The adequacy of the P2 Parallel and PS Power Split  See revisions to Section 6.8. 

Parameters technology Hybrid systems cannot be determined without having, at a

selection minimum, schematics and operational characteristics of the each

system together with comparisons with today’s hybrid systems.

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Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Inputs and Hybrid 179 Although not contained in the report, the teleconference call EPA Your points are valid.  To cla

Parameters technology

selection

on May 5, 2011 revealed that 90% of the deceleration kineticenergy would be recovered. 

assumes that 90% of the mecenergy will be performed by t

Kinetic energy recovery is limited by the following: system (not recovered) and l­ Maintaining high generator efficiency over the range of speeds

and resistive torques encountered during deceleration

be stored and even less avaireuse due to system efficienc

­ Limitations on the rate at which energy can be stored in the

battery

been accounted for in the hybrevisions to Section 6.8 to cla

­ Losses in the power electronics­ Some energy is lost when energy is withdrawn from the

battery for delivery to the motor. 

­ Inefficiencies in the motor at the speeds and torques required. The inefficiencies of each of these four subsystems are in seriesand are compounded.  If each subsystem had 90% efficiency, thekinetic energy recovery efficiency would be only 66%.

Inputs and Hybrid 180 Issue: Capturing 90% of the deceleration kinetic energy is a Your point is valid.  To clarify

Parameters technology significantly goal.  The technology to be used to achieve this goal assumes that 90% of the mec

selection needs to be explained and appropriate references added to the

report.  

energy will be performed by tsystem (not recovered) and lbe stored and even less avaireuse due to system efficiencbeen accounted for in the hybrevisions to Section 6.8 to cla

Inputs and Battery SOC 190 Although not contained in the report, an email from Jeff Cherry Section 6.8 of the report has 

Parameters swing and SOC (EPA) on May 5, 2011 revealed that the SOC swing was 30% include the 40% SOC value.

SOC to 70% SOC or 40% total, which appears to be appropriate. 

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Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Results Sample runs of 

CSM

215 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: Stoichiometric DI Turbo with Stop­Start to PS Hybrid­ 11.1% improvement in M­H mpg­ A detailed explanation of the differences in the improvements

between the P2 and PS hybrids should be provided in thereport, especially considering that the P2 hybrid has better fueleconomy and uses a 70% smaller electric motor (24 vs. 80kW). 

The P2 hybrid architecture ha

efficiency than the Powersplithaving a smaller electric macPowersplit traction motor, botregenerate at least 90% of ththe drive cycles.

Results Sample runs of 

CSM

216 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: Stoichiometric DI Turbo PS Hybrid to Naturally Aspirated Atkinson CPS Hybrid

­ Loss of 2.3% M­H mpg (From Stoichiometric DI Turbo PS

Hybrid)

­ The details of the Naturally Aspirated Atkinson CPS Hybrid

should be provided to explain the nearly equal fuel economyto the Stoichiometric DI Turbo PS Hybrid. 

One of the advantages of hyb

ability to operate the engine nefficient point. In this case, thhad a better best BSFC regioStoichiometric DI Turbo engin

Results Sample runs of 

CSM

217 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: Stoichiometric DI Turbo PS Hybrid to Naturally Aspirated Atkinson DVA Hybrid­ 2.1% M­H mpg improvement in M­H mpg (From Stoichiometric

DI Turbo PS Hybrid)

­ The details of the Naturally Aspirated Atkinson DVA Hybrid

should be provided to explain the nearly equal fuel economyto the Stoichiometric DI Turbo PS Hybrid

See response to Comment E

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments Hybrid

TechnologiesModels

265 Key elements of a hybrid system include:  electric machines

(motor­generator), power electronics, and a high­voltage battery.  Only the following vague description of the models for these

subsystems was provided:  “For each of these systems, current state of the art technologies were adapted to an advanced 2020­2025 version of the systems, such as by lowering internalresistance in the battery pack to represent 2010 chemistries underdevelopment and decreasing losses in the electric machine andpower electronics to represent continued improvements intechnology and implementation” (page 29).  This vaguedescription did not provide adequate details to assess theadequacy of these models.  For example, specific values forinternal resistance with references should be provided togetherwith an illustration of how this was incorporated in the model of the battery. 

See revisions to section 6.8. 

Other Comments Hybrid

TechnologiesModels

268 No mention was provided of how the cooling system for the hybrid

system was modeled.

The hybrid power electronics

assumed to be water cooled added to the cooling load of tthe efficiencies described in t

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Hybrid 345 Hybrid vehicles are particularly challenging to model because of  The powertrain operates at n

Parameters technology

selection

the extra components which allow multiple torque sources, andthus, require some form of torque management strategy (i.e. asupervisory control.)  The report briefly describes a proprietarysupervisory control strategy that is used to optimize the controlstrategy for the FTP, HWFET, and US06 drive cycle.  Thestrategy claims to provide the “lowest possible fuel consumption”which seems to be somewhat of an exaggeration – this impliesoptimality which is quite a burden to achieve and verify for such acomplicated problem.  The strategy also is reported to be “SOCneutral over a drive cycle” which is also difficult to achieve inpractice in a forward looking model.  Once can get SOC with acertain window, however, short of knowing the future or simply not using the battery ­ it is impossible to develop a totally SOC neutralcontrol strategy. 

thus is expected to provide veconsumption.  But, it is not opwhole design space.  Ricardo"lowest possible..." language flow diagram. See revised se

Inputs and Hybrid 347 Without even basic details on the hybrid control strategy, it is See revised section 6.8. 

Parameters technology

selection

simply not possible to evaluate this aspect of the work.  Becauseof the batch simulations with varying component sizes andcharacteristics, this problem is not trivial.  Supervisory controlstrategies used in practice and in the literature require intimateknowledge of the efficiency characteristics and performancecharacteristics of all of the components (engine, electricmotors/inverters, hydraulic braking system, and energy storagesystem) to develop control algorithms.  This concern is amplifiedby the lack of validation of the hybrid vehicle model against aknown production vehicle.  It is unclear how a “one­size fits all”control strategy can be truly be perform near optimal over suchwidely varying vehicle platforms.

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations Hybrid

technologyselection

350 Validate that the HEV control algorithm performs equally well on

all vehicle classes.

The class of vehicles does no

control strategy. The differenthe various classes change thfrom hybridization: however, maximizing efficiency throughenergy and operating the engpoints remain the same. 

Inputs and Electric Traction 352 The model of electric traction components is not discussed in any See significant revisions to se

Parameters Components detail, as the only mention in the report is that current technology

systems were altered by “decreasing losses in the electricmachine and power electronics.” Given the importance of theelectric motor and inverter system in hybrids this is not acceptable.

Recommendations Electric Traction

Components

353 Describe the method used to model electric traction components. See expanded discussion of 

section 6.8.

Recommendations Electric Traction

Components

354 Provide validation/basis for the process used to generate future

technology versions of these components. 

Part of Row 329; see expand

hybrid models in section 6.8.

Recommendations Electric Traction

Components

355 Describe the technique used to scale these components. Part of Row 329; see expand

hybrid models in section 6.8.

Inputs and HEV Battery 356 Battery models for HEVs are necessary to adequately model the See significant revisions to se

Parameters Model performance of an HEV.  The report provides no substantive

description of the battery pack model, other than that the modelwas developed by “lowering internal resistance in the battery packto represent 2010 chemistries under development.” Battery packsize is also not a currently a factor in the model – this has a

impact of charge and discharge efficiency of the battery pack.

Recommendations HEV Battery

Model

357 Describe the method used to model the HEV battery. See revisions to section 6.8. 

Recommendations HEV Battery

Model

358 Provide validation/basis for the process used to generate future

technology versions of the battery. 

See revisions to section 6.8. 

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Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Recommendations HEV Battery

Model

359 Describe the technique used to scale the HEV battery . See revisions to section 6.8. 

Inputs and Battery Warm up 387 The battery model described has the following possible problems:   EPA and Ricardo appreciate 

Parameters 1, Battery Warm The model is relatively simple – but could potentially work for the further response is required. 

up 2 application and generally is consistent with the fidelity of the rest 

of the model. 

Inputs and Battery Warm up 388 The battery model described has the following possible problems:  The drive cycles covered in t

Parameters 1, Battery Warm The model references ambient temperature for heat rejection.   cabin temperatures similar to

up 3 Most HEVs pull in cabin air rather than outside air for cooling,  temperatures. 

thus, this will cause modeling error. 

Inputs and

Parameters

Battery Warm up

1, Battery Warmup 4

389 The battery model described has the following possible problems: 

Adjusting the Mbat x Cpbat term by 200% is a red flag that something might be fundamentally wrong with either the modelformulation or the data used in the model.  There should be

These parameters were not p

study.  

minimal errors in the mass estimation of the pack and the specificheats of battery modules can be found in the literature or throughtesting. 

Inputs and Battery Warm up 390 The battery model described has the following possible problems:  See revised section 6.8 for d

Parameters 1, Battery Warm The method of handling battery packs of different classes of  battery packs for the study.

up 5 vehicles is not described, nor are the actual parameters for these

different models disclosed.

Simulation 412 Hybrid: I don’t see any effort to model motor/inverter temperature Motor/Inverter efficiencies we

methodology effects. One would expect significant degradation of motor outlined in section 6.8 of the 

capability as things heat up during normal operation.  operating temperatures. 

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Results 416 Motor Efficiency Maps: I am having trouble believing that motor

efficiency will stay above 90% once temperature effects areaccounted for. It also seems to me that these numbers don’t include the inverter even though the authors say that it does. TheUQM maps seem more reasonable.  As stated in a previouscomment, I believe that the cost reductions needed for motors willdrop their efficiencies in the future. 

Motor/Inverter efficiencies we

outlined in section 6.8 of the operating temperatures.  Theshown includes the inverter e

Inputs and 421 Carlson, R., et al., Argonne National Laboratory, On­Road EPA and Ricardo appreciate 

Parameters Evaluation of Advanced Hybrid Electric Vehicles over a Wide

Range of Ambient Temperatures EVS23 – Paper #275, 15 p. Paper reports on­road and dynamometer testing of two hybridvehicles at cold (­14 degC) and hot (33 decC) conditions. Fueleconomy increases with temperature (except for highest temperatures with the system which does not limit batterytemperature).Comment: Paper provides data showing importance of temperature on hybrid vehicle fuel economy. These data are usedby Ricardo to validate their battery warm up model, see next document. 

response needed.  Backgrouincluded both highly relevant as well as some general inforused during the course of thesources reviewed were of critthe study. 

Simulation 422 Ricardo, Hybrid Battery Warm Up Model Validation – Update,  EPA and Ricardo appreciate 

methodology Light Duty Vehicle Complex Systems Simulation ,EPA Contract 

No. EP­W­07­064, work assignment 2­2, 15 Mar 10, 5 p.  proprietary) This report presents a simple battery heat transfer

model for battery warm up and compares with Argonne National

Laboratory of the previous document. Comment: Model produces adequate prediction of batterytemperature.

response needed.   

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

425 Ricardo, Engine and Battery Warm­Up Methodology, Light Duty

Vehicle Complex Systems Simularion, 17 Feb 10, 16 p. (proprietary) Document reviews engine and battery warm­upstrategies and provides a simple model. Comment: The approach to battery warm­up is uncertain. Pointsto importance of test cycle (FTP for fuel economy complianceversus test for EPA label versus real­world). 

Cold FTP was not included in

Other Comments 429 Ricardo, Hybrid Controls Follow­up, 10 Sep 11, 3 p. (proprietary)

Report discussed motor/general efficiency map used for 2020

technology. Projected efficiencies peak at 95% but most P2 hybridapplication if below 90% efficiency. Comment: I am not qualified to assess if the projectedmotor/generator efficiencies are appropriate for 2020­2025 asreported, but they seem low for 15 years in the future. 

The motor maps used in the 

efficiency of the motor contro

Other Comments Hybrid

TechnologiesModels

266 In contrast, as an example, Staunton et al. (2006) provided a

detailed motor efficiency map, shown below, as well as efficiencymaps of other key components of the Prius hybrid vehicle.  Similarmaps for all hybrid subsystems would be expected in this report. (See Exhibit 5)

See revisions to section 6.8. 

Other Comments Hybrid

TechnologiesModels

267 In addition, “a Ricardo proprietary methodology was used to

identify the best possible fuel consumption for a given hybridpowertrain configuration over the drive cycles of interest.” (page29), which precluded an assessment of its suitability.

See revisions to section 6.8. 

Recommendations Hybrid

technologyselection

349 Better describe the hybrid control strategy and validate against a

current production baseline vehicle. 

See revisions to section 6.8. 

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations Hybrid Controls

Presentations

400 Several hybrid controls presentations were provided, however, it 

was difficult to piece together what information superseded theother since they were provided out of context.  There were severalgood slides showing dynamic programming results of different control scenarios, however, it is assumed that this was not usedfor the mass simulation since it would be computationallyimpractical.  Thus, I expected to see some results comparing theoffline control results to the actual control used in the vehiclesimulation, however, this was not found.  The major concern inthis area is developing a control strategy that is near optimal for awide variety of hybrid architectures as well as architectures withvarying component types and sizes.  Without further validation inthis area it is not clear that the hybrid results are valid since thecontrol has such an important role in this.

See revisions to section 6.8. 

Recommendations Warm­Up

Methodology

334 Cite and/or validate the modeling approach used. Please refer to the revised re

technology/model validation.

Results 42 For the vehicle performance simulation results shown in Table

7.1, were there any significant adjustable parameters used to fit these vehicles?

All vehicle parameters (road 

were the same for both casesthe models. 

Results 43 Even though it appears that the validation results from the

simulation have “acceptably” close agreement with the test data, there are up to 15% off. Even for the small car where all data isavailable, the error is on the order of 5%. These discrepancies areusually not negligible and should be taken into account whenconclusions are drawn from the results, especially if regulation isto be proposed based on these. 

EPA will take this into accoun

final results to support rulema

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Topic Topic No.  Comment Respons

Other Comments 55 It would be desirable to show the analysis used to convert fuel Appendix 3 to the final report

consumption savings to vehicle greenhouse gas (GHG) emissionsequivalent output. Ultimately, what matters is the GHG savings

baseline fuel economy and C

equivalents for all classes of 

resulting from the combined production and use cycle of  in this study. Note that the CO

alternative fuel options for combustion engines. in these tables were provided

9,087 g/gal of fuel for gasolinfor diesel. 

Results 7.1 Baseline

ConventionalVehicle Models

116 Better definition of what “acceptably close” means.  This doesn’t 

meet the criteria for objectivity. Something like, “the advisorycommittee determined that the baseline models had to predict within x% to be usable for this study.”

The final report retains this te

the text represents the approthe study, during which EPA results to be acceptable for mrevisions to section 7.1 to furprocess used to develop bas

Inputs and Baseline vehicle 160 The development of baseline vehicle models with comparison of  EPA and Ricardo appreciate 

Parameters subsystem the model results to available 2010 EPA fuel economy test data further response is required. 

models/maps was appropriate. 

Simulation Baseline vehicle 204 Ricardo developed baseline vehicle simulations for 2010 vehicles It is important to note that, fo

methodology model validation

results

for which EPA fuel economy data were available (page 30).  “Forthe 2010 baseline vehicles, the engine fueling maps and related

validation phase, baseline veestablished just using the giv

parameters were developed for each specific baseline exemplar data or the raw validated veh

vehicle.” (page 25).  Even though these are production vehicles, the models and maps used were not described (including whether

results. Rather than using thevehicles and corresponding f

they were derived from actual measurements or models) and they results, a new set of baseline 

were not provided in the report so that their appropriateness couldnot be assessed.

determined to facilitate a unifbetween the advanced (futurtoday’s current technologies.

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Simulation

methodology

Baseline vehicle

model validationresults

205 Table 7.1 shows the calculated vs. EPA test data for the baseline

vehicle fuel economy performance.  This table should includepercentage variation of the model calculations vs. the test data.  The agreement of the model with the test data is within 11%, but this is a larger error than some of the incremental changes shownin Appendix 3.  A closer agreement would have been expected.

Table 7.1 now compares vali

with EPA Test List data for FTof the validation results are wexception of the Large MPV Hwhich is within 9.5% of the pupurpose of the validation modprovide a benchmarked startof the analysis. 

Simulation

methodology

Baseline vehicle

model validationresults

206 Recommendation: A closer examination of the reasons for the up

to 11% discrepancies between the models and baseline vehicles’EPA fuel economy test data should be undertaken so that the

models could be refined to provide better agreement. 

EPA and Ricardo, together w

committee, determined that tagreement on fuel economy wthis study.  It is important to nthe model validation phase, bwere not established just usinTest List data or the raw valideconomy results. Rather thanvalidation vehicles and correseconomy results, a new set owere determined to facilitate comparison between the advconcepts and today’s current

Recommendations 241 Recommendation:  A baseline model of a hybrid vehicle should be

developed and compared to 2010 EPA fuel economy test data forproduction hybrid vehicles.

During development of the Po

modified small car with PS wvalidate the model but was noreport. 

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Recommendations 247 Recommendation: A closer examination of the reasons for the up Table 7.1 now compares vali

to 11% discrepancies between the models and baseline vehicles’fuel economy test data should be undertaken so that the models

with EPA Test List data for FTof the validation results are w

could be refined to provide better agreement.  exception of the Large MPV H

which is within 9.5% of the pupurpose of the validation modprovide a benchmarked startof the analysis. 

Inputs and Hybrid 346 Another factor that must be considered is that a hybrid strategy The study approach used 0­6

Parameters technology

selection

that achieves maximum fuel efficiency on FTP, HWFET, andUS06 does not consider many other relevant factors.

at different speeds, and otheto make sure that the modele

Performance metrics like 0­60 time and drivability metrics often acceptable performance on csuffer in practice.  In today’s hybrids, the number of stop­start events is sometimes limited from the optimum number for

issues.  See the nominal test7.1 and Appendix 5.

efficiency because of the emissions concerns.  Because of thesefactors and others, a strategy achieving optimal efficiency may behigher than what can be achieved in practice.

Inputs and

Parameters

Hybrid

technology

348 A last comment is that there is no validation of the HEV model

against current production vehicles.  At a minimum, the Toyota

No validation was performed 

architectures as no P2 hybrid

selection Prius has been dissected sufficiently in the public domain to production during the study. T

conduct a validation of this class of hybrid electric vehicle. P2 architecture was simulate

road loads to the Toyota Priueconomy figures were higherPrius. 

Simulation 7.2 Nominal Runs 98 Was a separate matrix of simulations run to obtain the nominal See revised section 7.2 for d

methodology sizes for the advanced engine or was it merely a matter of 

matching the peak torque. 

Simulation 99 How was a 20% reduction in engine size for the nominal hybrid The final report clarifies why 

methodology engine arrived at? Even for the micro­hybrid (engine start/stop)? & PS hybrids and all engines

directly for hybrids. See Sectdescription of hybrid engine s

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Topic Topic No.  Comment Respons

Simulation 100 “These summary results....used to assess the quality of the Appendix 5 presents the nom

methodology simulation....” Where is the data for this assessment published? data. 

What were the criteria that said pass or fail?

Inputs and Battery SOC 191 Achieving neutral SOC (neither net accumulation or depletion) for EPA and Ricardo appreciate 

Parameters swing and SOC hybrid vehicle simulations is appropriate (page 30).  further response is required. 

Results 8.2 RSM 119 A description of how the neural network is deployed is needed,  The fit criteria were based on

only the why it was used is discussed in this section.  What werethe best fit criteria?  What types of equations did the neural net 

regression line approximatedfrom the DoE, using both the

have to play with? Where are the fit’s published? How was it  well as the validation data.

determined that the “one fit per transmission” was the best way togo?

Simulation

methodology

369 The vehicle simulator is used to generate several thousand

simulations using a DOE technique.  This data is then fit with a

See revised section 8. 

neural­network­based response surface model in which the “goalwas to achieve low residuals while not over­fitting the data.”  Thisresponse surface model then becomes the method from whichvehicle design performance is estimated in the data analysis tool.  In this case, the response surface model is nothing more than amulti­dimensional black­box curve fit.  There was no error analysisgiven in the report regarding this crucial step.  By way of example, the vehicle simulator could provide near perfect predictions of future vehicle performance; however, a bad response surface fit could corrupt all of the results.

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Results 8.1 Evaluation of

Design Space

118 Why was Latin hypercube sampling methodology picked over

other sampling methods? While it’s attributes are mentioned, what other methods were considered?

As Section 8.1 states: "The m

samples the multidimensionain a way that provides comprrelatively sparse coverage foalso allows one to efficiently cmultidimensional parameter srandom sampling. It providestraditional multi­level factoriaassessing a large parametricefficient number of experimentraditional, multi­level factoriafeasible within the number ofperformed within the scope o

Recommendations 12 The design space should be expanded to include performance

parameters, such as power/weight or 0­60 times. 

Performance parameters are

RSM tool.

Results 46 The plots showing simulation results in blue, red, etc. could be

better labeled (i.e. legends could be inserted in the plots) andpossibly presented in a relative format indicating percent improvements over the baseline engine rather than absolutenumbers. This is more of a personal choice for a more clearrepresentation of the predicted improvement, rather than statingthat there is anything wrong with the current representation. 

EPA and Ricardo appreciate 

further response is required. 

Inputs and

Parameters

Other inputs 194 The Design Space Query within the Data Visualization Tool allows

the user to set a continuous range of variables within the designspace range.  Although this capability is useful for parametricstudies, the following risks are incurred with some of thevariables.

Ricardo is preparing a user g

help address these types of c

Inputs and

Parameters

Other inputs 195 The sliders for “Eng. Eff” and “Driveline Eff.” would allow the user

to arbitrarily change engine efficiency or driveline efficiencyuniformly over the map without having a technical basis for suchchanges. 

The justification for the range

variables in a given situation study

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Inputs and

Parameters

Other inputs 196 The slider for weight would allow the user to add hybrid or diesel

engines with signficant weight increases without incurring anyvehicle weight increase. 

The weight of technologies is

study due to the complex natopinions regarding this topic.

Inputs and Other inputs 197 Recommendation:  A default weight increase/decrease should be The weight of technologies is

Parameters added for each technology.  If weight reductions are to be studied, 

then the user should have to input a specific design change, with

the appropriate weight reduction built into the model, rather that having an arbitrary slider for weight. 

study due to the complex natopinions regarding this topic.

Results 9.1 Basic Results 120 Why 10Hz sampling rate?  By what criteria was a run considered

good vs bad?

See footnote added to Sectio

those that failed to follow the described in EPA test proced

Results 9.3 Exploration of 

the Design Space

121 If boundaries of acceptable performance were applied, a

considerable number of simulation runs could be eliminated.

The additional runs were nee

fill the design space to allow to obtain accurate results whvariables.

Other Comments 13 The conclusions, Section 11, are a reasonable summary of the

work conducted.

EPA and Ricardo acknowledg

the reviewer's comments.

Completeness 50 The “Conclusions” section of the report should be renamed

“Summary” since it does not present any actual conclusionsbased on the results, but it does provide a summary of the project. 

EPA and Ricardo appreciate 

section name has been chan

Recommendations 10 There should be a table describing the baseline vehicles. See Appendix 3 in final repor

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Topic Topic No.  Comment Respons

Completeness 299 Based on the above, it is clear that this reviewer feels the report is Baseline vehicle parameters 

inadequate at describing the entire process of modeling work frominput selection to results.  There was not a single subsystem that 

Appendix 3. 

was documented at the level desired.  It is understood that, insome cases, there are things of a proprietary nature that must beconcealed.  As a trivial example, the frontal area of the vehicleclasses does not seem to be anywhere in the report or dataanalysis tool.  This is one parameter amongst hundreds excludingthe real details of the models (i.e. equations or block diagrams), methods used to generate engine maps, details on control laws, etc.  On the topic of proprietary data, there are many ways of obscuring data sufficiently that can demonstrate a key point (i.e. simulation accuracy) without compromising confidentiality of data– this should not be a major barrier to providing some insight intothe inner working of the simulator. 

Recommendations Vehicle model

issues

305 List all parameters used for each vehicle class for simulation. Baseline vehicle parameters 

Appendix 3. 

Completeness 125 It said there was a comprehensive list of technologies that the Complete technology selectio

group started with, that list should be shown and a comment on appendix to the report.  why it wasn’t included.

Recommendations 151 Considerably more time in this effort is required up front in the Please refer to the technolog

report, to discuss the process of building consensus on data andmodels.  Because this is not really discussed, it gives the

provided in the appendices tocommenter a sense of the rig

impression that not much was done.  selection process.

Recommendations 153 An uncertainty rating for each model/data set should be published Some level of uncertainty is p

to highlight the relative differences in the technology selection slides passumptions/extrapolation of future technologies. attachment to the final report

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Inputs and Engine 166 The engine technologies selected for this study, listed in Table 5.1 EPA and Ricardo appreciate 

Parameters technology

selection

(page 22), are appropriate, but are not all­inclusive of possiblefuture engine technologies.

further response is required. selected the set of possible teappeared to provide the bestimprovements and viability inframe.  See Attachment A to the full range of technologies

Inputs and Engine 170 Issue: There are many engine technologies that have potential for EPA and Ricardo appreciate 

Parameters technology

selection

reduced GHG emissions that were not included in this study, suchas: ­ Single stage turbocharged engines­ Diesel hybrids­ Biofueled spark ignition and diesel engines­ Natural gas fueled engines­ Other alternative fuel engines­ Charge depleting PHEV and EV

program team selected the stechnologies that appeared tosuite of improvements and vitime frame.  See Attachment for the full range of technologevaluated. 

Completeness 230 There are many engine technologies that have potential for

reduced GHG emissions that were not included in this study, suchas: ­ Single stage turbocharged engines­ Diesel hybrids­ Biofueled spark ignition and diesel engines­ Natural gas fueled engines­ Other alternative fuel engines­ Charge depleting PHEV and EV

EPA and Ricardo appreciate 

program team selected the stechnologies that appeared tosuite of improvements and vitime frame.  See Attachment for the full range of technologevaluated. Part of the evaluaincluded expectation of markcost, performance, and readisources.  

Inputs and Future Friction 392 The provided presentation does not describe how engine friction Friction reduction improveme

Parameters Assessment projections to 2020 are made or how they are modeled.  It 

provides some data from 1995 to 2005, however, it does not provide any useful insight into how this information is used.

from those used in the 2008 described in Attachment A. 

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Completeness 450 Ricardo, Report on light­duty vehicle technology package

optimization, 4 Dec 09, 32 p. This is a progress report onRicardo’s modeling work for the EPA. A range of enginetechnologies, hybrid technologies, transmission, and vehicletechnologies are described.Comment: A comprehensive list of near term technologies areincluded. The report is incomplete and optimization apparent isnot included here. 

See Attachment A to final rep

Recommendations Additional

recommendationsshown in boldprint throughout other sections of this report arerepeated belowfor completeness

244 Recommendation:  To establish the adequacy of the subsystem

models/maps, derivation details should be provided.

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. 

Simulation

methodology

3 Ricardo simulated dynamic vehicle physical behavior using MSC

Easy5TM software with 10 Hz time resolution. This software andthe time resolution are appropriate for the computations to showthe effect of component interactions on vehicle performance. 10Hz time resolution is sufficient to capture both driver behavior andvehicle response. Should the application of informationtechnology, as is being implemented, as a means of vehiclecontrol for reducing fuel consumption become a future strategy, the model should be able to provide a suitable simulation.

EPA and Ricardo acknowledg

the reviewer's comments.

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Simulation

methodology

4 Drivetrain synergistic effects seem to be predicted reasonably. 

This was demonstrated by calculation of fuel economy of thebaseline vehicles and comparison with EPA certification test data. The model does not seem to have the capability to capturevehicle weight­drivetrain synergistic effects. Vehicle weight reductions associated with drivetrain efficiency improvements areinput rather than modeled internally. This is an important deficiency. Similarly, from the Complex System Tool, weight reductions do not seem to result in reduction in enginedisplacement.

The mass of technologies wa

this study due to the evolvingcomplex opinions regarding tof the RSM tool is responsiblmass from the baseline vehicdesired results. 

Results 6 It is conceivable that BEVs and PHEVs (and less likely FCEVS)

will be a significant part of the 2020­2025 vehicle fleet. That theyare excluded from the model is a deficiency.

GHG reductions for PHEVs a

applying a utility factor (percethe results of this study for thvehicle.

Completeness 7 The selection of drivetrain technologies (other than the electric

storage technologies) is comprehensive. The qualitativedescription of the drivetrain technologies is complete and clear, but quantitative performance data are missing. Transparency inthe actual performance data is entirely lacking. This includesengine performance maps, shift strategies, battery management in hybrids, and more. That much of that data is proprietary to thecompanies that generated it and/or to Ricardo is a problem forwhat is proposed as a regulatory tool. 

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. 

Completeness 8 The assumptions are difficult to extract from the text. Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. 

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Recommendations 9 The failure to model the drivetrain­weight interactions is a major

shortcoming. Appendix 2 should clearly state that vehicle weightsare held constant (assuming that I am correct in that assumption). 

The mass of technologies wa

this study due to the evolvingcomplex opinions regarding tof the RSM tool is responsiblmass from the baseline vehicdesired results. 

Recommendations 11 Summarizing assumptions in tabular form would be a great 

assistance to the reader.

The final report includes a nu

tables and graphics to addres

Other Comments 15 The report is intended to provide administrators, product planners

and legislators a practical tool for assessing what is achievable, as well as insight into the complexity of the path forward to reachthose advances that will be useful for productive discussionsbetween EPA and the manufacturers. This path forward involvestrade­offs among many design choices involving available, andsoon­to­be­available advances in engine technologies, hybridization, transmissions and accessories. The current versionof the simulation effort seems reasonably balanced in theattention paid to each of these areas. The range of improvementsshown in the technologies considered and examples isencouraging. 

EPA and Ricardo acknowledg

the reviewer's comments.

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Other Comments 16 Overall, the project attempts to undertake an analytical technology

assessment study of significant scope. It does a fairly competent 

job at analyzing a select number of technologies and packages, mostly aimed at improving the gasoline IC engine, and to a lessextent the diesel engine. It complements improvements on theengine side with synergistic developments on the transmissions, hybrids and accessories. The main shortcoming of the study isthat the methodology relies extensively on proprietary andundisclosed data, as well as empirical rules, correlations andmodifiers without citing published reference sources.  Beyond theperceived lack of transparency, keeping up with new technologiesor approaches will necessarily involve new versions of theprogram since the actual models of the technologies used areproprietary and the choice and range of parameters available tousers is fixed and to some extent hidden. Due to theseconstraints, the simulation tool is limited in its ability to providefundamental insight; this will require a more basic thermodynamicapproach, perhaps best carried out by universities.

The technology selections an

were selected to provide a reof combinations that reflect thprogram team of some of theexpected combinations acrosduty classifications.  The full considered is set forth in Attafinal report. In addition, whileproprietary data was a fundathe study design, Ricardo hasdetails and graphics, includinpublically available referenceincrease the transparency anthe final report. While EPA agcombinations are of interest, was a significant undertakingbudget and time, with the optreport is one of the technical EPA's ongoing rulemaking efwas designed to support thatanticipates that others and pecontinue to explore these issstudies that add scope. 

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Other Comments 18 The report is lengthy at places, for instance in the description of 

technologies which users of the simulation software are likely tobe already familiar with, while too laconic at other places, e.g. howthe selected technologies were modeled in some detail. The draft can benefit from better balancing of its sections. There shouldalso be more words summarizing the illustrative results (e.g., provide ranges of benefits), and assessing them critically (e.g., which technologies seem to incrementally or additively contributethe most), rather than just stating that the results are in Table 7.1or in Appendix 3. A discussion of uncertainties present in theanalysis should be presented so as to enable the reader to placethe findings into proper perspective.   

The final report addresses so

comments by adding discusssome of the modeling­focuseHowever, the results are presfound, without significant discuncertainty or critical assessmstudy objective for EPA and tthat the final report satisfies t

Inputs and 20 The report describes a comprehensive set of engine and vehicle To address this concern, the 

Parameters technologies for the prediction of GHG emissions and

performance. However, the full range of inputs and parameters isnot explicitly presented. It requires the reader to refer to the DataVisualization Tool figures to simulation environment, it isimpossible to extract details on, or judge the basis for a number of 

critical inputs. In some occasions, the report mentions that published data have been used, but there are no references to thesource. Baseline engine maps, torque converter maps andshifting maps, electric machine efficiency maps, and controlstrategies for hybrids, which have very direct effects on vehicleperformance and emissions, should be presented in the report, at least in a limited format.

public fueling maps conceptsthe technical transformation otechnologies to the future. SeSections 4.1 and revised SecSection 4.2.6 provides case sTurbo and Atkinson engines.sections (especially section 6expanded as well. 

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Inputs and 27 Alternative fuels are currently a key research topic and very The technology selections an

Parameters important for future energy independence. Because usage of 

these fuels can have an impact on efficiency and emissions, thestudy would be enhanced if engine performance maps withvarious fuels were included.

were selected to provide a reof combinations that reflect thprogram team of some of theexpected combinations acrosduty classifications. This incluThe full slate of options consAttachment A to the final repo

agrees that additional combininterest, the project scope waundertaking, both in terms of with the options selected.  Ththe technical studies relevantrulemaking efforts, and the scto support that effort.  EPA anothers and perhaps EPA will these issues with further stud

Simulation 28 The RSM approach is certainly a good way to provide quick The RSM approach was a fo

methodology access to wide range of results, but it has the limitation that a

large number of assumptions have to be made ahead of time inorder to determine the design space. Also, creating theseencompassing RSM’s requires a significant amount ofsimulations, and all the results will not necessarily be of interest. If a more flexible model/simulation was created and coupled to auser­friendly interface, users might be able to obtain and analyzethe desired results instead of being constrained by the designspace previously determined.

this study. While the revieweprovide another valuable appreport or study change is neethis comment. 

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Simulation

methodology

29 Even though the authors attempt to describe the simulation

methodology and assumptions in the report, it lacks details of themodels employed, which makes it hard to determine if refinements need to be made, or even if more appropriatemodels/methods should be used. It is understandable that, due tothe proprietary data, it is not possible to present everything.However, without any of this information, the RSM results aremore difficult to interpret. 

To address this concern, the 

public fueling maps conceptsthe technical transformation otechnologies to the future. SeSections 4.1 and revised SecSection 4.2.6 provides case sTurbo and Atkinson engines.sections (especially section 6expanded as well. 

Results 45 It would be desirable to include a complete test case with the

appropriate inputs, analysis and outputs as part of the report. Thesample results presented in figures seem to have been includedto indicate the RSM and Data Visualization Tool’s capabilities, but they do not provide a complete picture from which to draw solidconclusions.

The new user manual for the

present a complete test case

Completeness 47 Some of the aspects lacking form the report have already been

mentioned and discussed in the relevant sections.

EPA and Ricardo appreciate 

further response is required. 

Completeness 48 In general, the report provides a fair description of the modeling

process. Unfortunately, there are no equations, plots or mapsshowing any specific modeling item, thus making this part of thereport vague. 

The final report adds detail to

technology discussions and tdiscussions to better articulatapproach of the study. 

Completeness 49 It might be possible to shorten the descriptions related to the

individual technologies implemented and their improvements andadd more details on how they have been modeled. People usingthis tool will most likely not use the brief descriptions of thevarious technologies to draw conclusions and make decisions.

The final report adds detail to

technology discussions and tdiscussions to better articulatapproach of the study. 

Recommendations 51 Various suggestions have already been included in the relevant 

sections.

EPA and Ricardo appreciate 

further response is required. 

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Recommendations 52 The authors should expand the modeling sections. In particular, 

they should cite literature references (where possible) and providemore detail when empirical data, modifiers, or scaling laws areused. 

The final report adopts many

suggestions.

Recommendations 53 Flexibility should be added to the models. Some engine

technologies, such as variable cam phasing, HCCI and alternativefuels should be considered.

EPA and Ricardo appreciate 

Future analyses could expaninclude these technologies.  Vincorporated in the previous s

Recommendations 54 A self­contained study should be presented as a test case for the

results so that specific conclusions can be drawn and the utility of the approach more easily understood. 

The new user manual for the

present a complete test case

Inputs and

Parameters

72 How were baseline BFSC maps modified?  Was it across the

board improvement or were improvements only attributed tocertain parts of the map?

Baseline BSFC maps were n

Simulation

methodology

78 Some assessment of the model uncertainty would be helpful.  

This could be a qualitative rating assigned by the advisorycommittee or a more rigorous method could be used.

For future consideration in an

Simulation

methodology

79 More detail on the types of models is required.  Do some models

use first principals of physics and others lumped parameter?

Has be addressed with inclus

EASY5 model description/cita

Simulation

methodology

80 ANOVA or some other analytical approach to consider technology

interactions needs to be deployed. 

For future consideration in an

Simulation 81 It says a statistical analysis was used to correlate variations in the Has be addressed with revisi

methodology input factors to variations in the output factors.  This is

ambiguous. What analysis method was used? Where is it reported? I didn’t see anything in the results about this.  It wasused to generate the RSM, but what was the measure of fitment?

How did the RSM fit compare from vehicle config to vehicleconfig. 

of report  

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Completeness 148 When it comes to GHG reductions why weren’t plug­in hybrids

considered?

GHG reductions for PHEVs a

applying a utility factor (percethe results of this study for thvehicle.

Recommendations 149 Instead of using proprietary Ricardo data/models/control

algorithms citable data should be used.   

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. 

Recommendations 150 Without stating how this model is going to be used in the

regulatory decision making process, it is very difficult to assess itsappropriateness. 

The following EPA document

the 2017­2025 rule is relevanthis comment: Chapter 3 of thSupport Document, and ChaRegulatory Impact Analysis.

Recommendations 152 Guidelines for appropriate use should be given. The new user manual for the

present instructions for use acase. 

Recommendations 155 In terms of acceptable use: rather that trying to use the model to

assess the boundaries of the envelope (or which technology isbetter), the tool could be used to find the areas of maximumoverlap. In other words, knowing that the same performance andfuel economy is achievable using different technologies lendsmore confidence that the result is achievable.  Theoretically thisnumber could be a calculated value generated from the RSM’s.

EPA and Ricardo appreciate 

response needed.   

Recommendations 156 Recommend allowing “real world” drive cycles to assess the

robustness of the results. Could be a user generated result from acomposite of the data sets already generated. 

EPA and Ricardo appreciate 

response needed.   

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Topic

Comment

Excerpt

No.  Comment Respons

Recommendations 157 Should define the process for data selection....eventually you’ll be

asked by a manufacturer, ‘how do we get ‘x’ technology includedfor consideration in the study.

EPA and Ricardo appreciate 

response needed.   

Other Comments 159 Having conducted a similar effort for USCAR on the PNGV

program, I understand that considerable effort is required todevelop such a model. I don’t want to diminish all the hard workthat was done, by only offering criticism in the above sections.  It appears that the intent of the approach to this activity is in theright place, just better documentation is needed and appropriateuse guidelines.

EPA and Ricardo appreciate 

further response is required. 

Inputs and

Parameters

Baseline vehicle

subsystemmodels/maps

161 The models/maps for the subsystems used in these vehicle

models were not provided in the report so that their adequacycould not be assessed.

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. Amaps relevant to the engine mthe effects of the valve actuasystem, and boost system wethe final torque curves and futherefore subsystem performturbine and compressor efficirelevant to this study. 

Inputs and

Parameters

Baseline vehicle

subsystemmodels/maps

162 Including these baseline models in the report would assist in

assessing the development process as well as the adequacy of the new technology subsystem models/maps, which was not possible in this peer review.

See response to Comment E

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Topic

Comment

Excerpt

No.  Comment Respons

Inputs and Engine 169 Issue:  The technology “package definitions” precluded an EPA and Ricardo acknowledg

Parameters technology

selection

examination of the individual effects of a variety of technologiessuch as a single stage turbocharger vs. series­sequentialturbochargers.   

As with any study, there is a 

ability to evaluate each variabto contain the study to a manRicardo subject matter expertype of turbochargers used in

Inputs and

Parameters

181 None of the subsystem models/maps were provided for review so

comments on their adequacy are not possible.

See response to Comment E

Inputs and 182 Issue:  Insufficient reasons are presented to justify why the See response to Comment E

Parameters models/maps for subsystems are not provided in the report, 

especially when one of the goals of the report was to provide

transparency (per Jeff Cherry, May 5, 2011 teleconference andItem 5, below). 

Inputs and

Parameters

184 Recommendation:  To establish the adequacy of the subsystem

models/maps, derivation details should be provided.

See response to Comment E

Simulation

methodology

198 Concern:  Methodologies used in simulating the subsystems and

the overall vehicles were not provided, so that the validity andapplicability of these methodologies cannot be assessed.

See response to Comment E

Simulation

methodology

Major

deficiencies in thereport 

200 Technical descriptions of how the subsystems and vehicle

models/maps for the baseline vehicles were developed were not provided. 

See response to Comment E

Simulation

methodology

Major

deficiencies in thereport 

201 Most importantly, only non­technical descriptions of how each of 

the advanced technology subsystem models/maps wasdeveloped were provided.

See response to Comment E

Simulation

methodology

Major

deficiencies in thereport 

203 Descriptions of how synergistic effects were handled were not 

provided. 

Synergistic effects are inhere

Ricardo vehicle models.

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Results Overview of 

results

211 The results from this work could be useful in evaluating possible

GHG emission reductions in the 2020­2025 timeframe if theissues throughout this peer review were addressed and therecommendations in Item 5 (below) were implemented.  However, even if the foregoing deficiencies were resolved, the foregoingcaveat that there are numerous technologies that have potentialfor reduced GHG emissions that were not included in this studymust be recognized (see Item 1B, above). 

EPA believes that the overall

final report address the core the reviewers during the peeragrees that other technologieGHG emissions (see the full considered in Attachment A tbut also must develop study enable a report such as this ospecific options within the coeffective study design. 

Results Sample runs of 

CSM

212 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: Baseline engine with

AT6­2010 to Stoichiometric DI Turbo, Stop­Start, AT8­2020

­ 38.7% improvement in M­H mpg­ Lumsden et al. (2009) identified a 25­30% improvement in

mpg for a 50% downsized, DI, Turbo engine. 

­ The remaining 9­14% potentially could be explained by stopstart and the change from AT6­2010 to AT8­2020 (although

the details of the systems and the models used would beneeded to make this assessment). 

Baseline engines cannot be c

advanced technologies in the tool has been modified to pre

Results Sample runs of 

CSM

213 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: AT8­2020 to DCT­ 3.3% improvement in M­H mpg­ This improvement appears reasonable. 

EPA and Ricardo appreciate 

further response is required. 

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Topic

Comment

Excerpt

No.  Comment Respons

Results Sample runs of 

CSM

214 In the review process, several sample runs of the Complex

Systems Model (CSM) for the Standard Car (Toyota Camry) weremade and the results are shown in the attached chart (at the endof this peer review) and summarized below: Stoichiometric DI Turbo with Stop­Start to P2 Hybrid­ 18.2% improvement in M­H mpg­ This improvement appears reasonable. 

EPA and Ricardo appreciate 

further response is required. 

Results Issue with CSM 219 Some examples where the model did not allow a buildup of 

comparison cases are:­ Baseline engine with AT­2010 to AT­2020 to DCT­ Baseline engine without stop­start to with/stop­start 

Baseline engines cannot be c

advanced technologies in the tool has been modified to pre

Results Other issues 222 When the baseline cases were run in the Complex Systems

Model, incorrect values of displacement and architecture wereshown in the output.  ­ As an example shown on the attached chart (copied from the

output of the CSM), the baseline for the Standard Car with a2.4L engine shows a displacement of 1.04L.  

­ For the same example, the architecture is shown as

“conventional SS”, whereas the baseline was understood tonot have the stop­start feature (page 22, Table 5­2). 

Baseline engines cannot be c

advanced technologies in the tool has been modified to pre

Completeness 224 An overall schematic and description of the powertrain and

vehicle models and the associated subsystem models/maps werenot provided.  Only vague descriptions were included in the text of the report. 

See Figure 6.1 in the final rep

numerous changes made to on these types of issues thro

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Completeness 225 Technical descriptions of how the subsystems and vehicle

models/maps for the baseline vehicles were developed were not provided.  

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of the mrelated issues, and how the sthem. 

Completeness 226 None of the overall or subsystem models/maps were provided for

review so comments on their adequacy are not possible.

See response to Comment E

Completeness 227 Most importantly, only minimal descriptions were provided of how

each of the advanced technology subsystem models/maps wasdeveloped. 

See response to Comment E

Completeness 228 Descriptions of the algorithms used for engine control, 

transmission control, hybrid system control, and accessory controlwere not provided.  

See response to Comment E

Completeness 229 Descriptions of how synergistic effects were handled were not 

provided. 

The synergistic effects are in

Ricardo proprietary vehicle m

Recommendations 231 This report needs major enhancements to reach the stated goal of 

being open and transparent in the assumptions made and themethods of simulation.  Recommendations to rectify thedeficiencies in these areas are provided in the previous fouritems.  

See response to Comment E

Recommendations Overall

recommendations

232 Overall Recommendation:  Provide all vehicle and powertrain

models/maps and subsystem models/maps used in the analysis inthe report so that they can be critically reviewed.

See response to Comment E

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Peer Review Response Document N

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Recommendations Overall

recommendations

233 Overall Recommendation:  Expand the technology “package

definitions” to enable evaluation of the individual effects of avariety of technologies. 

The technology selections an

were selected to provide a reof combinations that reflect thprogram team of some of theexpected combinations acrosduty classifications.  The full considered is set forth in Attafinal report. In addition, whileproprietary data was a fundathe study design, Ricardo hasdetails and graphics, includinpublically available referenceincrease the transparency anthe final report. While EPA agcombinations are of interest, was a significant undertakingbudget and time, with the optreport is one of the technical EPA's ongoing rulemaking efwas designed to support thatanticipates that others and pecontinue to explore these issstudies that add scope. 

Recommendations Specific

recommendationsfor improvements

235 Provide technical descriptions of how the subsystems and vehicle

models/maps for the baseline vehicles were developed.

See response to Comment E

Recommendations Specific

recommendationsfor improvements

236 Provide overall system and subsystem models/maps in the

report. 

See response to Comment E

Recommendations Specific

recommendationsfor improvements

237 Provide detailed technical descriptions of how each of the

advanced technology subsystem models/maps was developed.

See response to Comment E

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Peer Review Response Document N

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Comment

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No.  Comment Respons

Recommendations Specific

recommendationsfor improvements

239 Provide detailed descriptions of how synergistic effects were

handled. 

This is inherent to Ricardo's p

models. 

Recommendations 243 Recommendation:  Subsystem models/map should be added to

this report and another peer review conducted to assess theiradequacy before this report is released. 

See response to Comment E

Other Comments 248 The vehicle model and powertrain model were developed and

implemented by Ricardo in the MSC.Easy5 software package.  The model reacts to driver input to provide the torque levels andwheel speeds required to drive a specified vehicle over specifieddriving cycles.  The overall model consists of subsystem modelsthat determine key component outputs such as torque, speeds, heat rejection, and efficiencies.  Subsystem models are expectedto be required for the engine, accessories, transmission, hybridsystem (if included), final drive, tires and vehicle, although thereport did not clearly specify the individual subsystem modelsused. 

See response to Comment E

Other Comments 249 A design of experiments (DOE) matrix was constructed and the

vehicle models were used to generate selected performance, fueleconomy and GHG emission results over the design space of theDOE matrix.  Response surface modeling (RSM) was generatedin the form of neural networks.  The output from each modelsimulation run was used to develop the main output factors usedin the fit of the RSM.  The resulting Complex Systems Model(CSM) provides a useful tool for viewing the results from thisanalysis that included over 350,000 individual vehicle simulationcases.

EPA and Ricardo acknowledg

the reviewer's comments.

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Comment

Excerpt

No.  Comment Respons

Other Comments 250 The vehicle and powertrain models/maps and subsystem

models/maps used in the analysis were not provided in the report and could not be reviewed.  In most cases, the report stated that the models/maps were either proprietary to Ricardo or at least elements were proprietary so that they could not be provided forreview.  Without having these models/maps and subsystemmodels/maps, their adequacy and suitability cannot be assessed.

See response to Comment E

Other Comments 251 Overall Recommendation:  Provide all vehicle and powertrain

models/maps and subsystem models/maps used in the analysis inthe report so that they can be critically reviewed.

See response to Comment E

Other Comments 252 The technology “package definitions” preclude an examination of 

the individual effects of a variety of technologies.  For example, for the Stoichiometric DI Turbo engine, only the version with aseries­sequential turbocharger could be evaluated whereas alower cost alternative with a single turbocharger could not beevaluated.  Likewise, only the AT8­2020 transmission could beevaluated with the Stoichiometric DI Turbo engine, while thesubstitution of the AT6­2010, as a lower cost alternative, could not be evaluated.  

See response to Comment E

Other Comments 253 Overall Recommendation:  Expand the technology “package

definitions” to enable evaluation of the individual effects of avariety of technologies.

See response to Comment E

Other Comments 291 Sample Output From Complex System Model (CSM)

5/4/2011Relative Percentage Differences Were Added by W. R. Wade(see Exhibit 9)

EPA and Ricardo acknowledg

the reviewer's comments.

References Used References

(Used for thisReview that arealso listed in theReport)

293 Reference that summarizes the 2008 study by Perrin Quarles

Associates (PQA) that provided the 2010 baseline cases for five

LDV classes (Page 30 of the report): 4.  PQA and Ricardo (2008), "A Study of Potential Effectiveness

of Carbon Dioxide Reducing Vehicle Technologies"

EPA and Ricardo acknowledg

the reviewer's comments.

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Topic

Comment

Excerpt

No.  Comment Respons

Executive 295 For the purpose of describing the modeling approach used in the See response to Comment E

Summary forecasting of the performance of future technologies, the report 

reviewed is inadequate.  In virtually every area, the report lackssufficient information to answer the charge questions provided forthe reviewer.  It is entirely possible that the approach used issatisfactory for the intended purpose.  However, given theinformation provided for the review, it is not possible for thisreviewer to make any statement regarding the suitability of thisapproach. 

Inputs and 296 From a high level, it is clear what the inputs to the design space See response to Comment E

Parameters tool are, which are listed in tables 8.1 and 8.2.  At the next level

down (i.e. the vehicle and subsystem models) there is nocomprehensive handling of inputs in parameters in the report.  Some models are partially fleshed out in this area but most arelacking.  By way of example, the engine models are described asmaps which are “defined by their torque curve, fueling map, andother input parameters” where “other input parameters” are neverdefined. 

Results 298 The third charge questions deals with the validity and the

applicability of the resulting prediction.  The difficulty in this task isthat it is an extrapolation from present technology that uses anextrapolation method (i.e. the model) and a set of inputs to themodel (i.e. future powertrain data.)  Since it is not possible tovalidate the results against vehicles and technology that do not exist, one can only ensure that the model and the model inputsare appropriate for the task.  Because of the lack of transparencyin the model and inputs it is difficult to make any claims regardingthe results.  In trying to validate results, one example is cited inthe body of the report that shows the baseline engine gettingsuperior HWFET and US06 fuel economy than all of the othernon­HEV powertrains with other factors being the same – thisleaves some skepticism regarding the results. 

The advanced turbo engines

downsized, operates outside optimum range on the more dcycles (such as the US06). Laspirated engines tend to havefficiency at high load conditi

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Comment

Excerpt

No.  Comment Respons

Recommendations 300 Given the low level of detail given in the report, it does seem that 

the strategy used is consistent with the goal of the work and what others in the field are doing.  That being said, the report isinadequate in nearly every respect at documenting model inputs, model parameters, modeling methodology, and the sources andtechniques used to develop the technology performance data.  Given the need for transparency in this effort, this reviewer feelsthat the detail in the report is wholly inadequate to document theprocess used.  The organization responsible for the modeling hasexpertise in this area it is certainly possible that the methodologyis sound, however, given just the information in the report there issimply no way for an external reviewer to make this conclusion.

See response to Comment E

Recommendations 301 Because of the lack of hard information to answer the charge

questions, this peer review evolved mainly into a suggested list of details that should be brought forward in order to allow the chargequestions to be answered properly.  With this information, it ishoped that a person with expertise in the appropriate areas will beable comment on the work more fully.

See response to Comment E

Recommendations Aftertreatment/ 

EmissionsSolutions

316 Provide better evidence that powertrain packages have credible

paths to meet emissions standards. 

The modeling ground rules st

2025 vehicles will meet futurerequirements for criteria polluassumed to be equivalent to EPA Tier 2 Bin 2) levels."  Thwere used in the proprietary models. 

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Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Recommendations Aftertreatment/ 

EmissionsSolutions

317 Provide evidence that fuel enrichment strategies are consistent 

with emissions regulations.

The modeling ground rules st

2025 vehicles will meet futurerequirements for criteria polluassumed to be equivalent to EPA Tier 2 Bin 2) levels."  Thwere used in the proprietary models. No enrichment was udevelopment of any of the bofollowing data from Mahle.

Recommendations Hybrid

technology

351 Validate that other vehicle performance metrics, like emissions

and acceleration, are not adversely impacted by an algorithm that 

The ground rules for the proje

simulations meet Tier 2 Bin 2

selection focuses solely on fuel economy.  The emission side of things will

challenge to validate with this level of model, however, some kind

Performance metrics were hevehicles.

of assurance should be made to these factors which are currentlynot addressed at all.

Simulation 370 Provide error metrics for the neural network RSMs (i.e. R2, min Methodology was to fit the RS

methodology absolute error, max absolute error, error histograms, error of the available data and test

standard deviation, etc.) before combining the fit and validation remaining data. Fits were wit

data sets.  (3­5%). 

Simulation

methodology

371 Provide the error metrics described above for the RSMs after

combining the fit and validation data sets. 

See response to Comment E

Simulation 372 Provide validation that the data analysis tool correctly uses the The RSM fit quality is represe

methodology RSM to predict results very close to the source data (i.e.  values. The predicted data w

demonstrate the GUI software behaves as expected). the source data to ensure go

Results 373 As outlined in the executive summary, it was not possible to

answer the charge questions provided for this peer review due to

In the final report, we have ad

detail using publically availab

lack of completeness in the report.  Thus, this report was aimed at providing feedback on what information would be helpful to allow

sources to provide further unissues and how the study add

a reviewer to truly evaluate the spirit of the charge questions. Withthe above in mind, the following conclusions are made.

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Comment

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Results 374 The modeling approach describe in the report could be

appropriate for the simulation task required and is generallyconsistent with approaches used by other groups in this field.  The

conclusions from the report could very well be sound; however, there is insufficient information and validation provided in thereport to determine if this is the case.  The technique used toanalyze the mass simulation runs could also be sound, althoughthe accuracy of the response surface model is not cited in thereport. 

See response to Comment E

Results 375 The process of arriving at the performance of the future

technologies is not well described. 

See response to Comment E

Results 376 The majority of models are only described qualitatively making it 

hard or impossible to judge the soundness of the model.

See response to Comment E

Results 377 Some of the qualitative descriptions of the models indicate that 

models do not consider some important factors. 

See response to Comment E

Results 378 Because of the qualitative nature of the model descriptions, there

is a major lack of transparency in the inputs and parameters in themodels. 

See response to Comment E

Results 379 Where precise value(s) are given for parameters in the model, the

report generally does not cite the source of the value(s) or providevalidation of the particular value.

See response to Comment E

Results 380 Validation of the model and sub­models is not satisfactory (It is

acknowledged that many of these technologies do not exist, but the parameters and structure of the model have to be based onsomething.)

See response to Comment E

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Peer Review Response Document N

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Specific Comment 

Charge Question Assumption/  Excerpt 

Topic Topic No.  Comment Respons

Executive 383 The supplemental review material provided some answers to See response to Comment E

Summary questions posed above, but in general, did not provide the level of 

detail necessary to ensure a thorough review of the process.  Theconclusion of this reviewer remains similar as on the originalreview, which is that there were no serious flaws found in thework, however, there were enough omissions that it is not possible to accurately judge if the predictions made are accurate.  The biggest concern in this work is the lack of validation and/orcitation of where data and models are coming from.  There arenumerous maps that are presented in the follow­up material, however, these maps had to have originated from some process(which needs documented) and should be compared against some kind of validation.  Despite the lack of documentationprovided, the work is generally that of a project team that iscompetent in this field of study. 

Inputs and SI Engine Maps 394 The baseline engine map data is shown in a series of figures and EPA and Ricardo appreciate 

Parameters and Diesel references are provided for the specific vehicle that the map is for. further response is required. 

Engine Maps It is assumed that this indicates that this data has been measured

experimentally.  If this is the case, then this is well documented.

Inputs and

Parameters

407 Curious about why no discussion of advanced materials in

engines to achieve improvements. 

Advanced materials were con

extent that they facilitated othsuch as in friction or mass. Tadvanced materials were notconsidered separately from o

Inputs and

Parameters

409 Future Developments in Engine Friction – I think it would be

worthwhile to point out that there are technologies that are more

EPA and Ricardo acknowledg

the reviewer's comments.

driven by increased durability rather than fuel economy but theycould play off one another. Engine friction reduction is one of those areas. 

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Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Simulation

methodology

414 EHVA: The paper addresses the potential of the technology

nicely. Since it was published in 2003 has any more recent workbeen done to address the durability and issues brought up in theconclusions?

Durability is beyond the scop

Results 417 After reading the papers and presentations I come to the

assumption that the papers were used to guide the selection of technology, but it’s not clear which maps were generated frommodel and which maps were generated in the test cell.  It’sevident that there is a heavy concentration on engine technologyand the fidelity of the engine models, which is appropriate.  I havea slight concern about the impression I’m left with; that there is not much attention to the interaction of systems effects. This is most likely because of cost and availability of data.  I would like to seethe EPA articulate a process for looking at system interactions, continuous improvement and model compatibility.  For example if the study were to run over several years the researches shouldfeel confident comparing a result generated with the models in2013 to modeling results generated today. 

All of the advanced engine m

models were generated usingexperience with engine desigdynamometer test results froengines and are meant to repengine calibration.  The enginmass flow rates based on enload.  Any vehicle system or several systems that would repowertrain work required aremodels by operating the engispeed or load. 

Completeness 418 Hybrid: Ricardo asserts that electric machine design activities of 

the future will most like concentrate around cost reductions; however I see machine efficiency dropping in order to meet cost reductions. Therefore I think it premature to assume that efficiency will stay the same and cost will drop.

Please refer to EPA’s 2017­2

of the joint TSD) to referencecomponent efficiency and costhe agencies.

Inputs and

Parameters

419 Ricardo, Action Item Response, 16 Feb 10, 15 p. (proprietary): A

response to an EPA inquiry, this document deals with enginemaps, engine map comparisons, engine map plots, transmissions, batteries, motor and generator efficiency maps.Comment: Ricardo responses and data selection seemreasonable. 

EPA and Ricardo appreciate 

response needed.   

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

420 Ricardo, Baseline Camry with Alternator Regen and Shift 

Optimizer Development of Optimized Shifting Strategy Light DutyVehicle Complex Systems Simulation EPA Contract No. EP­W07­064, work assignment 2­2, 15 Apr 10, 10 p. (proprietary): Thisdocument provides data on effectiveness of shift optimizer, including alternator regen, over the FTP and HWFET.Comment: Seems reasonable, improvements are greater on FTPthan HWFET.

EPA and Ricardo appreciate 

response needed.   

Recommendations 423 Ricardo, BSFC Map Commparisons, LBDI vs EGR Boost & DVA

for STDI, OBDI, & EGR Boost, Light Duty Vehicle ComplexSystems Simulation, EPA Contract No. EP­W=07=064, workassignment 2­2, 24 Feb 10, 20 p. (proprietary) Comparison of engine technologies in terms of maps of percent difference in bsfcin bmep vs rpm space allows visualization Comment: Straight forward data analysis, presumably as requested by USEPA. Should aid in understanding technology performance differences. 

EPA and Ricardo appreciate 

response needed.   

Inputs and

Parameters

424 Mischker, K. and Denger, D., Requirements of a Fully Variable

Valvetrain and implementation using the Electro­Hydraulic ValveControl System EHVS, 24th International Vienna EngineSymposium 2003, 17 p. This paper describes an electro­hydraulicvalve system (EVHS) and limited data on reduction in bsfc.Comment: This would seem to be of limited quantitative valuesince technology is well advanced beyond 2003. 

EPA and Ricardo appreciate 

response needed.  Backgrouincluded both highly relevant as well as some general inforused during the course of thesources reviewed were of critthe study. 

Recommendations 426 Ricardo, Response to EPA Questions on the Diesel Engine Fuel

Maps, Supplemental Graphs for Word Document, 16 Feb 10, 11p. (proprietary) Document presents proposed diesel engine mapsfor MY2020+ vehicles.Comment: Anticipated technologies are listed but how the mapswere generated is not described. Maps seem reasonable. 

EPA and Ricardo appreciate 

response needed.   

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Comment

Excerpt

No.  Comment Respons

Completeness 427 Ricardo, Assessment of Technology Options, Technologies

related to Diesel Engines, 23 Nov 09, 17 p. Overview predictscontinuation of low uptake in the U.S. LDA and LDT markets. Review deals with various engine technologies to improveefficiency. Individual improvements <1­5%. Most promising iselectric turbo­compounding (bottoming cycle to recover exhaust thermal energy to produce electricity). Comment: Individual technology assessments seem reasonable. There is no analysis of integrating several technologies.

EPA and Ricardo appreciate 

response needed.   

Inputs and

Parameters

428 Ricardo, EBDI Project Overview, Ethanol Boosted Direct Injection, 

Nov 09, 8 p. This study examines ethanol boosted direct injection(EBDI) to optimize engine operation of E85 fuel. Possibility existsto match or exceed diesel performance and reduce CO2emissions.Comment: It is not clear if comparison of EBDI and diesel is a

equal technology level.

See response to Comment E

Inputs and

Parameters

430 UOM, HiTor® for elecgtric, hybrid electric, and fuel cell powered

vehicles, 18 Aug 09, based on test data map, 5 p. Describespower electronics for motor generator control, including anefficiency map for combined controller and motor based on test data. Comment: Efficiency maps seem reasonable. 

EPA and Ricardo appreciate 

Recommendations 431 Odvarka, E., et al., Electgric motor­‐generator for a hybrid electric

vehicle, Engineering Mechanics, 16, 131­‐139, 2009, 9 p. 

Describes electrical machine options of hybrid electric vehicles. Includes efficiency maps for four technologies.Comment: Data are of general interest, but date from 2003. 

See response to Comment E

Inputs and

Parameters

432 UOM, PowerPhase®75 for electric, hybrid electric, and fuel cell

powered vehicles, not dated, 6 p. Described power electronics of vehicle electric power. Comment: Similar to earlier brochure onpower electronics, including efficiency map.  

See response to Comment E

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Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Completeness 433 Ricardo, Future Engine Friction Assessment—Response to Action

Item Question SI Engine #4, 18 Feb 11, 4 p. (proprietary) Projectscontinued reduction in engine friction, 2010­‐2020. 

Comment: Data provide confirm projection. 

EPA and Ricardo appreciate 

response needed.   

Completeness 434 Ricardo, Revised Follow­‐up Answers to 8 April 2010 Meeting

with EPA and Ricardo, 19 Apr 10, 8 p. (proprietary) Presentsfueling maps for several technologies. Comment: Adds to documentation of engine map data. 

EPA and Ricardo appreciate 

response needed.   

Completeness 435 Alger, T., Southwest Research Institute, Examples of HEDGE

Engines, 2009, 4 p. Presents engine map for a 2.4 L I4 High­­‐Efficiency Dilute Gasoline Engine (HEDGE) engine andcompares with TC GDI engine, diesel engine.Comment: Adds to documentation of engine map data. 

EPA and Ricardo appreciate 

response needed.   

Completeness 436 Ricardo, Hybrid Controls Peer Review, 18 Feb 10, 31 p. 

(proprietary)Review of hybrid control technologies for various architectures. Review of battery operation in cold weather.  Comment: Thorough description of technologies and theiroperation characteristics. Battery discussion covers similarmaterial to an earlier paper. 

EPA and Ricardo appreciate 

response needed.   

Inputs and

Parameters

437 Ricardo, Hybrids Control Strategy, 6 Aug 10, 41 p. (proprietary)

Discusses development of control strategies for P2 and PowerSplit hybrids.Comment:  Includes efficiency maps and substantial technicaldetail including vehicle mass effect. 

See response to Comment E

Completeness 438 Ricardo, Simulation Input Data Review, 4 Feb 10, 14 p. 

(proprietary) Described hybrid architectures with emphasis onmachine­inverter combine efficiencies, including efficiency maps.Comment: More data, seems reasonable. 

EPA and Ricardo appreciate 

response needed.   

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Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

439 Ricardo, Assessment of Technology Options, 18 Nov 09, 14 p. 

(proprietary) Assessment of hybrid technologies using evaluationtemplate. Comment: Treats a range of hybrid technologies, including serieshydraulic, giving projections of CO2 reduction benefits.

EPA and Ricardo appreciate 

response needed.   

Inputs and

Parameters

440 Ricardo, Simulation Input Data Review, 2 Feb 10, 30 p. 

(proprietary) Document review modeling parameters for vehicleperformance simulations, including engine efficiency maps for arange of engine and transmission technologies.Comment: This is the kind of data that we requested. Includesshift strategies. Seems reasonable and well­documented.

EPA and Ricardo appreciate 

response needed.   

Simulation

methodology

441 Trapp, C., et al., Lean boost and NOx—strategies to control

nitrogen oxide emissions, (no date), 23 p. Technical paper that describes lean burn direct injection (LBDI) engines, SCR NOxcontrol, and more. Includes some emission control cost data.Comment: Not clear how this related to Ricardo’s modeldevelopment for EPA.

See response to Comment E

Completeness 442 Trapp, C., et al., NOx emission control options for the Lean Boos

downsized gasoline engine, (2 Feb 07), 34 p. Paper compareslean NOx trap and selective catalytic reduction technologies. Includes some engine map data for NOx emissions. Includes cost data for aftertreatment.Comment: Good academic paper with useful data. Not clear what or how Ricardo used.

See response to Comment E

Completeness 443 Trap, C., et al., NOx emission control options for the lean boost 

downsized gasoline engine, (2 Feb 07), 27 p. Paper reviewinternational emissions regulation and technologies to meet.Comment: This paper contains some of the same information asthe preceding two. Simulated date presented, again for SCR andLNT technologies.

See response to Comment E

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Recommendations 444 Ricardo, Lean/Stoichiometric switching load for 2020 Hybrid Boost 

Concept, (no date), 2 p. Presents space velocity and fuel maps.Comment: Relevance not clear. 

See response to Comment E

Recommendations 445 Ricardo, Proposed Lean/Stoichiometric switching load for hybrid

boost concept, 29 Apr 10, 1 p. Identifies proposed lean zone

operating region on engine map. Comment: Relevance not clear. 

See response to Comment E

Results 446 Lymburner, J.A., et al., Fuel consumption and NOx Trade­offs on

a Port­Fuel­Injected SI Gasoline Engine Equipped with a LeanNOx Trap, 4 Aug 09, 20 p. This technical paper examines thetrade­off between NOx control and CO2 emissions.Comment: Good work but relevance not clear. 

See response to Comment E

Results 447 Lotus(?), (from Kapus, P.E. et al., May 2007), Comparison to

other downsized engines This one figure is a partial engine mapwith context vague. Comment: Significance is not clear. 

See response to Comment E

Completeness 448 Turner, J.W.G., et al., Sabre: a cost­effective engine technology

combination of high efficiency, high performance and low CO2emissions, Low Carbon Vehicles, May 09, IMechE Proceedings, 14 p. This paper describes a technology for reducing COsemissions in a downsized engine. The Sabre engine is acollaboration between Lotus Engineering and ContinentalAutomotive Systems.

Comment: Limited performance data provided. 

See response to Comment E

Inputs and

Parameters

449 Ricardo, Conventional Automatic Nominal Results, 16 Mar 10, 17

p. (proprietary) This presentation includes mileage versus 0­60mph time maps for a range of vehicles (light duty to large truck). 

Also presented are comparisons of fuel economy for different 

regulatory test cycles and technologies. Comment: Significance is not clear. 

See response to Comment E

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

451 Ricardo, Revised follow­up answers for hybrid action items, 23

Jun 10, 16 p. (proprietary) This report answers questions onelectric drive train efficiency, battery characteristics, and availablebraking energy, and more. Comment: Interesting data, but implication not clear.

See response to Comment E

Completeness 452 Ricardo, Response to questions regarding the generation of the

diesel fuel maps for fuel efficiency simulation, 16 Feb 10, 10 p. (proprietary) Paper answers a series of EPA questions on how thediesel fuel maps were generated.Comment: This is relevant information and provides a convincingdescription of the technical basis for the diesel fuel maps.

EPA and Ricardo appreciate 

response needed.   

Simulation

methodology

453 Ricardo, Scaling Methodology Review, 19 Jan 10, 9 p. This

document explains the scaling methodology used in the EASY5vehicle model.Comment: This description in clear and useful. 

EPA and Ricardo appreciate 

response needed.   

Completeness 454 Ricardo, SCR as an Enabler for Low CO2 Gasoline Applications, 

no date, 35 p. This presentation describes technology andimplementation for exhaust NOx reduction for lean burn gasolineengines. Comment: Comprehensive discussion of technology, but if andhow inconcorporated in the model not clear. 

See response to Comment E

Completeness 455 Ricardo, Simulation Input Data Review, 18 Mar 10, 17 p. 

(proprietary) This document reviews the engine maps used in themodel. Includes are examples of the baseline maps plusmodifications associated with a range of technologies. Data applyto all 7 vehicle classes.Comment: This is the documentation that was missing in theearlier review material. Looks reasonable and is reassuring.

EPA and Ricardo appreciate 

response needed.   

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments 456 Ricardo, Assessment of Technology Options, 19 Nov 09, 22 p. 

(confidential) This document reviews and rates a range of sparkignition adaptable technologies to reduce CO2 emissions. Biofuels are included.Comment: An interesting compendium but some previouslyreported. 

EPA and Ricardo appreciate 

response needed.   

Completeness 457 Shimizu, R., et al., Analysis of a Lean Burn Combustion Concept 

for Hybrid Vehicles, 2009, 13 p. A technical paper, this document describes early (1984) and more recent Toyota lean burn engines.Comment: Interesting technical description but no clear if or howused in the Ricardo model.

See response to Comment E

Simulation

methodology

458 Takoaka, T., et al., Toyota, Super high efficient gasoline engine

for Toyota hybrid system, (no date), 16 p. This paper describesthe hybrid system, IC engine interaction that allows increased ICengine efficiency.Comment: Of general interest but application to the model not clear. 

See response to Comment E

Inputs and

Parameters

459 Ricardo, Assessment of Technology Options, Technologies

related to Transmission and Driveline, 19 Nov 09, 21 p. Thisdocument described transmission technologies, including timingof their introduction. Comment: Seems reasonable.

EPA and Ricardo appreciate 

response needed.   

Recommendations 460 Ricardo, Transient Performance of Advanced Turbocharged

Engines, 15 Sep 10, 19 p. (proprietary) This report reviewsexpected advances in boosting technologies and anticipatedeffects on vehicle performance.Comment: Interesting information but how it impacts model is not clear. 

See response to Comment E

105
 
Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Completeness 461 Kapus, P., Potential of VVA Systems for Improvement of CO2

Pollutant Emission and Performance of Combustion Engines, 30Nov 2006, 9 p. This is a technical paper describing variable valveactuation approaches and performance effects.Comment: Useful general technical information. 

EPA and Ricardo appreciate 

response needed.   

Inputs and

Parameters

462 Ricardo, Assessment of Technology Options, Technologies

related to Vehicle­level Systems, 24 Nov 09, 16 p. This review of vehicle technologies that can improve vehicle efficienciesprovides a basic description and information on expected levels of CO2 reduction.Comment: This is a clear description of anticipated improvementsin vehicle technologies that reduce load and fuel consumption.

EPA and Ricardo appreciate 

response needed.   

Executive

Summary

463 Ricardo has provided material, which is stated to be the data

incorporated in the computer simulation. These data areconsistent with the data expected to be the basis of thesimulation. It is impossible to establish a precise correspondencebetween the data and the model. The performance data coveredby the 44 separate documents seem reasonable and provideadditional assurance that the simulation is soundly based onmeasured performance. There is no reason to doubt either theintegrity or capability of Ricardo in their incorporation of appropriate data into their simulation model. 

EPA and Ricardo appreciate 

response needed.   

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Other Comments 17 For the most part, the right technologies are being considered. 

However, certain promising technologies and fuel options for ICengine technologies (other than gasoline and diesel) that canmake a significant contribution to the improvement of mpg andreduction of CO2 emissions have not been considered, or evenmentioned at all. Primary examples are advanced combustiontechnologies, such as high pressure, dilute burn, low temperaturecombustion (e.g., Homogeneous Charge Compression Ignition, Partially Premixed Compression Ignition, Spark­AssistedCompression Ignition), and closed­loop, in­cylinder pressurefeedback. Some of these combustion technologies have thepotential to improve fuel economy by up to 25%. Anothersignificant assumption is that fuels used are equivalent to either87 octane pump gasoline or 40 cetane pump diesel. However, advanced biofuels, particularly from cellulosic or lingo­cellulosicbio­refinery processes, which from the standpoint of a life cycleanalysis have strong potential for reduction of CO2 emissions, can have significantly different properties (including octane andcetane numbers) and combustion characteristics than the current fuels. Note that over 13 billion gallons of renewables were used in2010, primarily from corn­ethanol and some biodiesel. Accordingto the Renewable Fuel Standard, 36 billion gallons of renewablesneed to be used by 2022. Also, a joint study carried­out by Sandiaand General Motors has shown that ninety billion gallons of ethanol (the energy equivalent of approximately 60 billion gallonsof gasoline) can be produced in the US by year 2030 under anaggressive biofuels deployment schedule. 

The technology selections an

were selected to provide a reof combinations that reflect thprogram team of some of theexpected combinations acrosduty classifications.  The full considered is set forth in Attafinal report. While EPA agreecombinations are of interest, was a significant undertakingbudget and time, with the optreport is one of the technical EPA's ongoing rulemaking efwas designed to support thatanticipates that others and pecontinue to explore these issstudies that add scope. 

107
 
Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Inputs and

Parameters

183 Recommendation:  Subsystem models/map should be added to

this report and another peer review conducted to assess theiradequacy before this report is released. 

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of thesthe study addressed them. Amaps relevant to the engine mthe effects of the valve actuasystem, and boost system wethe final torque curves and futherefore subsystem performturbine and compressor efficirelevant to this study. 

Completeness 223 Concern:  This report has significant deficiencies in its description

of the entire process used in the modeling work.  Many of thesedeficiencies have been previously discussed, but are listed herefor completeness. 

Use of proprietary data was a

study.  However, in the final radded a great deal of detail uavailable references and soufurther understanding of the mrelated issues, and how the sthem. 

Completeness Section 2

Objectives

122 A discussion of appropriate/anticipated use of the results is

required. 

Please refer to the 2017­202

Chapter 2 of the Joint TSD aEPA's draft Regulatory Impac

Inputs and

Parameters

Engine Models 309 This reviewer took some time to look at the data via the tool

provided.  One table is shown in Figure 1 which shows someunexpected results.  The results are for a small car with the dryclutch transmission and it shows the baseline engine havingsuperior fuel economy over all other non­hybrid powertrainoptions.  This is unexpected behavior and, since there is minimaltransparency in the model, it cannot be investigated any further. (See Exhibit 10)

The baseline engine may not

advanced technologies.  Thecorrected to avoid this issue.

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Peer Review Response Document N

Table 1: Response to Individual Peer Review Comments

Charge Question

Topic

Specific

Assumption/ 

Topic

Comment

Excerpt

No.  Comment Respons

Results 117 On the performance runs, a few tenths of a second represent 

measurable difference in engine torque for example.

EPA and Ricardo appreciate 

ReferencesColtman, et al. (2008), “Project Sabre:  A Close­Spaced Direct Injection 3­Cylinder Engine with Synergistic Technologies to Achieve Low CO2 Output”, SAEHellenbroich, et al. (2009), "FEV's New Parallel Hybrid Transmission with Single Dry Clutch and Electric Torque Support."Lumsden, et al. (2009), “Development of a Turbocharged Direct Injection Downsizing Demonstrator Engine”, SAE Paper 2009­01­1503.PQA and Ricardo (2008), "A Study of Potential Effectiveness of Carbon Dioxide Reducing Vehicle Technologies."Staunton, et al. (2006), "Evaluation of 2004 Toyota Prius Hybrid Electric Drive System", ORNL technical report TM­2006/423.Turner, et al. (2009), ‘Sabre: A Cost­Effective Engine Technology Combination for High Efficiency, High Performance and Low CO2 Emissions”, IMechE co

proceedings. 

109
 
Supplement
 
   

   

     

   

     

   

         

   

Peer Review of
 

Ricardo, Inc. Draft Report, “Computer Simulation of Light­Duty
 

Vehicle Technologies for Greenhouse Gas Emission Reduction 

in the 2020­2025 Timeframe”
 

Final Report

September 30, 2011

Prepared by

ICF International

9300 Lee Highway
 

Fairfax, VA
 

and
 

620 Folsom Street, Suite 200

San Francisco, CA
This report was prepared by ICF International for the U.S. Environmental Protection Agency (EPA), 

Office of Transportation and Air Quality under EPA Contract No. EP-C-06-094, Work Assignment 4-04, 

at the direction of EPA Work Assignment Manager Jeff Cherry. 
 

 

 

Contents
 

1. Introduction........................................................................................................................................... 1
 

2. The Peer Review Process...................................................................................................................... 1
 

3. Verbatim Peer Reviewer Comments in Response to Charge Questions............................................... 3
 

4. References........................................................................................................................................... 74
 

Appendix A. Charge to Peer Reviewers ................................................................................................... A-1
 

Appendix B. Peer Reviewer CVs............................................................................................................. B-1
 

Appendix C. Peer Reviewer Comments as Submitted Round 1 .............................................................. C-1
 

Appendix D. Peer Reviewer Comments as Submitted Round 2 ............................................................. D-1
 

Appendix E. Draft Project Report by Ricardo ..................................................................... E-1
 

blank

page
 

 

 

 

 

 

 

 

 

 

 

 

       

 

 

 

 

 

 

 

 

 

 

Introduction
 

1. Introduction

As the U.S. Environmental Protection Agency (EPA) develops programs to reduce greenhouse gas 

(GHG) emissions and increase fuel economy of light-duty highway vehicles, there is a need to evaluate 

the costs of technologies necessary to bring about such improvements. Some potential technology paths 

that manufacturers might pursue to meet future standards may include advanced engines, hybrid electric 

systems, and mass reduction, along with additional road load reductions and accessory improvements. 

One method of assessing the effectiveness of future light duty vehicle (LDV) technologies on future 

vehicle performance and GHG emissions in the near-term timeframe is through modeling assessments. 

Ricardo, Inc. (2011) developed such simulation models and documented the relevant technologies, inputs, 

modeling techniques, and results of the study in its April 6, 2011, report, Computer Simulation of LightDuty Vehicle Technologies for Greenhouse Gas Emission Reduction in the 2020–2025 Timeframe. 

Ricardo performed this work under a subcontract to Systems Research and Applications Corporation 

(SRA) under EPA contract EP-W-07-064. The report documented both LDV technologies likely to be 

available within the specified timeframe and the development of a visualization tool that allows users to 

evaluate the effectiveness of such technology packages in both reducing GHG emissions and their 

resulting effect on vehicle performance. The technologies addressed including conventional and hybrid 

powertrains, transmissions, engine technologies and displacement, final drive ratio, vehicle weight, and 

rolling resistance were examined for seven light-duty vehicle classes. 

EPA contracted with ICF International (ICF) to coordinate an external peer review of the inputs, 

methodologies, and results described in this report. The review was broad and encouraged reviewers to 

address the adequacy of the model’s inputs and parameters, the simulation methodology, and its 

predictions as well as the report’s completeness and adequacy for the stated goals. 

This report documents the peer review process and provides comments by the peer reviewers in a table 

sorted by charge question topic and subtopics. 

2. The Peer Review Process

From March to September 2011, EPA contracted with ICF to coordinate this peer review. ICF 

coordinated the peer review in compliance with EPA’s Peer Review Handbook (3rd Edition) (U.S. EPA, 

2006). 

EPA requested that the peer reviewers represent subject matter expertise in advanced engine technology, 

hybrid vehicle technology, and vehicle modeling. ICF developed a list of qualified candidates from the 

following sources: (1) ICF experts in this field with knowledge of industry, academia, and other 

organizations, and (2) suggestions from EPA staff. ICF identified ten qualified individuals as candidates 

to participate in the peer review. ICF sent each of these individuals an introductory screening email to 

describe the needs of the peer review and to gauge the candidate’s interest and availability. ICF asked 

candidates to provide an updated resume or curriculum vitae (CV). Several candidate reviewers were 

unable to participate in the peer review due to previous commitments, and one did not respond. ICF 

reviewed the responses and evaluated the resumes/CVs of the interested and available individuals for 

relevant experience and demonstrated expertise in the above areas, as demonstrated by educational 

1
 
 

 

 

 

 

 

 

 

 

      

     

   

   

   

   

 

 

 

 

 

 

The Peer Review Process
 

degrees attained, research and work experience, publications, awards, and participation in relevant 

professional societies. 

ICF reviewed the interested, available, and qualified candidates with the following concerns in mind. As 

stated in the EPA’s Peer Review Handbook (U.S. EPA, 2006), the group of selected peer reviewers 

should be “sufficiently broad and diverse to fairly represent the relevant scientific and technical 

perspectives and fields of knowledge; they should represent a balanced range of technically legitimate 

points of view.” As such, ICF selected peer reviewers to provide a complimentary balance of expertise of 

the above criteria (see Table 1). EPA reviewed and approved ICF’s slate of candidate peer reviewers. 

The following five individuals agreed to participate in the peer review: 

1. Dr. Dennis Assanis, University of Michigan 

2. Mr. Scott McBroom, Fallbrook Technologies, Inc. 

3. Dr. Shawn Midlam-Mohler, The Ohio State University 

4. Dr. Robert Sawyer, University of California at Berkeley 

5. Mr. Wallace Wade, Ford Motor Company (Retired) 

Table 1. Chart of Peer Reviewer Expertise Areas and Affiliation 

Peer Reviewers LDV

Technology

Computer

Simulations

HEV

Technology

D. Assanis,

Academic � � �

S. McBroom,

Industry

� � �

S. Midlam­Mohler,

Academic

� � �

R. Sawyer,

Academic � �

W. Wade,

Industry (Retired) � �

Prior to distributing the review materials, ICF sent each of the reviewers a conflict of interest (COI) 

disclosure and certification form to confirm that no real or potential conflicts of interests existed. The 

disclosure form addressed topics such as employment, investment interests and assets, property interests, 

research funding, and various other relevant issues. Upon review of each form, ICF determined that each 

peer reviewer had no COI issues and then executed subcontract agreements with all reviewers. 

ICF provided reviewers with the following materials: 

• Draft project report by Ricardo (2011); 

• The Ricardo Computer Simulation tool; 

2
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

            

   

 

 

 

 

Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

• The Peer Reviewer Charge to guide their evaluation; and 

• A template for the comments organized around the Peer Reviewer charge. 

The Peer Reviewer Charge provided peer reviewers with general guidelines for preparing their overall 

review, with particular emphasis on inputs, methodologies, and results. The charge to peer reviewers is 

provided in Appendix A. The CVs for the reviewers are included in Appendix B. 

A mid-review teleconference was held on May 5, 2011, to discuss the charge, the purpose of the review, 

and to answer any outstanding questions the reviewers might have. The call was moderated by ICF and 

attended by reviewers Dr. Assanis, Mr. McBroom, Dr. Midlam-Mohler, Dr. Sawyer, and Mr. Wade, as 

well as EPA staff Jeff Cherry, and Ricardo staff who were familiar with the report. During the midreview teleconference, several reviewers expressed some concerns about the level of detail provided in 

the report, but no one requested additional information beyond some cited references. 

The consensus of the first review was that reviewers needed more information than was provided in the 

Ricardo report to complete their review. 

EPA requested a second round of peer review in which the peer reviewers would be provide more 

detailed information. Ricardo provided 45 additional PowerPoint presentations and documents, which 

included more clarity on assumptions, pictures of engine maps, and other pertinent information. ICF 

contacted all five reviewers for interest and availability for this additional review. However, only three 

reviewers confirmed their availability, one could not commit to a five-year term of confidentiality, and 

one did not respond to the inquiry. 

Three individuals agreed to participate in the second round of peer review: 

1. Mr. Scott McBroom, Fallbrook Technologies, Inc 

2. Dr. Shawn Midlam-Mohler, Ohio State University 

3. Dr. Robert Sawyer, University of California, Berkeley 

ICF executed non-disclosure agreements (NDA) with Mr. McBroom, Dr. Midlam-Mohler, and Dr. 

Sawyer. Once the NDAs were in place, ICF sent them the 45 additional review documents, plus the 

reviewer charge and the reviewer charge template. 

3. Verbatim Peer Reviewer Comments in Response to

Charge Questions

Table 2 presents the verbatim comments received by the subject matter experts. Comments are sorted by 

charge question and then topic/categories. Cited exhibits and references are provided starting on page 69 

and 74, respectively. In addition, Appendix C provides the first round of peer reviewer comments as 

they were submitted by the peer reviewers, and Appendix D provides the second round of peer reviewer 

comments as they were submitted. 

3
 
Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

Table 2. Sorted, Verbatim Comments from Reviewers 


Charge Question

Topic

Specific

Assumption/Topic

Comment

Excerpt

No. 

Review

Round Reviewer Comment

Inputs and

Parameters

6.3 Accessories 73 1 McBroom I think the assumption that LDT cooling fans will be engine drive

F150’s have electric fans.

Inputs and

Parameters

6.4 Transmission

Models

76 1 McBroom no efficiency maps, no description of the efficiency maps. What w

Typically it’s gear ratio, torque and speed. 

Inputs and Accessory load 335 1 Midlam­ The accessory model is divided into electrical and mechanical lo

Parameters assumptions Mohler model assumes alternator efficiency’s of 55% and 70% for the b

vehicles respectively.  Given the required simplicity of the modellikely acceptable, however, there is no source described for the base electrical load of the vehicle is mentioned briefly, however,given for each vehicle class or any type of model described.

Inputs and Accessory load 336 1 Midlam­ The electrical system also includes an advanced alternator contr

Parameters assumptions Mohler alternator usage during decelerations for kinetic energy recovery

given is valid but simplistic, but seems to fit the expected level opurpose.  There is an issue regarding with the approach for modprocess.  When charging the battery at the stated level of 200 amof the battery will be relatively poor.  During removal of the energbe an efficiency penalty.  There is no description of a low­voltagenor any explicit reference to such charge/discharge efficiencies. limit of a 200 amp alternator is defined for all vehicle classes – itcar and a future light heavy duty truck will have an alternator wit

Inputs and Accessory load 337 1 Midlam­ On the mechanical side, it is assumed that “required accessories

Parameters assumptions Mohler engine oil pump) are included in the engine maps.  The mechan

fan is mentioned but no description of the model which, at a minbased on engine speed and engine power. 

Inputs and Accessory load 185 1 Wade The accessory selections listed in Table 5­2 (page 22) appear to

Parameters assumptions following issue: Belt driven air conditioning for the stop­start pow

acceptable for driver comfort.  Electrically driven air conditioningpowertrain configuration to provide driver comfort for extended id

Inputs and

Parameters

Accessory load

assumptions

186 1 Wade Input values

Alternator efficiency was increased from the current level of 55%improved efficiency design” (page 26 and 27).   

4
 
Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

Charge Question

Topic

Specific

Assumption/Topic

Comment

Excerpt

No. 

Review

Round Reviewer Comment

Inputs and Accessory load 187 1 Wade Comment: Justification for the increase in alternator efficiency fro

Parameters assumptions added to the report with references provided.  Alternator efficien

and load may be more appropriate than a constant value.

Inputs and Accessory load 188 1 Wade Accessory power requirements were not provided, such as show

Parameters assumptions Ricardo (2008), for example. 

Inputs and Accessory load 189 1 Wade Recommendation:  Both mechanically driven and electrically driv

Parameters assumptions requirements should be clearly provided in the report.  

Inputs and

Parameters

Actual

models/maps forsubsystems(engine, transmission, 

181 1 Wade None of the subsystem models/maps were provided for review s

adequacy are not possible.

hybrid system, accessories, finaldrive, tires andvehicle)

Inputs and

Parameters

Actual

models/maps forsubsystems(engine, transmission, 

182 1 Wade Issue:  Insufficient reasons are presented to justify why the mode

not provided in the report, especially when one of the goals of thtransparency (per Jeff Cherry, May 5, 2011 teleconference and 

hybrid system, accessories, finaldrive, tires andvehicle)

5
 
Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

Charge Question

Topic

Specific

Assumption/Topic

Comment

Excerpt

No. 

Review

Round Reviewer Comment

Inputs and

Parameters

Actual

models/maps forsubsystems(engine, transmission, 

183 1 Wade Recommendation:  Subsystem models/map should be added to 

review conducted to assess their adequacy before this report is 

hybrid system, accessories, finaldrive, tires andvehicle)

Inputs and

Parameters

Actual

models/maps forsubsystems(engine, transmission, 

184 1 Wade Recommendation:  To establish the adequacy of the subsystem 

details should be provided.

hybrid system, accessories, finaldrive, tires andvehicle)

Inputs and

Parameters

Advanced

Valvetrains

318 1 MidlamMohler Two types of advanced valvetrains were included in the study, c

digital valve actuation.  Both of these technologies are aimed at 

(Section 4.1.1) part­load.  The impact of these technologies is difficult to predict

techniques and typically require consideration of compressible flminimum.  Even with an appropriate fidelity model, these systemamounts of optimization in order to determine the best possible ptorque­speed plane of the engine.  It is unclear how these systemaccurate engine maps given the level of detail provided in the re

6
 
Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

Charge Question

Topic

Specific

Assumption/Topic

Comment

Excerpt

No. 

Review

Round Reviewer Comment

Inputs and

Parameters

Aftertreatment/ 

Emissions

315 1 MidlamMohler Based on the report, it seems that emissions solutions are assum

powertrain technology packages selected.  The report discusses

Solutions detail in section 4.2.2 with respect to lean­stoichiometric switchin

somewhat incomplete, in that the way it is written it assumes ope

lowers exhaust gas temperature.  In reality, switching from lean constant load results in higher exhaust gas temperatures.  Despit is indeed generally better to operate a temperature sensitive caor rich rather than hot and lean – so the concept of lean­stoich sexplanation provided is not.  Even without this factual inconsistediscussion of aftertreatment systems would be of benefit given thengines are at present a well­known technology for many years respect to emissions control.  A separate issue is the topic of fuetemperature management which will have an important impact o

emissions are excessive, reduce the peak torque available from 

Inputs and

Parameters

Alternator Regen

Shift Optimizer

385 2 MidlamMohler The alternator regeneration strategy is not well documented.  Th

such as max alternator output and efficiency, are listed as assumfor validation.  The efficiency of the battery is not mentioned in ththat this reviewer has read – battery efficiency for a lead acid bathis would have an impact on the recovery of energy.  Strategiesdrivability and this issue is not discussed in the presentation.

Inputs and Baseline vehicle 160 1 Wade The development of baseline vehicle models with comparison of

Parameters subsystem available 2010 EPA fuel economy test data was appropriate. 

models/maps

Inputs and Baseline vehicle 161 1 Wade The models/maps for the subsystems used in these vehicle mod

Parameters subsystem report so that their adequacy could not be assessed.

models/maps

Inputs and Baseline vehicle 162 1 Wade Including these baseline models in the report would assist in ass

Parameters subsystem process as well as the adequacy of the new technology subsyste

models/maps not possible in this peer review.

Inputs and Baseline vehicle 163 1 Wade Recommendation:  Since the baseline vehicles modeled were 20

Parameters subsystem models/maps for the subsystems used in these vehicle models s

models/maps report before it is released. 

7
 
Verbatim Peer Reviewer Comments in Response to
 

Charge Questions
 

Charge Question

Topic

Specific

Assumption/Topic

Comment

Excerpt

No. 

Review

Round Reviewer Comment

Inputs and

Parameters

Baseline vehicle

subsystemmodels/maps

164 1 Wade A major omission was that a baseline model of a hybrid vehicle, 

complex than the baseline vehicle, was not developed and compeconomy test data for production hybrid vehicles. 

Inputs and

Parameters

Baseline vehicle

subsystemmodels/maps

165 1 Wade Recommendation:  A baseline model of a hybrid vehicle should 

to 2010 EPA fuel economy test data for production hybrid vehicle

Inputs and

Parameters

Battery SOC swing

and SOC

190 1 Wade Although not contained in the report, an email from Jeff Cherry (

revealed that the SOC swing was 30% SOC to 70% SOC or 40%appropriate. 

Inputs and

Parameters

Battery SOC swing

and SOC

191 1 Wade Achieving neutral SOC (neither net accumulation or depletion) fo

is appropriate (page 30). 

Inputs and

Parameters

Battery Warm up 1, 

Battery Warm up 2

387 2 MidlamMohler The battery model described has the following possible problem

simple – but could potentially work for the application and generafidelity of the rest of the model.

Inputs and

Parameters

Battery Warm up 1, 

Battery Warm up 3

388 2 MidlamMohler The battery model described has the following possible problem

ambient temperature for heat rejection.  Most HEVs pull in cabincooling, thus, this will cause modeling error. 

Inputs and Battery Warm up 1,  389 2 Midlam­ The battery model described has the following possible problem

Parameters Battery Warm up 4 Mohler Cpbat term by 200% is a red flag that something might be funda

the model formulation or the data used in the model.  There shomass estimation of the pack and the specific heats of battery mo

literature or through testing. 

Inputs and

Parameters

Battery Warm up 1, 

Battery Warm up 5

390 2 MidlamMohler The battery model described has the following possible problem

battery packs of different classes of vehicles is not described, nofor these different models disclosed. 

Inputs and Boosting System 326 1 Midlam­ Boosting was applied to many of the different powertrain packag

Parameters (4.1.3 and 6.3) Mohler stating what maximum BMEP that was achievable, very little is m

efficiency of the boosted engines were determined.  Among othe

creates a need for spark retard which costs efficiency if comprescomplex issues are tied to combustion which is inherently difficuthe engine model is not well documented in the report. 

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Inputs and

Parameters

Direct Injection

Fuel Systems

322 1 MidlamMohler Because of the availability of research and production data in thi

performance from this technology was used to predict performanmodeling approach.  That being said, the report does not describmight have been used to develop the fuel consumption map of thwhat data sources were used.

Inputs and DOE ranges 192 1 Wade The following DOE ranges for Baseline and Conventional Stop­S

Parameters appropriate, with the exception of Engine Displacement. Since th

Stoichiometric DI Turbo engine appears to be greater than 50% (Standard Car baseline of 2.4L is reduced to 1.04L for the Stoich

46)), the opportunity should be provided to start with a displacme(2.4L) and progressively decrease it to approximatly 50% (1.04LEngine Displacement upper range of over 200%.  The model sho

of increasing the boost pressure as the displacement is reduced

Inputs and

Parameters

DOE ranges 193 1 Wade The following DOE ranges for P2 and PS hybrid vehicles (page 2

(See Exhibit 2)

Inputs and Electric Traction 352 1 Midlam­ The model of electric traction components is not discussed in an

Parameters Components Mohler in the report is that current technology systems were altered by “

electric machine and power electronics.” Given the importance oinverter system in hybrids this is not acceptable.

Inputs and Engine Downsizing 329 1 Midlam­ Engine scaling is used extensively in the report.  Basic scaling b

Parameters Mohler effective pressure is common in modeling at this level of fidelity, 

special description.  However, the report mentions some means relative heat loss with small displacement engines which is not amodel or process used to account for this effect should be explicengine size is one of the key parameters in the design space.

Inputs and Engine Models 306 1 Midlam­ The engine model is the most important element in successfully 

Parameters Mohler future vehicles, since it is the responsible for the largest loss of e

most difficult aspect to predict since it involves many complicate

combustion, compressible flow) which must be considered in pacompliance (i.e. in­cylinder formation, catalytic reduction.) Becamust be viewed with extreme scrutiny in order to ensure quality o

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Inputs and

Parameters

Engine Models 307 1 MidlamMohler The engine models are “defined by their torque curve, fueling ma

parameters.”  This implies that the maps are static representatioversus torque, engine speed, and other unknown input parameterepresenting engine performance in this fashion is consistent witclass of modeling.  This comment deals only with the representaperformance in simulation, the generation of the data contained challenging.

Inputs and Engine Models 308 1 Midlam­ The report outlines two methods were used to produce engine m

Parameters Mohler used for boosted engines and relied upon published data on adv

which would represent production engines in the 2020­2025 timewas used with Atkinson and diesel engines and somehow extrapproduction engines to the 2020­2025 time frame.  The descriptioin the report is unsatisfactory.  It also fails to address how the vato build up to a single engine map for a specific powertrain.  Valiwith future technologies, is also lacking in this area.

Inputs and

Parameters

Engine Models 309 1 MidlamMohler This reviewer took some time to look at the data via the tool prov

Figure 1 which shows some unexpected results.  The results areclutch transmission and it shows the baseline engine having supother non­hybrid powertrain options.  This is unexpected behaviotransparency in the model, it cannot be investigated any further. 

Inputs and Engine technology 342 1 Midlam­ There are a host of different technologies superimposed to creat

Parameters selection Mohler technologies.  There is not a clear process described on how thi

achieved.  For instance, an advanced engine technology may alBMEP.  Greatly improved BMEP often comes at the expense of difficult to model even with sophisticated modeling techniques.  Ilayers of powertrain technology are being compounded upon easum up to the best benefits of all of the technologies – there are interactions.  At the level of modeling described, which are maps unspecified ways; it is not clear how the technology stack­up is c

Inputs and Engine technology 166 1 Wade The engine technologies selected for this study, listed in Table 5

Parameters selection appropriate, but are not all­inclusive of possible future engine tec

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Inputs and

Parameters

Engine technology

selection

167 1 Wade Setting the minimum per­cylinder volume at 0.225L and the mini

3 is appropriate.  However, achieving customer acceptable NVH continues to be problematic.

Inputs and

Parameters

Engine technology

selection

168 1 Wade Issue:  The description of the derivation of all of the engine mode

Inputs and

Parameters

Engine technology

selection

169 1 Wade Issue:  The technology “package definitions” precluded an exam

effects of a variety of technologies such as a single stage turbocturbochargers.   

Inputs and Engine technology 170 1 Wade Issue: There are many engine technologies that have potential fo

Parameters selection that were not included in this study, such as:­Single stage turboc

hybrids­ Biofueled spark ignition and diesel engines­ Natural gasalternative fuel engines­ Charge depleting PHEV and EV

Inputs and Engine technology 171 1 Wade The feasibility of the following assumptions for the engines mode

Parameters selection as indicated below: None of the Stoichiometric DI Turbo engines

Ricardo (2011) limited the turbine inlet temperature to a value asRicardo model (Coltman et al., 2008; Turner et al., 2009; Lumsdthe turbine inlet temperature to reach this limit is expected to resassumed 25­30 bar level in the model (which were obtained in thturbine inlet temperature of 1025C). 

Inputs and Engine technology 172 1 Wade The feasibility of the following assumptions for the engines mode

Parameters selection as indicated below: Turbocharger delays of the magnitude assum

significant driveability issues for engines that are downsized appRicardo (2011) assumed a turbocharger delay of approximately delay published for a research engine was significantly longer atal., 2009). 

Inputs and

Parameters

Future Friction

Assessment

392 2 MidlamMohler The provided presentation does not describe how engine friction

made or how they are modeled.  It provides some data from 199not provide any useful insight into how this information is used.

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Inputs and

Parameters

HEV Battery Model 356 1 MidlamMohler Battery models for HEVs are necessary to adequately model the

The report provides no substantive description of the battery pacmodel was developed by “lowering internal resistance in the battchemistries under development.”  Battery pack size is also not amodel – this has a impact of charge and discharge efficiency of t

Inputs and Hybrid technology 345 1 Midlam­ Hybrid vehicles are particularly challenging to model because of

Parameters selection Mohler allow multiple torque sources, and thus, require som form of torq

(i.e. a supervisory control.)  The report briefly describes a propriestrategy that is used to optimize the control strategy for the FTP,cycle.  The strategy claims to provide the “lowest possible fuel cobe somewhat of an exaggeration – this implies optimality which and verify for such a complicated problem.  The strategy also is over a drive cycle” which is also difficult to achieve in practice in Once can get SOC with a certain window, however, short of knousing the battery ­ it is impossible to develop a totally SOC neutr

Inputs and Hybrid technology 346 1 Midlam­ Another factor that must be considered is that a hybrid strategy t

Parameters selection Mohler efficiency on FTP, HWFET, and US06 does not consider many o

Performance metrics like 0­60 time and drivability metrics often shybrids, the number of stop­start events is sometimes limited froefficiency because of the emissions concerns.  Because of thesestrategy achieving optimal efficiency may be higher than what ca

Inputs and Hybrid technology 347 1 Midlam­ Without even basic details on the hybrid control strategy, it is sim

Parameters selection Mohler this aspect of the work.  Because of the batch simulations with v

characteristics, this problem is not trivial.  Supervisory control str

in the literature require intimate knowledge of the efficiency charcharacteristics of all of the components (engine, electric motors/system, and energy storage system) to develop control algorithmby the lack of validation of the hybrid vehicle model against a knunclear how a “one­size fits all” control strategy can be truly be psuch widely varying vehicle platforms. 

Inputs and Hybrid technology 348 1 Midlam­ A last comment is that there is no validation of the HEV model a

Parameters selection Mohler vehicles.  At a minimum, the Toyota Prius has been dissected su

domain to conduct a validation of this class of hybrid electric veh

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Inputs and

Parameters

Hybrid technology

selection

177 1 Wade The hybrid technologies selected for this study, listed in Table 5.

Inputs and

Parameters

Hybrid technology

selection

178 1 Wade Issue:  The adequacy of the P2 Parallel and PS Power Split Hyb

determined without having, at a minimum, schematics and operaeach system together with comparisons with today’s hybrid syste

Inputs and Hybrid technology 179 1 Wade Although not contained in the report, the teleconference call wit

Parameters selection 5, 2011 revealed that 90% of the deceleration kinetic energy wo

Kinetic energy recovery is limited by the following:­ Maintaining high generator efficiency over the range of speedsencountered during deceleration­ Limitations on the rate at which energy can be stored in the bat­Losses in the power electronics­Some energy is lost when energy is withdrawn from the battery ­ Inefficiencies in the motor at the speeds and torques required. The inefficiencies of each of these four subsystems are in serieseach subsystem had 90% efficiency, the kinetic energy recovery66%.

Inputs and

Parameters

Hybrid technology

selection

180 1 Wade Issue: Capturing 90% of the deceleration kinetic energy is a sign

technology to be used to achieve this goal needs to be explainedadded to the report.  

Inputs and

Parameters

Input Data Review 397 2 MidlamMohler The documentation on the Diesel engine maps was helpful; how

the 2020 engine maps were developed.  This is critical for havingpredictions made for the Diesel powertrains in 2020. 

Inputs and

Parameters

Input Data Review 398 2 MidlamMohler The shift strategy is discussed qualitatively; however, it is not de

understand exactly how it is accomplished.  Shift schedules are validation is shown that would indicate that these shift schedules

Inputs and

Parameters

Input Data Review 399 2 MidlamMohler The torque converter models are standard models, thus, the pro

adequate. 

Inputs and

Parameters

Other inputs 194 1 Wade The Design Space Query within the Data Visualization Tool allow

continuous range of variables within the design space range.  Aluseful for parametric studies, the following risks are incurred with

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Inputs and

Parameters

Other inputs 195 1 Wade The sliders for “Eng. Eff” and “Driveline Eff.” would allow the use

efficiency or driveline efficiency uniformly over the map without hsuch changes.

Inputs and

Parameters

Other inputs 196 1 Wade The slider for weight would allow the user to add hybrid or diese

weight increases without incurring any vehicle weight increase. 

Inputs and Other inputs 197 1 Wade Recommendation:  A default weight increase/decrease should b

Parameters If weight reductions are to be studied, then the user should have

change, with the appropriate weight reduction built into the modearbitrary slider for weight. 

Inputs and

Parameters

Section 3.2 Ground

Rules for Study

63 1 McBroom The vehicle and technology selection process needs further disc

these large simulation studies is that the vast majority of the timeselection and once selected agreeing upon the model/data. 

Inputs and

Parameters

Section 4 64 1 McBroom There was no model data provided. Engine maps, transmission 

efficiency maps etc need to be in the Appendices.  The black bodisconcerting. 

Inputs and

Parameters

Section 4.1.1.1

CPS

65 1 McBroom How were the profiles selected? Was there an optimization proc

given engine type?

Inputs and Section 4.1.1.2 66 1 McBroom Was the actuation power requirement accounted for?  What wer

Parameters DVA what control strategy was used to select the timing/lift profile? W

was the timing/lift profile preset and then unchangeable. I would size changes and the boost changes the timing/lift profile will hav

Inputs and

Parameters

Section 4.1.3

Boosting Systems

67 1 McBroom What about superchargers?  Eaton’s AMS supercharger system

supercharges that are comparable to turbo’s and don’t have the 

Inputs and

Parameters

Section 4.1.4 Other

EngineTechnologies

68 1 McBroom regarding global engine friction reduction, what value(s) was ass

same across all engines? If so, why?

Inputs and

Parameters

Section 4.2 Engine

Configurations

71 1 McBroom Quantification needed ...“The combinations of technologies enco

engine concept provide benefits to the fueling map....”

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Inputs and Shift Optimizer 386 2 Midlam­ Shifting strategy impacts efficiency, performance, and drivability

Parameters Mohler of this and balance all three when calibrating shift maps.  Chang

improve efficiency will have an impact on the other metrics whichvehicle.  Additionally, it is not clear how the optimized shift strateshift strategy is, or how it will be applied to the range of transmisstated that is optimizes BSFC, however, there are other constraiaddition to this.

Inputs and

Parameters

SI Engine Maps

and Diesel EngineMaps

394 2 MidlamMohler The baseline engine map data is shown in a series of figures an

the specific vehicle that the map is for.  It is assumed that this inbeen measured experimentally.  If this is the case, then this is w

Inputs and SI Engine Maps 395 2 Midlam­ For the 2020 engine maps, there is insufficient detail in this pres

Parameters and Diesel Engine

Maps

Mohler were generated.  Getting accurate simulation requires careful va

as the data in the model – these engine maps are not sufficiently make a judgment on their suitability for the overall goal of the simthese future engines do not exist, but there had to be some procengine maps.  Without more information on this process it is simon their accuracy.  

Inputs and

Parameters

Transmission

technologyselection

173 1 Wade The transmission technologies selected for this study, listed in T

appropriate. 

Inputs and

Parameters

Transmission

technologyselection

174 1 Wade The forecast that current 4­6 speed automatic transmissions will

2025 is appropriate for all except the smallest and/or low cost ve

Inputs and

Parameters

Transmission

technologyselection

175 1 Wade The report mentions that the transmissions include dry sump, im

efficiency, improved kinematic design, super finish, and advance22). 

Inputs and Transmission 176 1 Wade Recommendation:  The detailed assumptions showing how the b

Parameters technology

selection

improved component efficiency, improved kinematic design, supdriveline lubricants were added to the transmission maps shouldbefore it is released. 

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Inputs and Transmissions 360 1 Midlam­ This peer reviewer is not as well­practiced in transmissions as in

Parameters Mohler Because of this, a more limited review was conducted of this asp

the other areas of the report, the general concern in this area is 

documentation of the modeling approach and validation.

Inputs and Turbo Lag 391 2 Midlam­ The data and methods used in modeling turbo lag are appropria

Parameters Mohler explanation and data to support the model. 

Inputs and Vehicle model 303 1 Midlam­ The vehicle model is described as “a complete, physics­based v

Parameters issues Mohler system model” developed in the MSC.Easy5TM simulation envir

not particularly helpful in defining the type of model as portions o

physics based, such as the various empirical maps used or sub­model which is by necessity an empirical model due to the comp

process compared to the expected level of fidelity of the model. standard longitudinal model accounts for rolling losses, aero lossmodel the forces acting on the vehicle.  Input parameters for thedescribed.  The baseline vehicle platforms are listed, however, tare not provided (rolling resistance, drag coefficient, inertia.)

Inputs and

Parameters

Warm­Up

Methodology

332 1 MidlamMohler The report describes a 20% factor applied to bag 1 of the FTP­7

10% factor applied to the advanced vehicles.  The motivation forqualitatively and is valid, as many organizations are currently invselectively heat powertrain components to combat friction effectsthese factors that were selected are not backed up with any datathat the two values cited are such round numbers ­ the data fromderived should be cited.  Because of the complexity of this phenempirical model is justified.  The model described in the report isjudge its suitability.

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Inputs and 20 1 Assanis The report describes a comprehensive set of engine and vehicle

Parameters prediction of GHG emissions and performance. However, the ful

parameters is not explicitly presented. It requires the reader to reTool figures to simulation environment, it is impossible to extractbasis for a number of critical inputs. In some occasions, the repodata have been used, but there are no references to the source.torque converter maps and shifting maps, electric machine efficistrategies for hybrids, which have very direct effects on vehicle pshould be presented in the report, at least in a limited format.

Inputs and 21 1 Assanis Some examples of the types of inputs and parameters that woul

Parameters following in the report: Any published fuel economy maps, or oth

numbers. For proprietary maps and data, a normalized represenwell, without the actual bsfc values shown on the map.

Inputs and

Parameters

22 1 Assanis Some examples of the types of inputs and parameters that woul

following in the report: Baseline maps used to represent turbomanormalized form. 

Inputs and 23 1 Assanis Some examples of the types of inputs and parameters that woul

Parameters following in the report: The baseline vehicle cooling system and 

cooling system and accessory load schematics of the future engsimulation.

Inputs and

Parameters

24 1 Assanis Some examples of the types of inputs and parameters that woul

following in the report: Details of EGR modeling parameters, sucpercentage of EGR being used at various loads.

Inputs and 25 1 Assanis Some examples of the types of inputs and parameters that woul

Parameters following in the report: Details of warm­up model parameters, su

warm up friction correction; cold start fuel consumption correctiorejection maps for various combinations in the simulation matrix.

Inputs and 26 1 Assanis The engine technology selection appears somewhat limited in te

Parameters combinations. For example, why is the Atkinson engine not boos

variable valve actuation technology, as common and important a

not included. As already stated in the introductory comments, adtechnologies, such as HCCI, are worth considering. More flexibil

parameters would also allow better understanding of the improveindividual technologies and possibly even show some potential s

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identified.

Inputs and 27 1 Assanis Alternative fuels are currently a key research topic and very impo

Parameters independence. Because usage of these fuels can have an impac

emissions, the study would be enhanced if engine performance 

included.

Inputs and

Parameters

69 1 McBroom How was the FEAD electrification energy balance accomplished

placed on the alternator?

Inputs and

Parameters

70 1 McBroom No mention or consideration of cylinder deactivation technologie

hanging fruit, even on downsized boosted engines, especially if 

Inputs and

Parameters

72 1 McBroom How were baseline BFSC maps modified?  Was it across the bo

improvements only attributed to certain parts of the map?

Inputs and

Parameters

74 1 McBroom Limiting the alternator to 200A is very conservative, particularly i

14V.

Inputs and

Parameters

75 1 McBroom Is there any accounting for the energy conversion on hybrids fro

low voltage?

Inputs and

Parameters

401 2 McBroom Battery Model: Overall the battery model is sound; however, I do

modeling is included.  The  FTP testing doesn’t include cold testup should be included and the battery is consistent at those tem

Inputs and

Parameters

402 2 McBroom Engine Model: I see data on the HEDGE engine technology but 

engine technologies unless it’s the high EGR DI gasoline engine

Inputs and

Parameters

403 2 McBroom Engine Model: The trend in engine technology is forced inductio

the selection of turbo only is too limiting. I anticipate variable specombination of forced induction. I think the study would do well t

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Inputs and

Parameters

404 2 McBroom Rgen Alternator: Ricardo (2011) states ­ 70% efficient alternator

efficiency is a function of temp, speed and load. 70% is probablyunlikely that it will operate there for  the duration of the condition

Inputs and

Parameters

405 2 McBroom Diesel Engine Fuel Maps: The presentation shows the technolog

doesn’t discuss how the 2020 bsfc maps were arrived at. It mighsame method for comparison that the authors used to show LBD

Inputs and

Parameters

406 2 McBroom Diesel Technology: Curious about the author’s comment regardi

to avoid variable speed”. Why not variable speed?

Inputs and

Parameters

407 2 McBroom Curious about why no discussion of advanced materials in engin

Inputs and

Parameters

408 2 McBroom EBDI Engine: Couldn’t find fuel economy benefit discussion in p

as gasoline or energy equivalent. I know CO2 is proportional, bu

Inputs and

Parameters

409 2 McBroom Future Developments in Engine Friction – I think it would be wor

are technologies that are more driven by increased durability ratthey could play off one another. Engine friction reduction is one o

Inputs and 296 1 Midlam­ From a high level, it is clear what the inputs to the design space 

Parameters Mohler tables 8.1 and 8.2.  At the next level down (i.e. the vehicle and s

no comprehensive handling of inputs in parameters in the reportfleshed out in this area but most are lacking.  By way of exampledescribed as maps which are “defined by their torque curve, fueparameters” where “other input parameters” are never defined. 

Inputs and 302 1 Midlam­ The simulation methodology is generally not described in the rep

Parameters Mohler assess the validity and accuracy of the approach.  The models a

qualitatively; however, this is insufficient to truly evaluate the abito perform the desired function.  The following subsections addremodels, inputs, and parameters and suggest possible correctiveissues.

Inputs and 1 1 Sawyer The vehicle classes and baseline exemplars are reasonably cho

Parameters that vehicle size, footprint, and interior volume for each class be 

year. It is likely that new vehicle classes will emerge by 2025 and

restraints will be relaxed.

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Inputs and

Parameters

2 1 Sawyer The design of experiment (DoE) ranges, Tables 5.4, 5.5, 8.1, an

not exclude likely sizings. The assumed alternator baseline and efficiencies are reasonable. The assumed reduction in automaticreasonable, but not aggressive for 15 development years from ththe state­of­charge swingfor hybrid modeling of 30­70% is reasonable, but does not reflectechnology for the 2020­25 period, which should allow a greater size, weight, and cost.

Inputs and

Parameters

419 2 Sawyer Ricardo, Action Item Response, 16 Feb 10, 15 p. (proprietary): A

inquiry, this document deals with engine maps, engine map comtransmissions, batteries, motor and generator efficiency maps.Comment: Ricardo (2011) responses and data selection seem re

Inputs and

Parameters

420 2 Sawyer Ricardo, Baseline Camry with Alternator Regen and Shift Optimi

Optimized Shifting Strategy Light Duty Vehicle Complex SystemNo. EP­W­07­064, work assignment 2­2, 15 Apr 10, 10 p. (proprprovides data on effectiveness of shift optimizer, including alternHWFET.Comment: Seems reasonable, improvements are greater on FTP

Inputs and

Parameters

421 2 Sawyer Carlson, R., et al., Argonne National Laboratory, On­Road Evalu

Electric Vehicles over a Wide Range of Ambient Temperatures EPaper reports on­road and dynamometer testing of two hybrid veand hot (33 decC) conditions. Fuel economy increases with temtemperatures with the system which does not limit battery tempeprovides data showing importance of temperature on hybrid vehdata are used by Ricardo to validate their battery warm up mode

Inputs and

Parameters

424 2 Sawyer Mischker, K. and Denger, D., Requirements of a Fully Variable V

implementation using the Electro­Hydraulic Valve Control System Vienna Engine Symposium 2003, 17 p. This paper describes ansystem (EVHS) and limited data on reduction in bsfc.Comment: This would seem to be of limited quantitative value siadvanced beyond 2003.

20
 
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Inputs and 425 2 Sawyer Ricardo, Engine and Battery Warm­Up Methodology, Light Duty 

Parameters Simularion, 17 Feb 10, 16 p. (proprietary) Document reviews eng

strategies and provides a simple model. Comment: The approach to battery warm­up is uncertain. Points(FTP for fuel economy compliance versus test for EPA label vers

Inputs and 428 2 Sawyer Ricardo, EBDI Project Overview, Ethanol Boosted Direct Injectio

Parameters examines ethanol boosted direct injection (EBDI) to optimize eng

Possibility exists to match or exceed diesel performance and redComment: It is not clear if comparison of EBDI and diesel is a eq

Inputs and 430 2 Sawyer UOM, HiTor® for elecgtric, hybrid electric, and fuel cell powered

Parameters on test data map, 5 p. Describes power electronics for motor gen

efficiency map for combined controller and motor based on test dComment: Efficiency maps seem reasonable. 

Inputs and

Parameters

432 2 Sawyer UOM, PowerPhase®75 for electric, hybrid electric, and fuel cell 

6 p. Described power electronics of vehicle electric power. Commbrochure on power electronics, including efficiency map.  

Inputs and 437 2 Sawyer Ricardo, Hybrids Control Strategy, 6 Aug 10, 41 p. (proprietary) 

Parameters control strategies for P2 and Power Split hybrids.

Comment: includes efficiency maps and substantial technical deeffect. 

Inputs and 439 2 Sawyer Ricardo, Assessment of Technology Options, 18 Nov 09, 14 p. (

Parameters hybrid technologies using evaluation template. 

Comment: Treats a range of hybrid technologies, including serieprojections of CO2 reduction benefits. 

Inputs and 440 2 Sawyer Ricardo, Simulation Input Data Review, 2 Feb 10, 30 p. (propriet

Parameters modeling parameters for vehicle performance simulations, includ

for a range of engine and transmission technologies.Comment: This is the kind of data that we requested. Includes sreasonable and well­documented. 

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Inputs and 449 2 Sawyer Ricardo, Conventional Automatic Nominal Results, 16 Mar 10, 1

Parameters presentation includes mileage versus 0­60 mph time maps for a 

to large truck). Also presented are comparisons of fuel economycycles and technologies.Comment: Significance not clear.

Inputs and 451 2 Sawyer Ricardo, Revised follow­up answers for hybrid action items, 23 J

Parameters This report answers questions on electric drive train efficiency, b

available braking energy, and more. Comment: Interesting data, but implication not clear.

Inputs and 459 2 Sawyer Ricardo, Assessment of Technology Options, Technologies relat

Parameters Driveline, 19 Nov 09, 21 p. This document described transmissio

timing of their introduction.Comment: Seems reasonable.

Inputs and 462 2 Sawyer Ricardo, Assessment of Technology Options, Technologies relat

Parameters 24 Nov 09, 16 p. This review of vehicle technologies that can im

provides a basic description and information on expected levels 

Comment: This is a clear description of anticipated improvementthat reduce load and fuel consumption. 

Simulation

methodology

Major deficiencies

in the report 

199 1 Wade An overall schematic and description of the powertrain and vehic

associated subsystem models/maps were not provided.  Only vaincluded in the text of the report.

Simulation

methodology

Major deficiencies

in the report 

200 1 Wade Technical descriptions of how the subsystems and vehicle mode

vehicles were developed were not provided. 

Simulation

methodology

Major deficiencies

in the report 

201 1 Wade Most importantly, only non­technical descriptions of how each of

subsystem models/maps was developed were provided. 

Simulation

methodology

Major deficiencies

in the report 

202 1 Wade Descriptions of the algorithms used for engine control, transmiss

control, and accessory control were not provided.  

Simulation

methodology

Major deficiencies

in the report 

203 1 Wade Descriptions of how synergistic effects were handled were not p

Simulation

methodology

4.4 Transmission

Technologies

88 1 McBroom How were the gear ratios selected? What about shift logic?

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Simulation

methodology

6.3 Engine Models 92 1 McBroom two methods to develop engine models were discussed. It is not

was used for which engine.  I recommend that one approach be both approaches be applied to each engine to converge to a sol

Simulation

methodology

6.3.1 Warm­up

Methodology

95 1 McBroom How was the engine warmup modeled? Is it a first order transfer

constant?  It said proprietary data was used, but how? Does thewarmup depending on size and engine technology?

Simulation

methodology

6.3.2 Accessories 96 1 McBroom Constant alternator efficiency and load is not a very good assum

technologies and higher alternator loads due to electrification andemands.  Will the future still continue to use 14V or will higher v

Simulation

methodology

6.8 Hybrids 97 1 McBroom Were separate optimization runs to determine the best control st

assured the best control strategy is implemented?

Simulation

methodology

7.2 Nominal Runs 98 1 McBroom Was a separate matrix of simulations run to obtain the nominal s

engine or was it merely a matter of matching the peak torque. 

Simulation

methodology

Accessories

Models (Section6.3.2)

38 1 Assanis Specific suggestions regarding models that need more detailed 

efficiency has been assumed to be constant around 55% for basvehicles the alternator efficiencies do vary with the temperature 

Simulation Accessories 39 1 Assanis Specific suggestions regarding models that need more detailed 

methodology Models (Section

6.3.2)

compressor load been considered in any of the simulations? In s

proposed by EPA, it is required that AC remains ON throughout management of the AC load is very critical.

Simulation Baseline vehicle 204 1 Wade Ricardo (2011) developed baseline vehicle simulations for 2010 

methodology model validation

results

economy data were available (page 30).  “For the 2010 baselinemaps and related parameters were developed for each specific (page 25).  Even though these are production vehicles, the modedescribed (including whether they were derived from actual meathey were not provided in the report so that their appropriateness

Simulation Baseline vehicle 205 1 Wade Table 7.1 shows the calculated vs. EPA test data for the baselin

methodology model validation

results

performance.  This table should include percentage variation of tthe test data.  The agreement of the model with the test data is wlarger error than some of the incremental changes shown in Appwould have been expected.

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Simulation

methodology

Baseline vehicle

model validationresults

206 1 Wade Recommendation: A closer examination of the reasons for the u

between the models and baseline vehicles’ EPA fuel economy teundertaken so that the models could be refined to provide better

Simulation

methodology

Cold Start 

CorrectionMethodology

384 2 MidlamMohler The correction used to adjust fuel economy for cold start is desc

The method is based on two pieces of information:1. A set of thrvehicle’s instantaneous fuel multiplication correction factor2. A pshows a fleet­wide average for 2007 of the instantaneous fuel mfactorThe instantaneous fuel multiplication correction factor is nopresentation, however, it is assumed to be the sum of the “short term fuel trim.”  If this is the case, then this value doesn’t correlaconsumption, but rather, to errors in the injector characterizationand air estimation algorithm in the engine controller.  The enginemaintain stoichiometry based on oxygen sensor measurements,simply the feedback correction values required to do this based in the ECU.  By way of example, I could alter the fuel tables of acause the feedback control system to correct by an opposite 15%the fuel consumption of the vehicle once the control system has happen in seconds.I don’t disagree necessarily with the magnituthey are based mostly on EPA bag fuel economy data.  If I am cthe correction factor then the method is not valid.

Simulation

methodology

Constraints 41 1 Assanis Specific suggestions regarding models that need more detailed 

discussion in the report that discusses the constraints on the comimplemented in real life. For example, would a multi­air system tsmall size engines work for a full size car?

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Simulation

methodology

Engines and

Engine Models

30 1 Assanis Specific suggestions regarding models that need more detailed 

It is not clear whether the engine maps in the simulation tool wer

(Sections 4.1 and6.3)

simulations or existing experimental data, somehow fitted and scconfigurations. In general, the explanation on how maps were obimportant component. In one section, the report states that the fuengine model parameters used in the study were based on publnice to have a list of the published materials that have been used 4.2, the report states that the performance of the engines in 202

taking the current research engines and assuming the performanengines will match that of the research engine under consideratitake into account the emission standards in 2020, and do the cumatch those emission standards? What is the systematic methoadopted to scale the performance and fuel economy of current bmodels for 2020­25? Also, the report lacks detail concerning theextrapolating from available maps to maps reflecting the effects 

performance of the combination of the future technologies consid

Simulation

methodology

Engines and

Engine Models

31 1 Assanis Specific suggestions regarding models that need more detailed 

detail on the specifics on the different engine design and operati

(Sections 4.1 and what was the compression ratio (and limit) that was used? What6.3) range considered, for the lean burn engine? How much EGR ha

speed and load range? What constraints, if any, were applied tofor combustions limitations such as knock and flammability limitsaftertreatment/constraints section could also be expanded.

Simulation

methodology

Engines and

Engine Models

32 1 Assanis Specific suggestions regarding models that need more detailed 

engine models have been used to generated maps, how was co

(Sections 4.1 and6.3)

instance, discussion is made as to the heat transfer effect resultchanges connected to downsizing. More detail on the heat transthe applied heat transfer factor would be helpful. Was heat transWoschni’s correlation? What about friction scaling with piston spwith stroke at a constant RPM. Also friction would change with thcylinders.

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Simulation Intelligent Cooling 34 1 Assanis Specific suggestions regarding models that need more detailed 

methodology Systems (Section

4.3.1)

describes intelligent cooling systems, but does not provide any ereductions in fuel consumption over the FTP cycle, though relatepublished in the open literature.

Simulation Intelligent Cooling 35 1 Assanis Specific suggestions regarding models that need more detailed 

methodology Systems (Section

4.3.1)

cooling components plays a very crucial role in fuel economy prenot provide any detail on how the optimum cooling flow required

transmission combination was determined. This would significan

transmission oil pump RPMs, which would in turn significantly ch

Simulation Intelligent Cooling 36 1 Assanis Specific suggestions regarding models that need more detailed 

methodology Systems (Section

4.3.1)

report does not have any discussion on how modified cooling cocondenser, etc.) would be sized for more efficient powertrains. Fengine that would reject less heat would likely need a smaller ra

through the radiator; hence, the grill opening could be reduced tohigh efficiency transmission will not reject a lot of heat to the trantransmission oil cooler could be used.  

Simulation Scaling 393 2 Midlam­ With one exception, the scaling methodology appears to be soun

methodology Methodology

Review

Mohler provided in the presentation.  The curve used to adjust BSFC wi

supported with data or any citation of where it originated.  The mseems valid, however, it needs to be supported with data.

Simulation Section 3.4 CSM 77 1 McBroom Is the CSM approach used in other applications? If so it would b

methodology Approach it was developed by Ricardo, that should be stated.  The discuss

models, but other than that very little about the type of modeling phone call that lumped parameter models were mentioned. Ther

Simulation

methodology

Section 4.1.1

Advanced

Valvetrains

82 1 McBroom There is no explanation of how CPS and DVA systems were mo

description of what CPS and DVA is. 

Simulation Section 4.2.1 83 1 McBroom Quantify how did the cooled exhaust manifold/lower turbine inlet

methodology Stoich DI Turbo map.  This is a good example of technology interaction...how did

accommodate the additional heat rejection; how did the frontal aaccommodate the larger radiator?

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Simulation

methodology

Section 4.2.2 Lean

Stoich Switching

84 1 McBroom This type of tech points to one of the dangers of optimizing confi

strategy to the drive cycles; that is that it has the potential to oveeffect the “real world” performance/fuel economy.

Simulation

methodology

Section 4.2.4

Atkinson Cycle

85 1 McBroom How do the 2020­2025 maps differ from the 2010 maps?

Simulation

methodology

Section 4.2.5

Advanced Diesel

86 1 McBroom Why were only the benefits of improved pumping losses or frictio

improvements were assigned to these benefits? Was it across thabout advanced boosting technology for these engines?

Simulation

methodology

Section 6 Vehicle

Models

89 1 McBroom No discussion of how driveline inertia is handled.  This is importa

models. 

Simulation

methodology

Transmission

Models (Section6.4)

40 1 Assanis Specific suggestions regarding models that need more detailed 

efficiencies vary by almost 10­15% based on the transmission othese effects been modeled?

Simulation

methodology

Transmission

optimization

207 1 Wade A transmission shift optimization strategy is presented in the rep

in Figure 6.1 (page 28).  This figure shows very frequent shifting6th gears. 

Simulation

methodology

Transmission

optimization

208 1 Wade Issue:  Optimized shift strategies of the type used by Ricardo (20

evaluated and found to provide customer complaints of “shift buslikely to reject such a shift strategy.  

Simulation Turbocharger 33 1 Assanis Specific suggestions regarding models that need more detailed 

methodology systems (Section

4.1.3)

discussion of turbocharger efficiencies and their range. Did the sboosting technologies? Were maps used for this simulation or so

Was scaling used? What were the allowed boost levels?

Simulation

methodology

Vehicle model

issues

209 1 Wade Although the report described the major powertrain subsystems 

models (page 24), a description of the vehicle model was not pro

Simulation

methodology

Vehicle model

issues

210 1 Wade Issue:  A description of how aerodynamic losses, tire rolling loss

the model was not provided.

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Simulation Warm­up 37 1 Assanis Specific suggestions regarding models that need more detailed 

methodology methodology

(Section 6.3.1)

about using engine warm­up profile during the cold start portion 

fueling requirements. It talks about a correction factor to accountwas this factor determined? Has a different correction factor bee

For instance, for a lean­burn engine that reject less heat, the oil to a baseline engine. Was a new heat rejection map generated tenrichment while modeling the warm­up? What is the ambient teconsidered while performing the FTP 75 fuel economy test? Hav

engine oil considered in the warm up simulation? How have the valvetrain engine combinations been modeled?

Simulation 28 1 Assanis The RSM approach is certainly a good way to provide quick acce

methodology but it has the limitation that a large number of assumptions have

order to determine the design space. Also, creating these encomsignificant amount of simulations, and all the results will not necemore flexible model/simulation was created and coupled to a use

might be able to obtain and analyze the desired results instead odesign space previously determined. 

Simulation 29 1 Assanis Even though the authors attempt to describe the simulation meth

methodology the report, it lacks details of the models employed, which makes

refinements need to be made, or even if more appropriate modeIt is understandable that, due to the proprietary data, it is not poseverything.However, without any of this information, the RSM re

interpret. 

Simulation

methodology

78 1 McBroom Some assessment of the model uncertainty would be helpful.  Th

rating assigned by the advisory committee or a more rigorous m

Simulation

methodology

79 1 McBroom More detail on the types of models is required.  Do some models

physics and others lumped parameter?

Simulation

methodology

80 1 McBroom ANOVA or some other analytical approach to consider technolog

deployed. 

Simulation 81 1 McBroom It says a statistical analysis was used to correlate variations in th

methodology in the output factors.  This is ambiguous. What analysis method 

reported? I didn’t see anything in the results about this.  It was u

but what was the measure of fitment? How did the RSM fit comp

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vehicle config.

Simulation

methodology

87 1 McBroom Ricardo’s expectation for pace and direction: I thought there was

making these decisions.  I’m surprised that they think boost will b

Simulation

methodology

90 1 McBroom There are several types of rolling resistance models, what type w

Simulation

methodology

91 1 McBroom Was coast­down data from the baseline vehicles obtained or wh

resistance and Cd modified to get the data to match?

Simulation

methodology

93 1 McBroom Regarding engine downsizing, I’m not sure that the scaling appro

engines, especially engine with multiple compressors as well as 

Simulation

methodology

94 1 McBroom Turbo lag applied as a first order transfer function with a time co

constant selected? Was it validated? How was the improvementcompounding modeled?

Simulation

methodology

99 1 McBroom How was a 20% reduction in engine size for the nominal hybrid e

the micro­hybrid (engine start/stop)?

Simulation

methodology

100 1 McBroom “These summary results....used to assess the quality of the simu

for this assessment published? What were the criteria that said p

Simulation

methodology

410 2 McBroom Transmission Model: Ricardo (2011) describes an approach that

average efficiency value vs a 3D efficiency map yields insignificaCAFÉ drive cycles, but offers no results to validate the claim.

Simulation

methodology

411 2 McBroom Transmission Model: Ricardo (2011) offers no discussion of how

managed during shifts. This may have greatest impact on the shtransmission shifts to put the engine at the best bsfc for the give

Simulation

methodology

412 2 McBroom Hybrid: I don’t see any effort to model motor/inverter temperatur

significant degradation of motor capability as things heat up duri

Simulation

methodology

413 2 McBroom Regen Alternator: Alternator model is too simplistic. On average

identified and it’s unrealistic to assume that the battery will be abcharge. 

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Simulation

methodology

414 2 McBroom EHVA: The paper addresses the potential of the technology nice

2003 has any more recent work been done to address the durabthe conclusions?

Simulation

methodology

415 2 McBroom Accessories: I don’t see any discussion on the treatment of acce

review of the previous material, that the authors assume that all think that engine driven accessories will play a key role in 2020.

Simulation 297 1 Midlam­ The vehicle model is reported as “a complete, physics­based ve

methodology Mohler model” ­ which it is not.  The modeling approach used relies hea

determined data which is decidedly not physics­based.  This nommodel is not described in sufficient detail in the report to make a

An excellent example of this is the electric traction drives and HE

which the report mentions no details, even qualitative ones, on t

Simulation 369 1 Midlam­ The vehicle simulator is used to generate several thousand simu

methodology Mohler technique.  This data is then fit with a neural­network­based resp

which the “goal was to achieve low residuals while not over­fittin

surface model then becomes the method from which vehicle des

estimated in the data analysis tool.  In this case, the response suthan a multi­dimensional black­box curve fit.  There was no errorregarding this crucial step.  By way of example, the vehicle simuperfect predictions of future vehicle performance; however, a bacorrupt all of the results. 

Simulation

methodology

370 1 MidlamMohler Provide error metrics for the neural network RSMs (i.e. R2, min a

error, error histograms, error standard deviation, etc.) before comdata sets. 

Simulation

methodology

371 1 MidlamMohler Provide the error metrics described above for the RSMs after co

data sets. 

Simulation

methodology

372 1 MidlamMohler Provide validation that the data analysis tool correctly uses the R

close to the source data (i.e. demonstrate the GUI software beha

30
 
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Simulation

methodology

3 1 Sawyer Ricardo (2011) simulated dynamic vehicle physical behavior usin

with 10 Hz time resolution. This software and the time resolutioncomputations to show the effect of component interactions on vetime resolution is sufficient to capture both driver behavior and vapplication of information technology, as is being implemented, a

for reducing fuel consumption become a future strategy, the moda suitable simulation.

Simulation

methodology

4 1 Sawyer Drivetrain synergistic effects seem to be predicted reasonably. T

calculation of fuel economy of the baseline vehicles and compartest data. The model does not seem to have the capability to capdrivetrain synergistic effects. Vehicle weight reductions associateimprovements are input rather than modeled internally. This is aSimilarly, from the Complex System Tool, weight reductions do nreduction in engine displacement.

Simulation

methodology

422 2 Sawyer Ricardo, Hybrid Battery Warm Up Model Validation – Update, Lig

Systems Simulation ,EPA Contract No. EP­W­07­064, work assiproprietary) This report presents a simple battery heat transfer mand compares with Argonne National Laboratory of the previousproduces adequate prediction of battery temperature.

Simulation

methodology

441 2 Sawyer Trapp, C., et al., Lean boost and NOx—strategies to control nitro

date), 23 p. Technical paper that describes lean burn direct injecNOx control, and more. Includes some emission control cost datComment: Not clear how this related to Ricardo’s model develop

Simulation

methodology

453 2 Sawyer Ricardo, Scaling Methodology Review, 19 Jan 10, 9 p. This docu

methodology used in the EASY5 vehicle model.Comment: This description in clear and useful. 

Simulation

methodology

458 2 Sawyer Takoaka, T., et al., Toyota, Super high efficient gasoline engine 

(no date), 16 p. This paper describes the hybrid system, IC engiincreased IC engine efficiency.Comment: Of general interest but application to the model not cl

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Simulation

methodology

198 1 Wade Concern:  Methodologies used in simulating the subsystems and

not provided, so that the validity and applicability of these methoassessed.

Results 5.2 Vehicle

Configuration andtechnologycombinations

105 1 McBroom Also there is no scientific or objective reason given for the DoE r

make any vehicle 60% less mass, 70% less rolling resistance ettowards that end of the DoE, when they may not be practically a

Results 6.1 Baseline

ConventionalVehicle Model

106 1 McBroom Results were compared to the EPA Vehicle Certification Databas

include correction factors and allowances that aren’t documenteRecommend that actual testing be run to perform the benchmark

Results 6.3.1 Engine

WarmupMethodology

107 1 McBroom Were there hot and cold engine maps? No mention.

Results 6.4 Transmission

Models

108 1 McBroom Fig 6.1 appears to be a comparison of desired cvt ratio vs desire

stated as such.  The shift logic controller should take into accounwhether or not the desired shift is achievable.

Results 6.5 torque

Converter models

112 1 McBroom The lockup strategy seems very conservative.  Large gains are a

sophisticated control and are in use today. 

Results 6.6 Final Drive

Model

114 1 McBroom Only discussed the baseline, what improvements for 2020 and w

criteria for the future vehicles was used?

Results 6.7 Driver Model 115 1 McBroom How was the soak modeled? Were there hot engine maps and cResults 7.1 Baseline

ConventionalVehicle Models

116 1 McBroom Better definition of what “acceptably close” means.  This doesn’t

objectivity. Something like, “the advisory committee determined to predict within x% to be usable for this study.”

Results 8.1 Evaluation of  

Design Space

118 1 McBroom Why was Latin hypercube sampling methodology picked over ot

While it’s attributes are mentioned, what other methods were co

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Results 8.2 RSM 119 1 McBroom A description of how the neural network is deployed is needed, o

discussed in this section.  What were the best fit criteria?  What neural net have to play with? Where are the fit’s published? How “one fit per transmission” was the best way to go?

Results 9.1 Basic Results 120 1 McBroom Why 10Hz sampling rate?  By what criteria was a run consideredResults 9.3 Exploration of 

the Design Space

121 1 McBroom If boundaries of acceptable performance were applied, a conside

runs could be eliminated.

Results Issue with CSM 218 1 Wade Issue:  The technology “package definitions” (page 22 and 23) p

the individual effects of a variety of technologies.  

Results Issue with CSM 219 1 Wade Some examples where the model did not allow a build up of com

­ Baseline engine with AT­2010 to AT­2020 to DCT­ Baseline engine without stop­start to with/stop­start 

Results Other issues 220 1 Wade The Advanced Diesel does not appear to be modeled for the St

(page 46 and 47), yet no reason was provided.

Results Other issues 221 1 Wade The P2 and PS hybrid system was not modeled for the LHDT (p

provided. 

Results Other issues 222 1 Wade When the baseline cases were run in the Complex Systems Mo

displacement and architecture were shown in the output.  o As an example shown on the attached chart (copied from the obaseline for the Standard Car with a 2.4L engine shows a displao For the same example, the architecture is shown as “conventiobaseline was understood to not have the stop­start feature (page

Results Overview of results 211 1 Wade The results from this work could be useful in evaluating possible

the 2020­2025 timeframe if the issues throughout this peer revierecommendations in Item 5 (below) were implemented.  Howevedeficiencies were resolved, the foregoing caveat that there are nhave potential for reduced GHG emissions that were not includerecognized (see Item 1B, above).

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Results Sample runs of 

CSM

212 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: Baseline enStoichiometric DI Turbo, Stop­Start, AT8­2020­38.7% improvem

et al. (2009) identified a 25­30% improvement in mpg for a 50% engine. The remaining 9­14% potentially could be explained by sfrom AT6­2010 to AT8­2020 (although the details of the systemsbe needed to make this assessment). 

Results Sample runs of 

CSM

213 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: AT8­2020 to­3.3% improvement in M­H mpg­ This improvement appears reasonable. 

Results Sample runs of

CSM

214 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: Stoichiometto P2 Hybrid­ 18.2% improvement in M­H mpg­ This improvement appears reasonable. 

Results Sample runs of 

CSM

215 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: Stoichiometto PS Hybrid­ 11.1% improvement in M­H mpg­ A detailed explanation of the differences in the improvements bhybrids should be provided in the report, especially considering tfuel economy and uses a 70% smaller electric motor (24 vs. 80 k

Results Sample runs of 

CSM

216 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: StoichiometNaturally Aspirated Atkinson CPS Hybrid­ Loss of 2.3% M­H mpg (From Stoichiometric DI Turbo PS Hybr­ The details of the Naturally Aspirated Atkinson CPS Hybrid sho

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the nearly equal fuel economy to the Stoichiometric DI Turbo PS 

Results Sample runs of 

CSM

217 1 Wade In the review process, several sample runs of the Complex Syste

Standard Car (Toyota Camry) were made and the results are shthe end of this peer review) and summarized below: StoichiometNaturally Aspirated Atkinson DVA Hybrid­ 2.1% M­H mpg improvement in M­H mpg (From Stoichiometric­ The details of the Naturally Aspirated Atkinson DVA Hybrid sho

the nearly equal fuel economy to the Stoichiometric DI Turbo PS 

Results Section 4.4.11

Lubrication

103 1 McBroom Assumes a sweeping improvement without identifying a clear rat

describe a scientific or objective approach. 

Results Section 4.4.6

Shifting ClutchTechnology

101 1 McBroom “The technology will be best suited to smaller vehicle segments 

drivability expectations” – not in the US market.

Results Section 4.4.7

ImprovedKinematic Design

102 1 McBroom Assumes a sweeping improvement without identifying a clear rat

describe a scientific or objective approach. 

Results Section 4.5.1

Intelligent CoolingSystem

104 1 McBroom The system as described seems more appropriate for regulated 

opportunity rather than fuel economy or GHG.  I think these  sysstrategies that aren’t part of this study that would have a greaterthan warming up the engine faster. 

Results 42 1 Assanis For the vehicle performance simulation results shown in Table 7

adjustable parameters used to fit these vehicles?

Results 43 1 Assanis Even though it appears that the validation results from the simula

agreement with the test data, there are up to 15% off. Even for tavailable, the error is on the order of 5%. These discrepancies ashould be taken into account when conclusions are drawn from tregulation is to be proposed based on these. 

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Results 44 1 Assanis There is also no baseline hybrid configuration and no validation 

the increased complexity of these vehicle systems, it is importanand assumptions are valid.

Results 45 1 Assanis It would be desirable to include a complete test case with the ap

and outputs as part of the report. The sample results presented included to indicate the RSM and Data Visualization Tool’s capaprovide a complete picture from which to draw solid conclusions

Results 46 1 Assanis The plots showing simulation results in blue, red, etc. could be b

could be inserted in the plots) and possibly presented in a relativimprovements over the baseline engine rather than absolute numpersonal choice for a more clear representation of the predicted stating that there is anything wrong with the current representatio

Results 109 1 McBroom What are the shift optimizer inputs? What are it’s basic decision Results 110 1 McBroom There is no discussion of engine downspeeding.Results 111 1 McBroom There is no discussion of gear ratio selection.Results 113 1 McBroom What was the basis for the minimum rpm’s for lockup sited? Sho

engine.  The controller should recognize when it needs to unlockengines ability to keep up.

Results 117 1 McBroom On the performance runs, a few tenths of a second represent me

engine torque for example. 

Results 416 2 McBroom Motor Efficiency Maps: I am having trouble believing that motor e

90% once temperature effects are accounted for. It also seems tdon’t include the inverter even though the authors say that it doemore reasonable.  As stated in a previous comment, I believe thneeded for motors will drop their efficiencies in the future.

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Results 417 2 McBroom After reading the papers and presentations I come to the assum

used to guide the selection of technology, but it’s not clear which model and which maps were generated in the test cell.  It’s evideconcentration on engine technology and the fidelity of the engineappropriate.  I have a slight concern about the impression I’m lefattention to the interaction of systems effects. This is most likely availability of data.  I would like to see the EPA articulate a proceinteractions, continuous improvement and model compatibility.  Fwere to run over several years the researches should feel confidgenerated with the models in 2013 to modeling results generated

Results 298 1 MidlamMohler The third charge questions deals with the validity and the applica

prediction.  The difficulty in this task is that it is an extrapolation fuses an extrapolation method (i.e. the model) and a set of inputspowertrain data.)  Since it is not possible to validate the results atechnology that do not exist, one can only ensure that the modelappropriate for the task.  Because of the lack of transparency in difficult to make any claims regarding the results.  In trying to vacited in the body of the report that shows the baseline engine geUS06 fuel economy than all of the other non­HEV powertrains wsame – this leaves some skepticism regarding the results. 

Results 373 1 MidlamMohler As outlined in the executive summary, it was not possible to ans

provided for this peer review due to lack of completeness in the aimed at providing feedback on what information would be helpfevaluate the spirit of the charge questions. With the above in minare made. 

Results 374 1 MidlamMohler The modeling approach describe in the report could be appropri

required and is generally consistent with approaches used by otconclusions from the report could very well be sound; however, tinformation and validation provided in the report to determine if ttechnique used to analyze the mass simulation runs could also baccuracy of the response surface model is not cited in the report

Results 375 1 MidlamMohler The process of arriving at the performance of the future technolo

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Results 376 1 MidlamMohler The majority of models are only described qualitatively making it

the soundness of the model.

Results 377 1 MidlamMohler Some of the qualitative descriptions of the models indicate that m

important factors. 

Results 378 1 MidlamMohler Because of the qualitative nature of the model descriptions, ther

transparency in the inputs and parameters in the models. 

Results 379 1 MidlamMohler Where precise value(s) are given for parameters in the model, th

cite the source of the value(s) or provide validation of the particu

Results 380 1 MidlamMohler Validation of the model and sub­models is not satisfactory (It is a

these technologies do not exist, but the parameters and structurbased on something.)

Results 5 1 Sawyer Performance calculations tied to the FTP, HWFET, and US06 te

capture vehicle behavior under real­world operation. Therefore, improving fuel economy under real­world operation are either exnot included. The application of a 20% reduction in fuel economyof the drive cycle for 2010 baseline vehicles and 10% for 2020­2treats only one of many problems with the driving simulation in thdifficulties carry over into the simulation of hybrid control strateg

Results 6 1 Sawyer It is conceivable that BEVs and PHEVs (and less likely FCEVS) 

the 2020­2025 vehicle fleet. That they are excluded from the mo

Results 446 2 Sawyer Lymburner, J.A., et al., Fuel consumption and NOx Trade­offs on

Gasoline Engine Equipped with a Lean NOx Trap, 4 Aug 09, 20 examines the trade­off between NOx control and CO2 emissionsComment: Good work but relevance not clear. 

Results 447 2 Sawyer Lotus(?), (from Kapus, P.E. et al., May 2007), Comparison to oth

one figure is a partial engine map with context vague. Comment

Completeness 4.4 Transmission

Technologies

136 1 McBroom What types of CVT’s were in the original mix? Toroidals, push­be

Completeness 4.4.1 Automatic

Transmission

138 1 McBroom No logical explanation for the 20­33% improvement...how was th

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Completeness 4.4.10 Super

Finishing

140 1 McBroom How much improvement is attributed to super finishing?

Completeness 4.4.3 Wet clutch 139 1 McBroom It said these were expected to be heavier, cost more and be less

where they included?

Completeness 4.5 Vehicle

Technologies

141 1 McBroom No values for mass, rolling resistance or drag given.  No discuss

possibilities.  This would be a good place to use historical trendsaero improvements and parasitic loss improvement.

Completeness 5.2 Vehicle

Configuration andtechnologycombinations

142 1 McBroom While the tables show the vehicle configurations, more discussio

criteria for each vehicle is warranted.  In some cases this discustechnology sections, but I don’t think it should go there. 

Completeness 6.8 Hybrid Models 145 1 McBroom Too much data is missing. What were the pack voltages? What w

technologies? Was there only one or more?  Other than improvefuture improvements were included, like improved power densityrange?  What was the control strategy for each type?

Completeness Section 2

Objectives

122 1 McBroom A discussion of appropriate/anticipated use of the results is requ

Completeness Section 3.3 Ground

Rules

123 1 McBroom How did the group arrive at the seven vehicles? While it show c

possible to see that there could be some overlap.  If one looks atransmissions packages available in these vehicles already you Reducing the number of vehicles might save on the number of ru

Completeness Section 3.3

TechnologySelection Process

124 1 McBroom Who is on the Advisory Committee? Is it independent? How did 

with the comprehensive list of potential technologies? (From thewas based on what models Ricardo (2011) had in their library. T

Completeness Section 4. 

TechnologyReview andSelection

127 1 McBroom Regarding qualitative evaluation of technology “Potential of the t

emissions on a tank to wheels basis”, since this was a qualitativebe better to include well to wheels. 

Completeness Section 4.1.2 DI 

Fuel Systems

131 1 McBroom No discussion of DI control strategy. How was it selected? Was 

of DI control or was it one size fits all?

Completeness Section 4.1.3

Boosting Systems

132 1 McBroom It says that other boosting systems were included in the study, b

discussed.

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Completeness Section 4.3 Hybrids 133 1 McBroom Don’t see any data on the battery technology, battery managem

No discussion of regen braking strategies. 

Completeness Section 4.3.1 Micro

Hybrids

134 1 McBroom It is implied that electrified accessories aren’t used in this configu

the case. 

Completeness Section 4.3.2 P2

Hybrid

135 1 McBroom No discussion of why DCT was only transmission used for P2 hy

AMT. 

Completeness Section 6 Vehicle

Models

143 1 McBroom No discussion of how driveline inertia is handled.  This is importa

models. 

Completeness Sections 4.1 and

4.2

130 1 McBroom There’s no descriptions of the models. There are only descriptio

their perceived benefits.  The reader has to assume that the samused to model each technology, but I know from personal experimost likely not the case.

Completeness 47 1 Assanis Some of the aspects lacking form the report have already been m

the relevant sections.

Completeness 48 1 Assanis In general, the report provides a fair description of the modeling 

are no equations, plots or maps showing any specific modeling ithe report vague. 

Completeness 49 1 Assanis It might be possible to shorten the descriptions related to the ind

implemented and their improvements and add more details on hPeople using this tool will most likely not use the brief descriptiontechnologies to draw conclusions and make decisions.

Completeness 50 1 Assanis The “Conclusions” section of the report should be renamed “Sum

present any actual conclusions based on the results, but it does project. 

Completeness 125 1 McBroom It said there was a comprehensive list of technologies that the gr

should be shown and a comment on why it wasn’t included.

Completeness 126 1 McBroom Why wasn’t HCCI technology considered? From the publications

candidate for production in the next 10 yrs. 

Completeness 128 1 McBroom Regarding “Current (2010) maturity of the technology”, how was 

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Completeness 129 1 McBroom Citations required for statement “ SI engine efficiency to approac

frame considered”  This represents relatively large gains in SI tehowever EU and Japanese engine companies are making big im

Completeness 137 1 McBroom No transmission data was shown. No mass, no inertia to efficienCompleteness 144 1 McBroom There are several types of rolling resistance models, what type wCompleteness 146 1 McBroom Load leveling the engine by charging the batteries has been sho

idea because the round trip efficiency hit is a killer. Should only bbelow a certain level.

Completeness 147 1 McBroom We’re left to assume that SOC leveling is accomplished, but the

Was an EPA/SAE method used.

Completeness 148 1 McBroom When it comes to GHG reductions why weren’t plug­in hybrids cCompleteness 418 2 McBroom Hybrid: Ricardo (2011) asserts that electric machine design activ

like concentrate around cost reductions; however I see machineto meet cost reductions. Therefore I think it premature to assumesame and cost will drop.

Completeness 299 1 MidlamMohler Based on the above, it is clear that this reviewer feels the report 

the entire process of modeling work from input selection to resulsubsystem that was documented at the level desired.  It is underthere are things of a proprietary nature that must be concealed. frontal area of the vehicle classes does not seem to be anywheranalysis tool.  This is one parameter amongst hundreds excludinmodels (i.e. equations or block diagrams), methods used to genon control laws, etc.  On the topic of proprietary data, there are msufficiently that can demonstrate a key point (i.e. simulation accuconfidentiality of data – this should not be a major barrier to provinner working of the simulator.

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Completeness 7 1 Sawyer The selection of drivetrain technologies (other than the electric s

comprehensive. The qualitative description of the drivetrain techclear, but quantitative performance data are missing. Transparenperformance data is entirely lacking. This includes engine perforstrategies, battery management in hybrids, and more. That muchthe companies that generated it and/or to Ricardo (2011) is a proas a regulatory tool. 

Completeness 8 1 Sawyer The assumptions are difficult to extract from the text.Completeness 427 2 Sawyer Ricardo, Assessment of Technology Options, Technologies relat

Nov 09, 17 p. Overview predicts continuation of low uptake in the markets. Review deals with various engine technologies to improimprovements <1­5%. Most promising is electric turbo­compounrecover exhaust thermal energy to produce electricity).Commentassessments seem reasonable. There is no analysis of integrati

Completeness 433 2 Sawyer Ricardo, Future Engine Friction Assessment—Response to Actio

#4, 18 Feb 11, 4 p. (proprietary) Projects continued reduction in 

Comment: Data provide confirm projection. 

Completeness 434 2 Sawyer Ricardo, Revised Follow­‐up Answers to 8 April 2010 Meeting w

10, 8 p. (proprietary) Presents fueling maps for several technoloComment: Adds to documentation of engine map data. 

Completeness 435 2 Sawyer Alger, T., Southwest Research Institute, Examples of HEDGE E

engine map for a 2.4 L I4 High­‐Efficiency Dilute Gasoline Engin

compares with TC GDI engine, diesel engine.Comment: Adds to documentation of engine map data. 

Completeness 436 2 Sawyer Ricardo, Hybrid Controls Peer Review, 18 Feb 10, 31 p. (proprie

Review of hybrid control technologies for various architectures. Rin cold weather. Comment: Thorough description of technologiescharacteristics. Battery discussion covers similar material to an e

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Completeness 438 2 Sawyer Ricardo, Simulation Input Data Review, 4 Feb 10, 14 p. (propriet

architectures with emphasis on machine­inverter combine efficiemaps. Comment: More data, seems reasonable. 

Completeness 442 2 Sawyer Trapp, C., et al., NOx emission control options for the Lean Boos 

(2 Feb 07), 34 p. Paper compares lean NOx trap and selective ctechnologies. Includes some engine map data for NOx emissionaftertreatment.Comment: Good academic paper with useful data. Not clear whaused. 

Completeness 443 2 Sawyer Trap, C., et al., NOx emission control options for the lean boost 

(2 Feb 07), 27 p. Paper review international emissions regulationComment: This paper contains some of the same information asSimulated date presented, again for SCR and LNT technologies

Completeness 448 2 Sawyer Turner, J.W.G., et al. (2009), Sabre: a cost­effective engine tech

efficiency, high performance and low CO2 emissions, Low CarboProceedings, 14 p. This paper describes a technology for reducidownsized engine. The Sabre engine is a collaboration betweenContinental Automotive Systems.Comment: Limited performance data provided. 

Completeness 450 2 Sawyer Ricardo, Report on light­duty vehicle technology package optimi

is a progress report on Ricardo’s modeling work for the EPA. A rhybrid technologies, transmission, and vehicle technologies are comprehensive list of near term technologies are included. The roptimization apparent is not included here. 

Completeness 452 2 Sawyer Ricardo, Response to questions regarding the generation of the 

efficiency simulation, 16 Feb 10, 10 p. (proprietary) Paper answeon how the diesel fuel maps were generated. Comment: This is relevant information and provides a convincingbasis for the diesel fuel maps.

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Completeness 454 2 Sawyer Ricardo, SCR as an Enabler for Low CO2 Gasoline Applications

presentation describes technology and implementation for exhauburn gasoline engines.Comment: Comprehensive discussion of technology, but if and hmodel not clear. 

Completeness 455 2 Sawyer Ricardo, Simulation Input Data Review, 18 Mar 10, 17 p. (proprie

the engine maps used in the model. Includes are examples of thmodifications associated with a range of technologies. Data appComment: This is the documentation that was missing in the earreasonable and is reassuring.

Completeness 457 2 Sawyer Shimizu, R., et al., Analysis of a Lean Burn Combustion Concep

13 p. A technical paper, this document describes early (1984) anburn engines. Comment: Interesting technical description but no clear if or howmodel. 

Completeness 461 2 Sawyer Kapus, P., Potential of VVA Systems for Improvement of CO2 P

Performance of Combustion Engines, 30 Nov 2006, 9 p. This is variable valve actuation approaches and performance effects. Comment: Useful general technical information. 

Completeness 223 1 Wade Concern:  This report has significant deficiencies in its descriptio

in the modeling work.  Many of these deficiencies have been prelisted here for completeness.

Completeness 224 1 Wade An overall schematic and description of the powertrain and vehi

associated subsystem models/maps were not provided.  Only vaincluded in the text of the report.

Completeness 225 1 Wade Technical descriptions of how the subsystems and vehicle mode

vehicles were developed were not provided.  

Completeness 226 1 Wade None of the overall or subsystem models/maps were provided fo

their adequacy are not possible.

Completeness 227 1 Wade Most importantly, only minimal descriptions were provided of ho

technology subsystem models/maps was developed.

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Completeness 228 1 Wade Descriptions of the algorithms used for engine control, transmiss

control, and accessory control were not provided.  

Completeness 229 1 Wade Descriptions of how synergistic effects were handled were not pCompleteness 230 1 Wade There are many engine technologies that have potential for redu

were not included in this study, such as:­Single stage turbochargBiofueled spark ignition and diesel engines­Natural gas fueled eengines­Charge depleting PHEV and EV

Recommendations Accessory load

assumptions

338 1 MidlamMohler Cite and/or validate the alternator efficiency values of 55% and 7

Recommendations Accessory load

assumptions

339 1 MidlamMohler Account for charge/discharge losses in the advanced alternator 

12V battery model used for the simulation. 

Recommendations Accessory load

assumptions

340 1 MidlamMohler Describe, cite, and validate the accessory fan model used in the

Recommendations Accessory load

assumptions

341 1 MidlamMohler Justify the use of a 200 Amp advanced alternator across all of th

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

240 1 Wade Recommendation:  Since the baseline vehicles modeled were 20

models/maps for the subsystems used in these vehicle models sreport before it is released. 

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

241 1 Wade Recommendation:  A baseline model of a hybrid vehicle should 

to 2010 EPA fuel economy test data for production hybrid vehicle

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Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

242 1 Wade Recommendation:  The detailed assumptions showing how the b

improved component efficiency, improved kinematic design, supdriveline lubricants were added to the transmission maps shouldbefore it is released. 

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

243 1 Wade Recommendation:  Subsystem models/map should be added to 

review conducted to assess their adequacy before this report is 

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

244 1 Wade Recommendation:  To establish the adequacy of the subsystem 

details should be provided.

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

245 1 Wade Recommendation:  Both mechanically driven and electrically driv

requirements should be clearly provided in the report.  

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Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

246 1 Wade Recommendation:  A default weight increase/decrease should b

If weight reductions are to be studied, then the user should havechange, with the appropriate weight reduction built into the modearbitrary slider for weight. 

Recommendations Additional

recommendationsshown in bold print throughout othersections of thisreport are repeatedbelow forcompleteness

247 1 Wade Recommendation: A closer examination of the reasons for the u

between the models and baseline vehicles’ fuel economy test dathat the models could be refined to provide better agreement. 

Recommendations Advanced

Valvetrains(Section 4.1.1)

319 1 MidlamMohler Describe how variable valve timing technologies were applied to

Recommendations Advanced

Valvetrains(Section 4.1.1)

320 1 MidlamMohler Describe the process of determining the extent of the efficiency 

Recommendations Advanced

Valvetrains(Section 4.1.1)

321 1 MidlamMohler Describe how optimal valve timing was determined  across the v

Recommendations Aftertreatment/ 

EmissionsSolutions

316 1 MidlamMohler Provide better evidence that powertrain packages have credible 

standards. 

Recommendations Aftertreatment/ 

EmissionsSolutions

317 1 MidlamMohler Provide evidence that fuel enrichment strategies are consistent 

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Recommendations Boosting System

(4.1.3 and 6.3)

327 1 MidlamMohler Describe the process of arriving at the boosted engine maps.

Recommendations Boosting System

(4.1.3 and 6.3)

328 1 MidlamMohler Describe how factors like knock are addressed in the creation of

Recommendations BSFC Map

Comparisons

396 2 MidlamMohler I reviewed this but do not have any substantive comments.  All o

pseudo­virtual engines with other pseudo­virtual engines.  A comexperimentally validated engine current engine would have beenwould allow one to see the magnitude of improvements that werengines and where on the map these improvements were made

Recommendations Direct Injection

Fuel Systems

323 1 MidlamMohler Cite sources of data used to predict DI performance.

Recommendations Direct Injection

Fuel Systems

324 1 MidlamMohler Describe how this data was used to develop the future engine pe

Recommendations Direct Injection

Fuel Systems

325 1 MidlamMohler Provide validation of modeling techniques used.

Recommendations Electric Traction

Components

353 1 MidlamMohler Describe the method used to model electric traction components

Recommendations Electric Traction

Components

354 1 MidlamMohler Provide validation/basis for the process used to generate future 

components. 

Recommendations Electric Traction

Components

355 1 MidlamMohler Describe the technique used to scale these components.

Recommendations Engine Downsizing 330 1 MidlamMohler Properly document the process of scaling engines.Recommendations Engine Downsizing 331 1 MidlamMohler Validate the process used to scale engines.Recommendations Engine Models 310 1 MidlamMohler Provide fuel and efficiency map data for all engines used in simuRecommendations Engine Models 311 1 MidlamMohler Describe what the “other inputs” are to the engine maps.

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Recommendations Engine Models 312 1 MidlamMohler Provide specific references of which published data was used to

future engines.  Some references are given, however, it is not clreferences are used. 

Recommendations Engine Models 313 1 MidlamMohler Wherever possible, provide validation against data on similar tecRecommendations Engine Models 314 1 MidlamMohler Describe in detail the approach used to “stack up” technologies fRecommendations Engine technology

selection

343 1 MidlamMohler Describe in greater detail the approach used to model technolog

vehicles.

Recommendations Engine technology

selection

344 1 MidlamMohler Provide some form of validation that this approach is justified.

Recommendations HEV Battery Model 357 1 MidlamMohler Describe the method used to model the HEV battery.Recommendations HEV Battery Model 358 1 MidlamMohler Provide validation/basis for the process used to generate future 

battery. 

Recommendations HEV Battery Model 359 1 MidlamMohler Describe the technique used to scale the HEV battery .Recommendations Hybrid Controls

Presentations

400 2 MidlamMohler Several hybrid controls presentations were provided, however, it

together what information superseded the other since they wereThere were several good slides showing dynamic programming scenarios, however, it is assumed that this was not used for the would be computationally impractical.  Thus, I expected to see soffline control results to the actual control used in the vehicle simnot found.  The major concern in this area is developing a controoptimal for a wide variety of hybrid architectures as well as archicomponent types and sizes.  Without further validation in this arehybrid results are valid since the control has such an important r

Recommendations Hybrid technology

selection

349 1 MidlamMohler Better describe the hybrid control strategy and validate against a

vehicle.

Recommendations Hybrid technology

selection

350 1 MidlamMohler Validate that the HEV control algorithm performs equally well on

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Recommendations Hybrid technology

selection

351 1 MidlamMohler Validate that other vehicle performance metrics, like emissions a

adversely impacted by an algorithm that focuses solely on fuel eof things will challenge to validate with this level of model, howevshould be made to these factors which are currently not address

Recommendations Overall

recommendations

232 1 Wade Overall Recommendation:  Provide all vehicle and powertrain mo

models/maps used in the analysis in the report so that they can 

Recommendations Overall

recommendations

233 1 Wade Overall Recommendation:  Expand the technology “package def

of the individual effects of a variety of technologies. 

Recommendations Specific

recommendationsfor improvements

234 1 Wade Provide an overall schematic and description of the powertrain a

a.  Show all of the subsystem models/maps used in the overall mb.  Show the format of the information in each of the subsystem subsystem model, output). 

Recommendations Specific

recommendationsfor improvements

235 1 Wade Provide technical descriptions of how the subsystems and vehic

baseline vehicles were developed. 

Recommendations Specific

recommendationsfor improvements

236 1 Wade Provide overall system and subsystem models/maps in the repo

Recommendations Specific

recommendationsfor improvements

237 1 Wade Provide detailed technical descriptions of how each of the advan

models/maps was developed.

Recommendations Specific

recommendationsfor improvements

238 1 Wade Provide descriptions of the algorithms used for engine control, tr

system control, and accessory control.  

Recommendations Specific

recommendationsfor improvements

239 1 Wade Provide detailed descriptions of how synergistic effects were han

Recommendations Transmissions 361 1 MidlamMohler Cite data sources used in modeling.

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Recommendations Transmissions 362 1 MidlamMohler Validate models wherever possible.Recommendations Transmissions 363 1 MidlamMohler Fully describe transmission models/maps and processes used toRecommendations Transmissions 364 1 MidlamMohler Fully describe clutch/torque converter models/maps and processRecommendations Transmissions 365 1 MidlamMohler Fully describe the process used to generate shift maps and the o

controller. 

Recommendations Transmissions 366 1 MidlamMohler Fully describe the lockup controller (i.e. how soon can it enter loRecommendations Transmissions 367 1 MidlamMohler Fully describe the process for modeling torque holes during shiftRecommendations Transmissions 368 1 MidlamMohler Fully describe the model used for the final drive (i.e. inputs/strucRecommendations Vehicle model

issues

304 1 MidlamMohler List the dynamic equation describing the longitudinal motion of th

Recommendations Vehicle model

issues

305 1 MidlamMohler List all parameters used for each vehicle class for simulation.

Recommendations Vehicle model

issues

381 2 MidlamMohler List the dynamic equation describing the longitudinal motion of th

a. NOT ADDRESSED IN SUPPLEMNTAL MATERIAL REVIEWE

Recommendations Vehicle model

issues

382 2 MidlamMohler List all parameters used for each vehicle class for simulation a. NOT ADDRESSED IN SUPPLEMNTAL MATERIAL REVIEWE

Recommendations Warm­Up

Methodology

333 1 MidlamMohler Cite sources of data for 10% and 20% factors applied to the cold

Recommendations Warm­Up

Methodology

334 1 MidlamMohler Cite and/or validate the modeling approach used.

Recommendations 51 1 Assanis Various suggestions have already been included in the relevant Recommendations 52 1 Assanis The authors should expand the modeling sections. In particular, 

references (where possible) and provide more detail when empi

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scaling laws are used.

Recommendations 53 1 Assanis Flexibility should be added to the models. Some engine technolo

phasing, HCCI and alternative fuels should be considered.

Recommendations 54 1 Assanis A self­contained study should be presented as a test case for th

conclusions can be drawn and the utility of the approach more e

Recommendations 149 1 McBroom Instead of using proprietary Ricardo (2011) data/models/control 

should be used.  

Recommendations 150 1 McBroom Without stating how this model is going to be used in the regulat

it is very difficult to assess its appropriateness.

Recommendations 151 1 McBroom Considerably more time in this effort is required up front in the re

of building consensus on data and models.  Because this is not impression that not much was done. 

Recommendations 152 1 McBroom Guidelines for appropriate use should be given.Recommendations 153 1 McBroom An uncertainty rating for each model/data set should be publishe

differences in the assumptions/extrapolation of future technologi

Recommendations 154 1 McBroom Should use coast down data for baseline vehicles to model paraRecommendations 155 1 McBroom In terms of acceptable use: rather that trying to use the model to

the envelope (or which technology is better), the tool could be usmaximum overlap. In other words, knowing that the same perforachievable using different technologies lends more confidence thTheoretically this number could be a calculated value generated

Recommendations 156 1 McBroom Recommend allowing “real world” drive cycles to assess the rob

be a user generated result from a composite of the data sets alre

Recommendations 157 1 McBroom Should define the process for data selection....eventually you’ll 

‘how do we get ‘x’ technology included for consideration in the st

Recommendations 158 1 McBroom Where lumped improvements are made, I recommend using hist

technology improvement curves. For example, the parasitic lossquantifiable. Vehicle mass reductions as well.

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Recommendations 300 1 MidlamMohler Given the low level of detail given in the report, it does seem tha

consistent with the goal of the work and what others in the field athe report is inadequate in nearly every respect at documenting parameters, modeling methodology, and the sources and technitechnology performance data.  Given the need for transparency feels that the detail in the report is wholly inadequate to documeorganization responsible for the modeling has expertise in this athat the methodology is sound, however, given just the informatisimply no way for an external reviewer to make this conclusion.

Recommendations 301 1 MidlamMohler Because of the lack of hard information to answer the charge qu

evolved mainly into a suggested list of details that should be brothe charge questions to be answered properly.  With this informaperson with expertise in the appropriate areas will be able comm

Recommendations 9 1 Sawyer The failure to model the drivetrain­weight interactions is a major 

should clearly state that vehicle weights are held constant (assuassumption). 

Recommendations 10 1 Sawyer There should be a table describing the baseline vehicles.Recommendations 11 1 Sawyer Summarizing assumptions in tabular form would be a great assisRecommendations 12 1 Sawyer The design space should be expanded to include performance p

power/weight or 0­60 times. 

Recommendations 423 2 Sawyer Ricardo, BSFC Map Commparisons, LBDI vs EGR Boost & DVA

Boost, Light Duty Vehicle Complex Systems Simulation, EPA Cowork assignment 2­2, 24 Feb 10, 20 p. (proprietary) Comparisonterms of maps of percent difference in bsfc in bmep vs rpm spacComment: Straight forward data analysis, presumably as requesunderstanding technology performance differences. 

Recommendations 426 2 Sawyer Ricardo, Response to EPA Questions on the Diesel Engine Fue

Graphs for Word Document, 16 Feb 10, 11 p. (proprietary) Docudiesel engine maps for MY2020+ vehicles.Comment: Anticipated technologies are listed but how the mapsdescribed. Maps seem reasonable. 

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Recommendations 431 2 Sawyer Odvarka, E., et al., Electgric motor­‐generator for a hybrid electr

Mechanics, 16, 131­‐139, 2009, 9 p. Describes electrical machin

vehicles. Includes efficiency maps for four technologies.Comment: Data are of general interest, but date from 2003. 

Recommendations 444 2 Sawyer Ricardo, Lean/Stoichiometric switching load for 2020 Hybrid Boo

Presents space velocity and fuel maps. Comment: Relevance not clear. 

Recommendations 445 2 Sawyer Ricardo, Proposed Lean/Stoichiometric switching load for hybrid 

p. Identifies proposed lean zone operating region on engine mapComment: relevance not clear.

Recommendations 460 2 Sawyer Ricardo, Transient Performance of Advanced Turbocharged Eng

(proprietary) This report reviews expected advances in boosting effects on vehicle performance.Comment: Interesting information but how it impacts model is no

Recommendations 231 1 Wade This report needs major enhancements to reach the stated goal 

transparent in the assumptions made and the methods of simularectify the deficiencies in these areas are provided in the previou

Executive

Summary

295 1 MidlamMohler For the purpose of describing the modeling approach used in the

performance of future technologies, the report reviewed is inadearea, the report lacks sufficient information to answer the chargereviewer.  It is entirely possible that the approach used is satisfapurpose.  However, given the information provided for the reviewreviewer to make any statement regarding the suitability of this a

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Executive

Summary

383 2 MidlamMohler The supplemental review material provided some answers to qu

general, did not provide the level of detail necessary to ensure aprocess.  The conclusion of this reviewer remains similar as on tthat there were no serious flaws found in the work, however, thethat it is not possible to accurately judge if the predictions made 

concern in this work is the lack of validation and/or citation of whcoming from.  There are numerous maps that are presented in thhowever, these maps had to have originated from some processand should be compared against some kind of validation.  Despiprovided, the work is generally that of a project team that is com

Executive

Summary

463 2 Sawyer Ricardo (2011) has provided material, which is stated to be the d

computer simulation. These data are consistent with the data exsimulation. It is impossible to establish a precise correspondencemodel. The performance data covered by the 44 separate documprovide additional assurance that the simulation is soundly baseThere is no reason to doubt either the integrity or capability of Rincorporation of appropriate data into their simulation model.

Other Comments Accessory Models 269 1 Wade None of the accessory models were not provided for review, so t

cannot be assessed.

Other Comments Accessory Models 270 1 Wade The accessory loads vs. engine speed for the conventional belt 

apparently removed from the engine when electric accessories wconventional accessory loads as well as the alternator loads/bataccessories were not provided.

Other Comments Accessory Models 271 1 Wade In contrast, as an example, PQA and Ricardo (2008) provided th

electric water pump and AC compressor drive efficiency.  Simila

models would be expected in this report. (See Exhibit 6)

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Other Comments Advanced

Valvetrains(Section 4.1.1)

56 1 Assanis The report states that advanced valvetrain systems improve fuel

emissions mainly by improving engine breathing. Other benefits engine downsizing and faster aftertreatment warm­up. Beyond imefficiency and reducing pumping losses, advanced valvetrains cavariation to increase fuel economy and avoid knock, alter the comodulating trapped residual, and enable cylinder deactivation toFrom the report, it is not clear which of the possible benefits of thpackages have been harnessed in each case. A more systematipackage combinations is warranted as several are synergistic bu

Other Comments Boosting System

(4.1.3 and 6.3)

57 1 Assanis A two­stage system is indeed promising for advanced turbocharg

should be made between series and sequential configurations. Amake it a series system (two­stage expansion and compression)(turbos activated at different rpm). Variable geometry or twin­scroptions for the low or high pressure stages, respectively. A two­slike this would take advantage of the lean SI exhaust enthalpy, reven aid pumping), avoid mechanical work penalties, improve enenable high dilution levels (if desired) and probably help keep in­below 12:1, since significant compression would be done beforebe driven through a low pressure loop (after the turbines) or an i(between the turbines). The resulting turbo lag will depend on thand the control logic used. Note that the assumption of a time costated in the report) to represent the expected delay may not ho

Other Comments Boosting Systems 272 1 Wade The report states that “various boosting approaches are possible

turbochargers, and electric motor­driven compressors and turbinelsewhere the report states “series­sequential turbochargers” wiStoichiometric DI Turbo engine (page 15). 

Other Comments Boosting Systems 273 1 Wade It is not clear in the report how the series­sequential turbocharge

variety of boosting devices that were introduced.  Models for thecompressor and turbine efficiency maps were not provided, so thmodel cannot be assessed.

Other Comments Boosting Systems 274 1 Wade Comment:  The model should include a single turbocharger syst

downsizing as advocated by the Sabre Engine (Coltman et al., 2a lower cost alternative to series­sequential turbochargers. 

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Other Comments Cooled Exhaust 

Manifold

284 1 Wade The Ricardo (2011) report states, “The future engine configuratio

cooled exhaust manifold to keep the turbine inlet temperature bewas provided of how the limit on turbine inlet temperature wouldpower. 

Other Comments Efficient 

Components(Section 4.4.9)

61 1 Assanis Efficient components should also include gears since rotating ge

of drag. Designing a better profile for gear teeth can reduce drag

Other Comments Engine Models 254 1 Wade Engine models provided the torque curve, fueling map and other

were not specified in the report) (page 25).  Since the report statand other engine model parameters used in the study were baseRicardo (2011) proprietary data (page 26), their adequacy and sassessed.

Other Comments Engine Models 255 1 Wade The report states that engines used in the model were develope

(page 14).  1. The first method assumed that “reported performaengines would closely resemble production engines of the 2020­second method began with current production engines and thenimprovements over the new 10­15 years that would lead to an aconfiguration for the 2020­2025 timeframe” was applied.Both of reasonable if: 1. appropriate references are provided, 2. the reporesearch engines used are documented in the report, 3. the techdocumented in the report, and 4. the methodology of incorporatidocumented. 

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Other Comments Engine Models 256 1 Wade The description of the derivation of the engine models in the rep

illustrated by the two examples below:Example 1:  Stoichiometric DI TurboThe current research engines of this configuration were reporteddeveloped by Lotus and the downsized concept engine developeengine modeled in the Ricardo (2011) report had a peak BMEP series­sequential turbochargers, the Sabre engine is not applicaBMEP of 20 bar and used a single stage turbocharger (Coltman 2009).   On the other hand, the Mahle engine appeared to be directly appBMEP of 30 bar and used series­sequential turbocharging (LumLumsden, et a. (2009) provided the BSFC map for this engine, swhy the Ricardo (2011) report could not have shown this map, oone, and then described how it was derived and/or combined witmodel used in the report. (See Exhibit 3)

Other Comments Engine Models 257 1 Wade The description of the derivation of the engine models in the rep

illustrated by the two examples below: Example 2:  Advanced DiFor the advanced diesel, the report provided the following descrtorque curve and fueling maps were generated by starting with afor this class and applying the benefits of improvements in pumpfueling map”.  No description of the improvements in pumping loprovided and the variation of these improvements over the speeprovided.  In addition, the baseline 6.6L engine map was not prowas not provided and the methodology for applying the improvemmap was not provided.

Other Comments Engine Models 258 1 Wade The report should explain whether the engine model is only a ma

load, or if the engine model includes details of the turbocharger, algorithms for parameters such as air/fuel ratio, spark/injection tipressure, and valve timing.

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Other Comments Engine Models 259 1 Wade Advanced valvetrains were included in many of the advanced en

the method for applying these advanced valvetrains to the enginAlso, no description of the control strategy for these valvetrains w

not provide a description of how the reduction of pumping losseswas applied to a downsized engine that already had reduced puassessment of how the model handled synergies could be made

Other Comments Engine Models 260 1 Wade In summary, the Ricardo (2011) report provided insufficient desc

the maps used for all of the engines in this study, which included­ Baseline­ Stoichiometric DI Turbo­ Lean­Stoichiometric Switching­ EGR DI Turbo­ Atkinson Cycle­ Advanced Diesel

Other Comments Engine Scaling 289 1 Wade The report states, “The BSFC of the scaled engine map is ...adj

accounts for the change in heat loss that comes with decreasingthereby increasing the surface to volume ratio for the cylinder” (pdirectionally correct correction.  However, specific values for theprovided, together with references to the data and methodology used. 

Other Comments Engine Scaling 290 1 Wade Issue:  The report states, “...downsizing the engine directly scale

(page 26).  However, since there will be increased heat loss fromcylinder, the torque would be expected to be less than the directfueling rate.  

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Other Comments Hybrid

TechnologiesModels

265 1 Wade Key elements of a hybrid system include:  electric machines (mo

electronics, and a high­voltage battery.  Only the following vague

for these subsystems was provided:  “For each of these systemstechnologies were adapted to an advanced 2020­2025 version o

lowering internal resistance in the battery pack to represent 2010development and decreasing losses in the electric machine and represent continued improvements in technology and implementdescription did not provide adequate details to assess the adequexample, specific values for internal resistance with references swith an illustration of how this was incorporated in the model of t

Other Comments Hybrid

TechnologiesModels

266 1 Wade In contrast, as an example, Staunton, et al. (2006) provided a de

shown below, as well as efficiency maps of other key componenSimilar maps for all hybrid subsystems would be expected in this

Other Comments Hybrid

TechnologiesModels

267 1 Wade In addition, “a Ricardo proprietary methodology was used to iden

consumption for a given hybrid powertrain configuration over the(page 29), which precluded an assessment of its suitability.

Other Comments Hybrid

TechnologiesModels

268 1 Wade No mention was provided of how the cooling system for the hybr

Other Comments LeanStoichiometric

Switching (Section4.2.2)

58 1 Assanis The mixed­mode operation considered in the report seems to sw

and lean SI direct injection operation. There are several multi­modevelopment that encompass several more combustion modes, Sparkassisted compression ignition with amounts of EGR dilutio

Other Comments LeanStoichiometric

Switching Engine

288 1 Wade The report states that this engine will use a lean NOx trap or a u

(page 15).  The use of fuel as a reducing agent was also suggesHowever, the fuel economy penalty associated with regeneratingreducing agent for the SCR system was not provided.

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Other Comments P2 Parallel Hybrid

(Section 4.3.2)

59 1 Assanis P2 refers to pre­transmission parallel hybrid, where an electric m

the engine and the transmission. While the report does not discupossible configurations: (i) a single clutch, located in between thmachine, such as in the Hyundai Sonata, and (ii) two clutches, oand the motor, and the other one in between the motor and the tInfiniti M35 HEV. The P2 system looks promising to achieve goobarriers include cost, drive quality, durability and to a lesser exteconsideration of details is needed to properly assess benefits copower split. Early reports have indicated that Nissan got 38% mpand Hyundai got 42%, both with higher horsepower, as well. Howdoesn't seem to meet EPA 2012 CAFE standards.

Other Comments Stoichiometric DI 

Turbo Engine

275 1 Wade The table below compares several attributes of the Ricardo Stoic

with the Mahle Turbocharged, DI Concept Engine. (See Exhibit 7

Other Comments Stoichiometric DI 

Turbo Engine

276 1 Wade Key content of the Mahle Turbocharged, DI Concept Engine:

­  Two turbochargers in series­  Charge air cooler­  Dual variable valve timing­  High energy ignition coils­  Fabricated, sodium cooled valves­  EGR cooler

Other Comments Stoichiometric DI 

Turbo Engine

277 1 Wade Lumsden, et al. (2009) describing the Mahle concept engine stat

consumption that usually occurs around 2000 rpm had moved osequential turbocharged engine. 

Other Comments Stoichiometric DI 

Turbo Engine

278 1 Wade Issue:  The Ricardo (2011) report did not discuss the concern th

consumption in a series­sequential turbocharged engine had mothan the usual 2000 rpm and did not discuss how this concern w

Other Comments Stoichiometric DI 

Turbo Engine

279 1 Wade The foregoing table indicates several significant issues: 1.  The t

the Mahle engine is significantly higher than the limit assumed fovs. 950C).  Reducing the turbine inlet temperature is expected towhere the temperature is limited. (see Exhibit 7)

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Other Comments Stoichiometric DI 

Turbo Engine

280 1 Wade The foregoing table indicates several significant issues: 2.  The t

for the Mahle engine is 2.5 seconds, whereas Ricardo (2011) asseconds.   Such turbocharger delays are expected to result in sigfor engines that are downsized approximately 50%. (see Exhibit 

Other Comments Stoichiometric DI 

Turbo Engine

281 1 Wade The table below compares several attributes of the Ricardo Stoic

with the Lotus Sabre Engine. (see Exhibit 8)

Other Comments Stoichiometric DI 

Turbo Engine

282 1 Wade The paper on the Sabre engine (Turner et al., 2009) indicates th

inlet temperatures results in a reduction in BMEP.  However, thethe Sabre engine is still 40C above Ricardo’s assumption.

Other Comments Stoichiometric DI 

Turbo Engine

283 1 Wade Turner et al. (2009) indicates that the Sabre engine with a single

an attractive alternative to extreme downsizing with series­seque

Other Comments Transmission

Models

261 1 Wade Similar to engine models, the description of the derivation of tran

vague.  Using the automatic transmission model as an example,timeframe, losses in automatic transmissions are expected to becurrent automatic transmissions from the specific technologies dspecific technologies that could provide these reductions appear­ Shift clutch technology ­ to improve thermal capacity of the shifcount and lower clutch losses during shifting.­ Improved kinematic design – no description of these improvem­Dry sump – to reduce windage and churning losses.­ Efficient components – improvements in seals, bearings and cl­ Super finishing ­ improvements expected were not specified.­Lubrication­ new developments in base oils and additive packagnot specified.

Other Comments Transmission

Models

262 1 Wade In addition to not specifying the improvements expected from the

indication was provided of how these technologies were applied For example,  ­The report stated that losses in automatic transmissions are explower than in current automatic transmissions (page 19).  Howevnot provided for reference and the means to achieve these redu­ The report stated that energy losses in DCTs are expected to bcurrent automatic transmissions (page 19).  The details of this reand references describing these reductions were not provided.

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­ Bearing and seal losses have a greater effect on efficiency at li

loads.  The report did not describe how these losses were incorpcontrast to the lack of descriptions of details in the report, PQA aexample, provided the following map of bearing losses in a transshaft diameter and speed.  Similar details for the relevant aspecin this report would have been expected. (See Exhibit 4)

Other Comments Transmission

Models

263 1 Wade In summary, the Ricardo (2011) report provided insufficient desc

the maps for the following transmissions:­ Advanced automatic­ DCT­ P2 Parallel hybrid transmission­ PS Power Split hybrid tran

Other Comments Transmission

Models

264 1 Wade In addition, the models for the automatic transmissions of the ba

provided, so that their adequacy could not be assessed.

Other Comments Transmission

Models (Section6.4)

62 1 Assanis It is claimed that gear selection will be optimized for fuel econom

road load. Can this also be adaptive? Engine performance degracould also lead to more gear shifts; the latter would increase hydpower losses in the clutch, thus eroding some of the possible fue

Other Comments Transmission

Technologies(Section 4.4)

60 1 Assanis What about automatic transmissions with automated clutch repla

and lock­up clutch? This is also a possibility.

Other Comments Warm­Up

Methodology

285 1 Wade “Ricardo used company proprietary data to develop an engine w

used to increase the fueling requirements during the cold start pcycle (page 26).  Since this data was proprietary, no assessmenbe made. 

Other Comments Warm­Up

Methodology

286 1 Wade Elsewhere the report states, “A bag 1 correction factor is applied

economy result of the vehicles to approximate warm­up conditioreduces the fuel economy results of the FTP75 bag 1 portion of the current baseline vehicles and 10% on 2020­2025 vehicles thwarm­up technologies” (page 29).  No references or data are citreduction in correction factor. 

Other Comments Warm­Up

Methodology

287 1 Wade Issue:  No explanation was provided to clarify when the “engine 

when the “correction factor” is used.  Therefore, the appropriatenmethodology cannot be assessed. 

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Other Comments 15 1 Assanis The report is intended to provide administrators, product planner

tool for assessing what is achievable, as well as insight into the cforward to reach those advances that will be useful for productivand the manufacturers. This path forward involves trade­offs aminvolving available, and soon­to­be­available advances in enginetransmissions and accessories. The current version of the simulareasonably balanced in the attention paid to each of these areasshown in the technologies considered and examples is encourag

Other Comments 16 1 Assanis Overall, the project attempts to undertake an analytical technolo

significant scope. It does a fairly competent job at analyzing a seand packages, mostly aimed at improving the gasoline IC enginediesel engine. It complements improvements on the engine sidedevelopments on the transmissions, hybrids and accessories. Thstudy is that the methodology relies extensively on proprietary aas empirical rules, correlations and modifiers without citing publieyond the perceived lack of transparency, keeping up with new twill necessarily involve new versions of the program since the actechnologies used are proprietary and the choice and range of pis fixed and to some extent hidden. Due to these constraints, theits ability to provide fundamental insight; this will require a more approach, perhaps best carried out by universities.

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Other Comments 17 1 Assanis For the most part, the right technologies are being considered. H

technologies and fuel options for IC engine technologies (other tcan make a significant contribution to the improvement of mpg aemissions have not been considered, or even mentioned at all. Padvanced combustion technologies, such as high pressure, dilutcombustion (e.g., Homogeneous Charge Compression Ignition, Compression Ignition, Spark­Assisted Compression Ignition), anpressure feedback. Some of these combustion technologies havfuel economy by up to 25%. Another significant assumption is thto either 87 octane pump gasoline or 40 cetane pump diesel. Hoparticularly from cellulosic or lingo­cellulosic bio­refinery processstandpoint of a life cycle analysis have strong potential for reduchave significantly different properties (including octane and cetacharacteristics than the current fuels. Note that over 13 billion gaused in 2010, primarily from corn­ethanol and some biodiesel. AFuel Standard, 36 billion gallons of renewables need to be used carried­out by Sandia and General Motors has shown that ninety(the energy equivalent of approximately 60 billion gallons of gasoUS by year 2030 under an aggressive biofuels deployment sche

Other Comments 18 1 Assanis The report is lengthy at places, for instance in the description of 

the simulation software are likely to be already familiar with, while.g. how the selected technologies were modeled in some detailbetter balancing of its sections. There should also be more wordresults (e.g., provide ranges of benefits), and assessing them crtechnologies seem to incrementally or additively contribute the mthat the results are in Table 7.1 or in Appendix 3. A discussion o

analysis should be presented so as to enable the reader to placeperspective.  

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Other Comments 19 1 Assanis The characterization of the modeling methodology as objective a

the simulation is composed of rigorous, first­principle expressionwithout using “correlations”, “empirical formulas”, and “phenomeconditions truly met? For instance, in many cases, steady­state dof an engine map featuring a certain technology. In other cases,based onempirical/proprietary factors and modifiers. The report sstudy as “scientific” unless data uncertainty is discussed and shoFor example, Table 7.1 presents comparisons between simulateeconomy performance. Given the various subjective assumptionthe authors should comment whether the noticeable differences significant. 

Other Comments 55 1 Assanis It would be desirable to show the analysis used to convert fuel c

vehicle greenhouse gas (GHG) emissions equivalent output. UltGHG savings resulting from the combined production and use cyoptions for combustion engines.

Other Comments 159 1 McBroom Having conducted a similar effort for USCAR on the PNGV prog

considerable effort is required to develop such a model. I don’t wwork that was done, by only offering criticism in the above sectioof the approach to this activity is in the right place, just better doappropriate use guidelines.

Other Comments 13 1 Sawyer The conclusions, Section 11, are a reasonable summary of the wOther Comments 14 1 Sawyer Including the membership of the advisory committee would be aOther Comments 429 2 Sawyer Ricardo, Hybrid Controls Follow­up, 10 Sep 11, 3 p. (proprietary

motor/general efficiency map used for 2020 technology. Projectebut most P2 hybrid application if below 90% efficiency.Comment: I am not qualified to assess if the projected motor/genappropriate for 2020­2025 as reported, but they seem low for 15

Other Comments 456 2 Sawyer Ricardo, Assessment of Technology Options, 19 Nov 09, 22 p. (

reviews and rates a range of spark­ignition adaptable technologiBiofuels are included.Comment: An interesting compendium but some previously repo

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Other Comments 248 1 Wade The vehicle model and powertrain model were developed and im

(2011) in the MSC.Easy5 software package.  The model reacts ttorque levels and wheel speeds required to drive a specified vehcycles.  The overall model consists of subsystem models that deoutputs such as torque, speeds, heat rejection, and efficiencies. expected to be required for the engine, accessories, transmissioincluded), final drive, tires and vehicle, although the report did noindividual subsystem models used. 

Other Comments 249 1 Wade A design of experiments (DOE) matrix was constructed and the 

generate selected performance, fuel economy and GHG emissiospace of the DOE matrix.  Response surface modeling (RSM) wneural networks.  The output from each model simulation run waoutput factors used in the fit of the RSM.  The resulting Complexprovides a useful tool for viewing the results from this analysis thindividual vehicle simulation cases. 

Other Comments 250 1 Wade The vehicle and powertrain models/maps and subsystem model

were not provided in the report and could not be reviewed.  In mthat the models/maps were either proprietary to Ricardo (2011) oproprietary so that they could not be provided for review.  Withouand subsystem models/maps, their adequacy and suitability can

Other Coments 251 1 Wade Overall Recommendation:  Provide all vehicle and powertrain mo

models/maps used in the analysis in the report so that they can 

Other Comments 252 1 Wade The technology “package definitions” preclude an examination o

variety of technologies.  For example, for the Stoichiometric DI Tversion with a series­sequential turbocharger could be evaluatedalternative with a single turbocharger could not be evaluated.  Litransmission could be evaluated with the Stoichiometric DI Turbo substitution of the AT6­2010, as a lower cost alternative, could n

Other Comments 253 1 Wade Overall Recommendation:  Expand the technology “package def

of the individual effects of a variety of technologies.

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Other Comments 291 1 Wade Sample Output From Complex System Model (CSM)

5/4/2011Relative Percentage Differences Were Added by W. R. Wade (s

68
 
 

 

 

 

 

 

 

 

 

Verbatim Peer Reviewer Comments in Response to
 

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The peer reviewer Dr. Wade included the following ten exhibits in comments. These are cited in the table 

of verbatim comments. 

Exhibit 1 

Parameter DoE Range (%) 

Engine Displacement 50 125 

Final Drive Ratio 75 125 

Rolling Resistance 70 100 

Aerodynamic Drag 70 100 

Mass 60 120 

Exhibit 2
 

DoE Range (%) 

Parameter P2 Hybrid Powersplit 

Engine Displacement 50 150 50 125 

Final Drive Ratio 75 125 75 125 

Rolling Resistance 70 100 70 100 

Aerodynamic Drag 70 100 70 100 

Mass 60 120 60 120 

Electric Machine Size 50 300 50 150 

Exhibit 3
 

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Exhibit 4
 

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Exhibit 5
 

Exhibit 6
 

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Verbatim Peer Reviewer Comments in Response to
 

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Exhibit 7
 

Feature 

Ricardo 

Stoichiometric DI 

Turbo Engine 

Mahle 

Turbocharged, DI 

Concept Engine 

SAE 2009-01-1503 

Downsizing 57% (for Std Car) 50% 

BMEP 25-30 bar 30 bar 

Turbo Response 1.5 second time 

constant 

2.5 second time 

constant 

(estimated from 4 

second total response 

time) 

Turbine Inlet 

Temperature 

950C 1025C 

NEDC fuel economy Not available 25 – 30% better that 

NA baseline 

Exhibit 8
 

Feature 

Ricardo Stoichiometric 

DI Turbo Engine 

Lotus Sabre Engine 

SAE 2008-01-0138 

Downsizing 57% (for Std Car) 32% 

BMEP 25 - 30 bar 20.1 bar 

Turbine Inlet 

Temperature 

950C 980C 

1050C (common) and 

desired 

Fuel RON 87 PON 

(Pump Octane Number) 

95 RON 

Est 91 PON 

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Verbatim Peer Reviewer Comments in Response to
 

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Exhibit 9
 

FTP HWFET US06 Combined 0­60

mph Displacement FDR RollingR. Aero Weight Eng. Eff Hybird Class

Conventional SS

Base

(Baseline)

30.0

­

43.5

­

29.1

­

34.9

­

8.3

­

1.04 3.23 0.00822 0.69 3625 1 Standard

Car

(Toyota

Camry)

Stoich DI Turbo 44.5

48.2%

54.2

246%

32.5

11.7%

48.4

38.7%

8.5 1.04 3.23 0.00822 0.69 3625 1 Standard

Car

(Toyota

Camry)

AT8­2020 to DCT 46.3

4.21%

55.3

1.93%

33.7

3.51%

50.0

3.28%

8.6 1.04 3.23 0.00822 0.69 3625 1 Standard

Car

(Toyota

Camry)

HYBRIDS

P2 w/Stoich DI Turbo

(Rel to Conv SS SCT)

61.6

32.96%

56.3

1.80%

36.6

8.89%

59.1

18.23%

8.6 0.83 3.23 0.00822 0.69 3625 1 24 Standard

Car

(Toyota

Camry)

PS w/Stoich DI Turbo

(Rel to Conv SS

DCT)

57.5

24.00%

53.3%

­3.50%

36.4

8.24%

55.5

11.11%

9.2 0.83 3.23 0.00822 0.69 3625 1 80 Standard

Car

(Toyota

Camry)

PS w/Akins on CPS

(Rel to Stoich DI

Turbo)

55.1

­4.08%

53.2

­0.18%

38.1

4.61%

54.3

­2.29%

8.5 2.4 3.23 0.00822 0.69 3625 1 80 Standard

Car

(Toyota

Camry)

PS w/Akins on DVA

(Rel to Stoich DI

Turbo)

58.3

1.5%

54.8

2.7%

38.7

6.1%

56.7

2.1%

8.5 2.4 3.23 0.00822 0.69 3625 1 80

Exhibit 10
 

Engines FTP HWFET US06 

Baseline 42.1 62.6 37.0 

Stoich_DI_Turbo 46.3 55.3 33.7 

Lean_DI_Turbo 48.3 56.4 33.9 

EGR_DI_Turbo 48.2 57.6 35.2 

Atkinson_CPS 44.5 59.0 35.4 

Atkinson_DVA 45.5 57.1 34.5 

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References
 

4. References
 

Coltman, D., J.W.G. Turner, R. Curtis, D. Blake, B. Holland, R.J. Pearson, A. Arden, and H. Nuglisch, 

2008, Project Sabre: A close-spaced direct injection 3-cylinder engine with synergistic 

technologies to achieve low CO2 output. SAE Paper 2008-01-0138. 

Hellenbroich, G., and V. Rosenburg, 2009, FEV's new parallel hybrid transmission with single dry clutch 

and electric torque support. Aachener Koolquium Fahrzeug- und Motorentechnik 2009 18:1209– 

1222. 

Lumsden, G., D. OudeNijeweme, N. Fraser, and H. Blaxill, 2009, Development of a turbocharged direct 

injection downsizing demonstrator engine. SAE Paper 2009-01-1503. 

PQA and Ricardo, 2008, A Study of potential effectiveness of carbon dioxide reducing vehicle 

technologies. Prepared for the U.S. Environmental Protection Agency, 

Ricardo, Inc., 2011, Computer simulation of light-duty vehicle technologies for greenhouse gas emission 

reduction in the 2020-2025 timeframe. Prepared for the U.S. Environmental Protection Agency. 

April 6, 2011. 

Staunton, R.H., C.W. Ayers, L.D. Marlino, J.N. Chiasson, T.A., Burress, 2006, Evaluation of 2004 

Toyota Prius hybrid electric drive system. ORNL technical report TM-2006/423. 

Turner, J.W.G., R.J. Pearson, R. Curtis, and B. Holland, 2009, Sabre: A cost-effective engine technology 

combination for high efficiency, high performance and low CO2 emissions. Low Carbon 

Vehicles 2009: Institution of Mechanical Engineers (IMechE) conference proceedings. 

U.S. Environmental Protection Agency (U.S. EPA), 2006, Peer Review Handbook, 3rd ed. Science Policy 

Council. EPA/100/B-06/002. 

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Appendix A. Charge to Peer Reviewers
 

Appendix A. Charge to Peer Reviewers

Charge to the Peer Reviewers of Ricardo’s “Computer Simulation of Light-Duty Vehicle
 

Technologies for Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe” Report
 

Charge to Peer Reviewers of "COMPUTER SIMULATION OF LIGHT-DUTY VEHICLE 

TECHNOLOGIES FOR GREENHOUSE GAS EMISSION REDUCTION IN THE 2020–2025 

TIMEFRAME" 

As EPA and NHTSA develop programs to reduce greenhouse gas (GHG) emissions and increase 

fuel economy of light-duty highway vehicles, there is a need to evaluate the effectiveness of technologies 

necessary to bring about such improvements. Some potential technology paths that manufacturers might 

pursue to meet future standards may include advanced engines, hybrid electric systems, mass reduction, 

along with additional road load reductions and accessory improvements. 

Ricardo Inc. has developed simulation models including many of these technologies with the 

inputs, modeling techniques, and results described in the Ricardo Inc. document "COMPUTER 

SIMULATION OF LIGHT-DUTY VEHICLE TECHNOLOGIES FOR GREENHOUSE GAS 

EMISSION REDUCTION IN THE 2020–2025 TIMEFRAME" 

EPA is seeking the reviewers' expert opinion on the inputs, methodologies, and results described 

in this document and their applicability in the 2020-2025 timeframe. The Ricardo Inc. report is provided 

for review. We ask that each reviewer comment on all aspects of the Ricardo Inc. report. Findings of this 

peer review may be used toward validation and improvement of the report and to inform EPA and 

NHTSA staff on potential use of the report for predicting the effectiveness of these technologies. No 

independent data analysis will be required for this review. 

Reviewers are asked to orient their comments toward the five (5) general areas listed below. 

Reviewers are expected to identify additional topics or depart from these general areas as necessary to 

best apply their particular set of expertise toward review of the report. 

(1) Inputs and Parameters. Please comment on the adequacy of numerical inputs to the model as 

represented by default values, fixed values, and user-specifiable parameters. Examples might include: 

engine technology selection, battery SOC swing, accessory load assumptions, etc.) Please comment on 

any caveats or limitations that these inputs and parameters would affect the final results. 

(2) Simulation methodology. Please comment on the validity and applicability of the 

methodologies used in simulating these technologies with respect to the entire vehicle. Please comment 

on any apparent unstated or implicit assumptions and related caveats or limitations. Does the model 

handle synergistic affects of applying various technologies together? 

(3) Results. Please comment on the validity and applicability of the results to the light-duty 

vehicle fleet in the 2020-2025 timeframe. Please comment on any apparent unstated or implicit 

assumptions that may affect the results, and on any related caveats or limitations. 

A-1
 
 

 

 

 

 

 

 

 

 

Appendix A. Charge to Peer Reviewers
 

(4) Completeness. Please comment on whether the report adequately describes the entire process 

used in the modeling work from input selection to results. 

(5) Recommendations. Please comment on the overall adequacy of the report for predicting the 

effectiveness of these technologies, and on any improvements that might reasonably be adopted by the 

authors for improvement. Please note that the authors intend the report to be open to the community and 

transparent in the assumptions made and the methods of simulation. Therefore recommendations for 

clearly defined improvements that would utilize publicly available information would be preferred over 

those that would make use of proprietary information. 

Comments should be sufficiently clear and detailed to allow readers familiar with the report to 

thoroughly understand their relevance to the material provided for review. EPA requests that the 

reviewers not release the peer review materials or their comments until Ricardo Inc. makes its report and 

supporting documentation public. EPA will notify the reviewers when this occurs. 

If a reviewer has questions about what is required in order to complete this review or needs 

additional background material, please contact Susan Blaine at ICF International (SBlaine@icfi.com or 

703-225-2471). If a reviewer has any questions about the EPA peer review process itself, please contact 

Ms. Ruth Schenk in EPA’s Quality Office, National Vehicle and Fuel Emissions Laboratory by phone 

(734-214-4017) or through e-mail (schenk.ruth@epa.gov). 

A-2
 
Appendix B. Reviewer Resumes
 

C-1 
 

 

 

 

 

 

 

 

 

 

 

 

DIONISSIOS (DENNIS) N. ASSANIS
 

PERSONAL 

Degrees 

Ph.D., Power and Propulsion, Massachusetts Institute of Technology (M.I.T.), 1985 

M.S., Management, Sloan School of Management, M.I.T., 1986 

M.S., Mechanical Engineering, M.I.T., 1982 

M.S., Naval Architecture and Marine Engineering, M.I.T., 1982 

B.Sc., Marine Engineering, Newcastle University, England, 1980 

Positions at University of Michigan 

Director, Michigan Memorial Phoenix Energy Institute, July 2009-date 

Jon R. and Beverly S. Holt Professor of Engineering 

Arthur F. Thurnau Professor of the University of Michigan 

Chair, Mechanical Engineering, Jan. 2002- Aug. 2007 

Professor of Mechanical Engineering, Sept. 1994-date 

Professor of Applied Physics, 2003-date 

Founding Director for the United States, Clean Vehicle Consortium, U.S.-China 

Clean Energy Research Center, 2010-2015 

Director, Automotive Research Center, Sept. 2000- Oct.2009 

Director, W. E. Lay Automotive Laboratory, 1996-date 

Fellow, Michigan Memorial Phoenix Energy Institute, 2007-date 

Founding Co-Director, General Motors Collaborative Research Laboratory on 

Engine Systems Research, 2002-2011 

Associate Director, General Motors Satellite Research Laboratory, 1998-2002 

Deputy Director, Automotive Research Center, Jan. 1996-Aug. 2000 

Acting Director, Automotive Research Center, Aug.1995- Dec. 1995 

Interim Director, CoE Interdisciplinary Professional Programs, Fall 2001 

Founding Director, CoE Automotive Engineering Program, Sept. 1999-Apr. 2002 

Founding Director, MEAM Automotive Engineering Program, 1995-1999 

Positions at University of Illinois in Urbana-Champaign 

Associate Professor of Mechanical Engineering, Aug. 1990 - Aug. 1994 

Head, Thermal Sciences/Systems Division II, Aug. 1992 - Aug. 1994 

Research Scientist, Office for Supercomputing Applications, Aug. 1991- 1994 

Assistant Professor of Mechanical Engineering, Sept. 1985 - Aug. 1990 

Positions at Other Institutions 

Honorary President, Zhejiang Automotive Engineering Institute, 2009-date 

Honorary Professor, Zhejiang Automotive Engineering Institute, 2009-date 

Advisory Professor, Shanghai Jiao Tong University, Shanghai, China, 2009-date 

Guest Professor, Shanghai Jiao Tong University, Shanghai, China, 2003-2008 

Assanis, 1 
Assanis, 2 

Adjunct Research Scientist, Argonne National Laboratory, Energy and 

Environmental Systems Division, May 1987-2002 

Research Assistant, Sloan Automotive Laboratory, Massachusetts Institute of 

Technology, Sept. 1982- Aug. 1985 

Teaching and Research Assistant, Department of Ocean Engineering, Massachusetts 

Institute of Technology, Sept. 1980-June 1982 

Honors and Awards 

ASEE Mechanical Engineering Division Ralph Coats Roe Award, 

2011 

College of Engineering, Stephen S. Atwood Award, 2011 

University of Michigan Rackham Distinguished Faculty Achievement 

Award, 2009 

Member, National Academy of Engineering, 2008 

ASME, Internal Combustion Engine Award, 2008 

ASME Fellow, 2008 

Tau Beta Pi Professor of the Year Award, 2006 

SAE Award for Research on Automotive Lubricants, 2002 

SAE Fellow, 2001 

Jon R. and Beverly S. Holt Professor of Engineering, 2000 

ASEE Annual Distinguisher Lecturer, College of Engineering, The 

University of Michigan, April 12, 2000 

Teaching Excellence Award, College of Engineering, The University 

of Michigan, 2000 

Arthur F. Thurnau Professor, The University of Michigan, 1999 

Excellence in Teaching Award, Mechanical Engineering and Applied 

Mechanics, The University of Michigan, 1998 

ASME Internal Combustion Engine Division Meritorious Service 

Award, 1997 

ASME Internal Combustion Engine Division Speaker Award, 1993, 

ASME Internal Combustion Engine Division Speaker Award, 1994 

Listed in Who's Who in America, 1994-date 

Listed in Who's Who in Science and Engineering, 1993-date 

Listed in American Men and Women of Science, 1992-date 

University of Illinois Scholar, 1991 - 94 

SAE Russell Springer Award, 1991 

IBM Research Award, 1991 
Assanis, 3 

ASME/Pi Tau Sigma Gold Medal Award, 1990 

NSF Presidential Young Investigator Award, 1988-93 

Lilly Endowment Teaching Fellow Award, 1988 

NSF Engineering Initiation Award, 1987 

NASA Certificate of Recognition for Creative Development of a 

Technical Innovation, 1987 

SAE Ralph Teetor Award to Outstanding Young Educators, 1987 

Excellent Teacher, listed every semester in student newspaper 

The Daily Illini, 1985-94 

Honors, B.Sc. Degree with Distinction, 1980 

CONTRIBUTIONS TO ACADEMIC LEADERSHIP AND SERVICE 

Contributions as Director, Michigan Memorial Phoenix Energy Institute 

As the Director of the Michigan Memorial Phoenix Energy Institute (MMPEI), 

Professor Assanis leads an organization that manages the development, coordination 

and promotion of multidisciplinary energy research and education programs across 

the University of Michigan (UM). MMPEI’s mission is to chart pathways to a secure, 

affordable and sustainable energy future. His current priorities include the 

following: 

• Develop the vision for integrated research thrusts on energy generation, storage, 

and utilization, and their interconnection with policy, economics, and social 

impact. Among major efforts, sustainable carbon-neutral transportation has 

emerged as a powerful research theme for UM that closely couples to the broad 

sustainability issues and integrated assessments. Electrification of transport, 

advanced energy storage in batteries and renewable fuels, as well as grid supply 

and distribution are of crucial importance to maintain UM’s status of being a 

world-leader in automotive and manufacturing engineering. In the area of carbonneutral electricity, MMPEI is bringing into a common energy systems focus the 

campus-wide efforts in the areas of nuclear engineering, solar energy, wind, and 

wave energy. MMPEI is committed to fostering changes that would facilitate the 

permitting, leasing, construction, and monitoring of renewable energy projects 

while protecting natural resources. 

• Establish new faculty appointments that combine strengths in science/technology 

with those in public policy, business, economics and social sciences. Examples of 

multi-disciplinary cluster hires that MMPEI is leading include energy economics, 

political science and public policy, energy storage, sustainable energy and climate 

change impacts. These new searches involve multiple Departments from the 

College of Engineering, the College of Literature, Science and Arts, the Ford 

School of Public Policy, and the School of Natural Resources and Environment. 
Assanis, 4 

• Spearhead the development of innovative and transformative energy literacy 

across various media including curricular offerings, workshops, lecture series, and 

seminars. Catalyze cross-disciplinary educational programs in sustainable energy 

across the UM campus and in collaboration with global partners. Enhance the 

integration of energy education and research. 

• Develop partnerships with other academic institutions, national laboratories, 

industry, start-ups, venture capitalists, and economic development agencies to 

promote scientific discovery and its translation to innovation and job creation. As 

an exemplar, UM is proud to be among the founding members of the Oak Ridge 

National Lab-led partnership that has won the first, highly competitive DOE 

energy innovation hub for “Advanced Simulation of Light Water Nuclear 

Reactors” funded with $122M for five years. MMPEI played a significant role in 

institutionalizing this strategic partnership which positions UM to attack largescale problems though the establishment of a discovery innovation network. 

• Develop strong international partnerships with first-class peer institutions with the 

strategic objective of tackling global energy and sustainability problems through 

education, research, industry transformation and innovative policies. For 

instance, MMPEI has significantly contributed to the expansion of the UMShanghai Jiao Tong University educational collaboration to encompass joint 

research in renewable energy. With Tsinghua University and other Chinese and 

US partners in academia, industry and national labs, we have recently won the 

competition for establishing the highly visible U.S.-China Clean Energy Research 

Center on Clean Vehicles funded with over $50M for the next five years. With the 

Fraunhofer Institutes of Germany, we have initiated a landmark international 

collaboration aimed at the transformation of the transportation industry towards 

electrical mobility. With the National University of Singapore, UM is proposing 

the joint development of renewable energy technologies and policies for highdensity urban communities that will be demonstrated in Singapore, an ideal test 

bed for sustainability. 

• Under Dr. Assanis’ leadership, MMPEI is pursuing a two-pronged approach for 

the development of comprehensive building facilities for the Institute. First, the 

UM Regents are funding a $11M renovation and expansion of the Phoenix 

Memorial Laboratory to provide state-of-the-art space for energy research, as well 

as the home for MMPEI’s administrative and collaborative functions. In parallel, 

MMPEI is developing a staged plan for the establishment of a multi-disciplinary 

hub for innovation and entrepreneurship in renewable energy, in partnership with 

other UM Centers, at the UM North Campus Research Complex.

Contributions as Chair of Mechanical Engineering 

As Chair of the Department of Mechanical Engineering (ME) at the University of 

Michigan (2002-2007), Professor Assanis led the administration and long-range 

development of the ME Department’s academic and research programs. The ME 

Department is a major academic unit that is educating more than 700 undergraduate 

students and 500 graduate students (250 Master’s and 250 PhDs), and employing 55 

tenured and tenure track professorial faculty members, 18 primary research scientists 
Assanis, 5 

and 70 support staff members in a physical plant of approx. 120,000 square feet spread 

out over four buildings. Throughout his tenure as ME Chair, the Department’s 

undergraduate and graduate programs were consistently ranked within the top five 

nationally by U.S. News and World Report. Also, based on data from the 2005-06 

academic year, the National Research Council rated the ME graduate program as #4 in 

the country based on both regression and survey rankings. His efforts have made 

significant contributions in the following areas: 

• Planned strategically to establish and articulate a shared vision for the future that 

sustains and evolves the ME Department’s core academic and research strengths in 

automotive and manufacturing engineering, while also developing a competitive 

position into the emerging areas of mechanical engineering, including bio-systems, 

energy/ eco-systems and micro/nano-systems. As the culmination of these strategic 

planning efforts, a major addition and remodeling of the ME Building facilities, has 

emerged as the #2 all-campus building priority for UM’s capital outlay plan over the 

next five years.

• Successfully retained the ME Department’s excellent body of faculty and hired 

outstanding new faculty (11 new Professors and 15 Research Scientists). Promoted 

in rank 27 faculty members, including 5 women faculty who reached the rank of 

Professor. In addition to assessing and rewarding the performance of professorial 

faculty, implemented procedures for the annual review and merit raises of primary 

research faculty. Mentored junior faculty members in their professional careers and 

made a deliberate effort to address issues that could compromise their success. 

Nominated a number of colleagues, students, alumni and staff who received 

prestigious professional awards, both outside and within the University, including 

four new endowed chairs.

• Enhanced the ME Department’s efforts to create a multi-cultural and diverse 

intellectual environment by retaining all women and underrepresented minority 

(URM) faculty; by hiring thee more women faculty members for a total of 10 (18% 

of ME faculty); by strategically recruiting URM and women students through K-12 

programs, the Detroit Area Pre-College Engineering Program, and the NSF Research 

Experience for Undergraduates Program; and by supporting mentorship groups 

including Unified Minority Mechanical Engineers and Society of Women Engineers. 

Improved communications among the students, alumni, faculty and staff. 

• Oversaw financial planning, budgets and expenditures for the ME Department 

(annual budget of approx. $14M in general funds and more than $28M in research 

funds and gifts) and introduced “paperless” electronic tools in the areas of student 

services, financial reporting, and faculty recruiting. Participated in fundraising and 

pubic relations efforts for the ME Department and College of Engineering in close 

coordination with the development staff. Through these efforts, new endowed 

professorships, a number of undergraduate student scholarships, and new graduate 

fellowships from industry, and a prestigious named lectureship series about the role 

of the Engineer in Society have been attracted to the ME Department. 

• Made significant progress towards a “paperless” administration through the 

development and implementation of electronic solutions in the areas of student 
Assanis, 6 

services (with web-based graduate application and admissions tracking systems), 

financial reporting (with accounting statements for contracts on line), faculty 

recruiting and faculty data center. 

• Promoted the systematic exchange of faculty and students with strategically selected 

global partners, notably with the Shanghai Jiao Tong University, the Korean 

Advanced Institute for Science and Technology, Seoul National University and the 

Technical University of Berlin. 

• Enhanced the strong tradition of an active and engaged External Advisory Board 

(EAB) which has served as a model for other CoE Departments and the University 

of Michigan’s Transportation Research Institute (UMTRI). 

• Promoted the development of K-12 programs intended to spark the interest of the 

brightest youngsters – including women and traditionally underrepresented groups in 

math, science and engineering.

Contributions as Director of Automotive Engineering Program 

As the Founding Director of the Master’s of Engineering Program in Automotive 

Engineering (AUTO), I was responsible for designing the curriculum and launching 

the new degree Program, first in the Department of Mechanical Engineering and 

subsequently as a College-wide program in the College of Engineering. My 

responsibilities have included recruiting prospective students, advising all M. Eng. 

students, developing new courses, and pursuing international collaborations for 

joint degree offerings with global Universities, and especially Aachen (Germany) 

and Loughborough (UK) as part of the Ford Global Automotive Systems Master’s 

degree. As part of our curriculum improvement activities, I founded the College of 

Engineering AUTO Council and led its efforts to develop and evolve a strong 

academic curriculum that meets industry needs. I also worked very effectively 

with the UM Center for Professional Development to offer to industry a distancelearning version of our M.Eng. Program. Our visionary pursuit of distance learning 

teaching has set a standard for other programs to emulate. 

Overall, I strived to grow our AUTO program, while simultaneously improving the 

quality of the entering students and courses offered. Our goals were met with great 

success, as evidenced by the enrollment in the AUTO program, which exceeded 

100 students within 5 years from the program's introduction, and the excellent job 

placement and very positive feedback expressed by many of our continuing students 

and graduates. 
Assanis, 7 

Contributions as Interim Director of Interdisciplinary Professional Programs 

As the Interim Director of the College of Engineering’s Interdisciplinary 

Professional Programs (INTERPRO), I provided stability and leadership during a 

period of transition and growth to six interdisciplinary programs, automotive 

engineering, financial engineering, integrated micro-systems, manufacturing 

engineering, pharmaceutical engineering, and plastics engineering. During my 

tenure as Director and working with the INTERPRO Directors’ Council, I oversaw 

the management of the large growth in student enrollment which reached an all time 

high (320 enrolled students) in the history of the INTERPRO programs. Most of 

this growth was accounted by part-time, distance learning professionals. I stepped 

down from my role as INTERPRO Director and AUTO Program Director to 

assume the position of Chair of Mechanical Engineering. 

 

OTHER CONTRIBUTIONS TO SERVICE 

 Major Committee Assignments at University of Michigan 

 

University of Michigan Committees: 

Vice President of Research Committee on Entrepreneurship, 2011 

Vice President of Research Director’s Council, 2009-date 

North Campus Research Complex, Director’s Committee, 2009-2010 

Rackham Distinguished Faculty Achievement Award Committee, 2009-2011 

Panel on Engagement/Institutes, Site Visit of High Learning Commission on 

University Re-Accreditation, March 2010 

UM Energy Council, Founding Member, 2003-2007 

Charter member of the team that actively pursued the development of a 

UM research thrust on Energy working in partnership with other 

Colleges, articulated the vision statement for the thrust, and recommended 

to the UM administration the development of a University-wide Energy 

Laboratory at the site of the decommissioned nuclear reactor. 

President’s Committee on Intellectual Property Policy, 2001-02, Member 

University Senate, 1995-98, Elected Senator 

College of Engineering Committees: 

College of Engineering (COE) Budget Task Team, 2005-07, Member 

COE Center of Professional Development Executive Committee, 2005-06, 

Member 

COE Faculty Fellows Program, October 11-12, 2002, Panelist 

COE Interdisciplinary Professional Program Directors Committee, 2001, Chair 

COE Nominating Committee, 2000-2001, Chair 

COE Automotive Council, 1999-date, Chair 

COE Curriculum Committee, 2000, Member 

 COE Committee on Reshaping Graduate Education at the Master’s Level, 

 1998-99, Member 

 COE Committee on M. Eng. Programs, 1998-99, Member 

 COE UM-National University of Singapore Committee on Establishment of 
Assanis, 8 

 Joint M.Eng. Program in Automotive Engineering, 1997-98, Chair 

COE Committee on Faculty Incentives for Continuing Education (ICE) and 

Distance Learning Instruction, 1997-98, Member 

Departmental Committees: 

ME Honors and Awards Committee, 2008-2011 

ME (formerly MEAM) Advisory Committee, 

Elected Member 1995-96, 1997-98 and Fall 2001 

Chair, 2002-2008 

 ME (formerly MEAM) Planning Committee 

Member, 1997-98 

Chair, 2002-2008 

 MEAM Thermal Science Instructional Area Coordinator, 1997-2000 

 MEAM Space Task Force Committee, 1996-98, Member 

 W. E. Lay Automotive Laboratory Test Cell Committee, 1994-present, Chair 

 W. E. Lay Automotive Laboratory Renovations Committee, 1994-95, Member 

 MEAM Laboratory and Safety Committee, 1995-1998, Member 

Service to Other Organizations 

1. External Boards 

Member, Board of Directors, NextEnergy, a non-profit organization with a 

mission to be one of the nation’s leading non-profit research catalysts 

and business accelerators for alternative and renewable energy, 2010-

date. 

Member, Board of Directors, Consortium for Advanced Simulation of Nuclear 

Reactors, an energy innovation hub led by Oak Ridge National 

Laboratory (ORNL) and funded by DOE up to $122 million, 2010-2015. 

Member, President’s Council of Advisors on Science and Technology 

(PCAST) Working Group on Energy Technology Innovation System, 

2010. 

Co-Chair, National Academy of Engineering Annual German-American 

Frontiers of Engineering GAFOE Symposium, 2010-2012. 

Member, Science and Technology Council Advisory Board, Cummins Engine 

Company, Inc., Columbus, IN, 2010. 

Member, International Advisory Board, Center for Clean Combustion Energy, 

Tsinghua University, China, 2010-2013. 

Chair, Advisory Board, Tula Technology, Santa Clara, CA, 2009-date. 

Member, State of Michigan Great Lakes Wind Council, 2009-2010. 
Assanis, 9 

Member, Energy Council of the CTO Forum, a Silicon Valley-based 

organization that brings together Chief Technology Officers from a 

cross-section of businesses and industries to discuss critical issues at the 

intersection of technology, energy and the environment, 2009-date. 

Member, External Advisory Board, Center for Mobile Propulsion, RWTH 

Aachen University, 2009-date. 

Member, National Academy of Sciences Committee on Fuel Economy of 

Medium- and Heavy-Duty Vehicles, appointed by the National Research 

Council’s Board on Energy and Environmental Systems, 11/08-5/31/10. 

Member, ASME Internal Combustion Engine Division Executive Committee, 

2008-10. 

Chair, King Abdullah University of Science and Technology (KAUST) 

Search for Director of Center for Clean Combustion Energy, 2008-09. 

Member, External Validation Panel for Launching MSc degree in Automotive 

Engineering Design, Hong Kong Polytechnic University, 2007. 

Member, Global External Advisory Board, Department of Mechanical 

Engineering, Korean Advanced Institute for Science and Technology 

(KAIST), 2006-2008. 

Member, External Advisory Board, Department of Mechanical Engineering, 

Georgia Tech, 2004-date. 

Member, External Advisory Panel, “Business Briefing: Global Automotive 

and Manufacturing and Technology,” World Market Research Centre, 

May 2002. 

2. Editorships 

 Editor, International Journal of Automotive Technology, 2008-2011 

 

 Editorial Board, International Journal of Powertrains, 2010-date 

 Editorial Board, International Journal of Engine Research, 2003-2012 

 Editorial Board, International Journal of Automotive Technology, 2005-2008 

 Associate Editor, ASME Journal for Gas Turbines and Power, 1996-2007 

 Scientific Board, Ingineria Automobilului, 2007-date 

 Guest Editor, International Journal of Heavy Vehicle Systems, 2004 
Assanis, 10 

3. Professional Society Memberships 

American Society of Mechanical Engineers, Fellow 

 Executive Committee Member, ICE Division, 2008-2013 

 Journal Associate Editor, 1996-2008 

 Past Chair of Student Activities, ICE Division 

Society of Automotive Engineers, Fellow 

 Member, SAE Research Executive Committee, 2000-date 

 Faculty Advisor, University of Michigan, 1996-2004 

CoE Future Car, Faculty Co-Advisor, 1997-98 

 Member, Advanced Powerplant Committee 

 Member, Passenger Car Readers Committee 

 Member, Vehicular Heat Exchanger and Heat Transfer Committee 

American Society for Engineering Education, Member 

Sigma Xi, Member 

New York Academy of Sciences, Member 

The Combustion Institute, Member 

Society of Naval Architects and Marine Engineers, Associate Member 

4. Organizing and Chairing Conferences, Sessions, Workshops, Lectures

Co-Chair and Co-Organizer, Michigan Memorial Phoenix Energy Institute and 

Fraunhofer Institutes of Germany Joint Conference, “Towards Carbon Neutral 

Vehicles,” Plymouth, MI, October 21, 2010.

Moderator, Panel on “Fuel Economy and Clean Transportation of the Future,” 

Michigan Memorial Phoenix Energy Institute and Fraunhofer Institutes of 

Germany Joint Conference, “Towards Carbon Neutral Vehicles,” Plymouth, 

MI, October 21, 2010.

Chair, Plenary Session on “Future Mobility - Energy, Environment & Carbon 

Management,” Emissions 2010, Michigan League, University of Michigan, 

Ann Arbor, June 15-16, 2010.

Co-Organizer and Co-Chair, 11th International Conference on Present and Future 

Engines for Automobiles, Shanghai, China, May 30-June 3, 2010.

Organizer, 3rd Annual Michael E. Korybalski Endowed Lecture in Mechanical 

Engineering: “Engineering, Innovation and the Challenges of the 21st

Century,” given by Charles Vest, President NAE and Emeritus President, 

M.I.T., May 12, 2010

Co-Chair, National Academy of Engineering Annual German-American Frontiers of 

Engineering GAFOE Symposium, Oak Ridge National Laboratory, Oak Ridge, 

TN, April 22-25, 2010.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May 12-13, 2009 

Organizer, 2nd Annual Michael E. Korybalski Endowed Lecture in Mechanical 

Engineering: “Size Matters,” given by Dr. Roger McCarthy, Emeritus 

Chairman and CEO, Exponent, Inc., May 4, 2009

Chair, Prime Power, National Defense Industrial Association – Michigan Chapter, 

Power and Energy Workshop, Troy, MI, November 18-19, 2008

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2008
Assanis, 11 

Member of Scientific Committee, International Workshop on Advances in 

Combustion Science and Technology, India Institute of Technology, Kanpur, 

India, Dec. 31, 2007- Jan. 8, 2008

Organizer, Inaugural Michael E. Korybalski Endowed Lecture in Mechanical 

Engineering: “Driving to a Sustainable Future, a New DNA for the 

Automobile,” given by Dr. Lawrence Burns, VP Research, Development and 

Planning, General Motors

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2007. 

Member of Scientific Committee, 2nd International Symposium on Clean and 

Efficient Combustion Engines, Tianjin, China, July 10-13, 2006.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2006. 

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2005.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2004.

Co-Organizer, “Premixed Charge Compression Ignition Engines,” 2003 JSAE/SAE 

International Spring Meeting, Yokohama, Japan, May 19-22, 2003.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2003.

Co-Organizer and Chair, “Homogeneous Charge Compression Ignition Engines,” 

2003 SAE World Congress, Detroit, MI, March 3-6, 2003.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May, 2002.

Organizer, “Homogeneous Charge Compression Ignition Engines,” 2002 SAE 

International Spring Fuels & Lubricants Meeting, Reno, Nevada, May 6 - 8, 

2002.

Co-Organizer, “Advanced Hybrid Powertrain Systems,” 2002 World Congress, 

Detroit, MI, March 4-7, 2002.

Co-Organizer, “Homogeneous Charge Compression Ignition Engines,” 2002 World 

Congress, Detroit, MI, March 4-7, 2002.

Co-Organizer and Chair, “Homogeneous Charge Compression Ignition Engines,” 

ASME Fall Technical Conference, Argonne, IL, Sep. 23-26, 2001.

Co-Organizer, “Homogeneous Charge Compression Ignition Engines,” SAE 2001 

Fall Fuels and Lubricants International Conference, San Antonio, TX, 

September 24-27, 2001.

Member, Advisory Committee, COMODIA 2001, International Symposium on 

Diagnostics and Modeling of Combustion in Internal Combustion Engines, 

Nagoya, Japan, July 1-4, 2001.

Organizer and Chair, “Homogeneous Charge Compression Ignition Engines,” SAE 

2001 Spring Fuels and Lubricants International Conference, Orlando, 

Florida, May 7-9, 2001.

Chair and Co-Organizer, ARC Annual Conference, “Critical Technologies for 

Modeling and Simulation of Ground Vehicles,” May 15-16, 2001.

Co-Organizer and Co-Chair, “Hybrid Electric Vehicles,” SAE International Congress 

and Exhibition, March 5-8, 2001.

Co-Organizer and Chair, “Novel SI and CI Combustion Systems,” SAE 2000 Fuels 

and Lubricants International Conference, Paris, France, June 19-22, 2000.

Co-Organizer and Session Chair, ARC Annual Conference, “Critical Technologies 

for Modeling and Simulation of Ground Vehicles,” May 2000.

Co-Organizer, “Direct Injection Engines and Sprays,” ASME-ICE Sprint Technical 

Conference, San Antonio, TX, April 9-12, 2000.
Assanis, 12 

Co-Organizer, “Homogeneous Charge Compression Ignition Engines,” SAE 

International Fuel and Lubricants Meeting, Toronto, Canada, Oct. 25-28, 

1999.

Organizer, “Modeling and Simulation of Direct Injection Engine Processes,” ASMEICE Fall Technical Conference, Ann Arbor, MI, Oct. 16-20, 1999.

Host, ASME-ICE Fall Technical Conference, Ann Arbor, MI, Oct. 16-20, 1999.

Member of Technical Program Committee, Vehicle Thermal Management Systems 

VTMS-4 International Conference, London, UK, May 24-26, 1999.

Co-Organizer and Session Chair, ARC Annual Conference, “Critical Technologies 

for Modeling and Simulation of Ground Vehicles,” May 1999.

Organizer, “Modeling and Simulation of Engine Combustion Processes,” ASME-ICE 

Spring Technical Conference, Columbus, IN, April 24-28, 1999.

Organizer, “Advanced Diesel Engine Powertrains,” SAE International Congress and 

Exposition, Detroit, MI, Feb. 23-26, 1999.

Organizer, “Modeling and Simulation of Engine Combustion Processes,” ASME-ICE 

Fall Technical Conference, Clymer, New York, September 27-30, 1998.

Moderator, “The Future of Automotive Systems,” SAE Automotive Systems Testing 

Topical Technical Symposium (TOPTEC), Novi, MI, October 14-15, 1998.

Co-Organizer and Session Chair, ARC Annual Conference, “Critical Technologies 

for Modeling and Simulation of Ground Vehicles,” May 1998.

Chair, Panel on Surface Engineering and Tribology, SAE International Congress and 

Exposition, Detroit, MI, Feb. 23-26, 1998.

Organizer, “Adiabatic and Miller Cycle Engines,” SAE International Congress and 

Exposition, Detroit, MI, Feb. 23-26, 1998.

Organizer, “New Analytical Methods in Engine Design,” ASME-ICE Fall Technical 

Conference, Madison, WI, Sept. 27 - Oct. 1, 1997.

Co-Organizer and Session Chair of ARC Annual Conference, “Critical Technologies 

in Modeling and Simulation of Ground Vehicles,” June 3-4, 1997.

Member of Technical Program Committee, Vehicle Thermal Management Systems 

VTMS-3 International Conference, Indianapolis, IN, May 19-22, 1997.

Organizer, “New Analytical Methods in Engine Design,” ASME-ICE Spring 

Technical Conference, Fort Collins, Colorado, April 27-30, 1997.

Co-Organizer, "Adiabatic Engines", SAE International Congress and Exposition, 

Detroit, MI, 1997.

Member, Program Review Subcommittee, Twenty-Sixth International Symposium on 

Combustion, Naples, Italy, July 28-Aug. 2, 1996.

Co-Organizer and Session Chair, ARC Annual Conference, “Critical Technologies 

for Modeling and Simulation of Ground Vehicles,” May 29-30, 1996.

Organizer, Student Paper Competition, ASME ICE Fall Technical Conference, 

Fairborn, OH, Oct. 20-23, 1996.

Co-Organizer and Chairman, “Engine Simulations,” ASME ICE Fall Technical 

Conference, Fairborn, OH, Oct. 20-23, 1996.

Co-Organizer, “Adiabatic Engines,” SAE International Congress and Exposition, 

Detroit, MI, 1996.

Organizing Committee, Fraunhofer Institute-University of Michigan Joint 

Conference, “The Best of German/American Automotive Technology,” 

Southfield, MI, June 27-28, 1995

Co-Organizer and Chairman, “Engine Simulations,” ASME Engine Technology 

Spring Conference, Marietta, Ohio, April 23-26, 1995.

Co-Organizer and Session Chair of ARC Annual Conference, “Critical Technologies 

in Modeling and Simulation of Ground Vehicles,” April 19-20, 1995

Co-Organizer, “Adiabatic Engines,” SAE International Congress and Exposition, 

Detroit, MI, 1995.
Assanis, 13 

Chairman and Co-Organizer, “Modeling Engine Processes,” ASME Fall Technical 

Conference, Lafayette, IN, 1994.

Chairman and Co-Organizer, “Adiabatic Engines,” SAE International Congress and 

Exposition, Detroit, MI, 1994.

Chairman and Organizer, “Engine Design,” Energy Technology Conference and 

Exhibition, New Orleans, LA, 1994.

Chairman and Co-Organizer, "Engine Simulation and Controls," ASME Fall 

Technical Conference, Morgantown, WV, 1993.

Co-Chairman, "Engine Sprays," ILASS, Worcester, MA, 1993.

Chairman, “Vehicle Cooling Systems,” International Conference on Vehicle Thermal 

Management Systems, Columbus, OH, 1993.

Chairman and Co-Organizer, “Adiabatic Engines,” SAE International Congress and 

Exposition, Detroit, MI, 1993.

Vice-Chairman and Co-Organizer, “Intake Air Management,” Energy Technology 

Conference and Exhibition, Houston, TX, 1993.

Chairman and Co-Organizer, “Adiabatic Engine Components,” Vice-Chairman, 

“High Temperature Engine Heat Transfer,” SAE International Congress and 

Exposition, Detroit, MI, 1992.

Vice-Chairman and Co-Organizer, “Engine Simulation,” Energy Technology 

Conference and Exhibition, Houston, TX, 1992.

Co-Organizer, “Panel on Post-95 Low Emission Engines,” ASME Energy 

Technology Conference and Exhibition, Houston, TX, 1991.

Moderator and Co-Organizer, “Panel on Post-95 Low Emission Engines,” SAE 

International Congress and Exposition, Detroit, MI, 1991.

Chairman and Co-Organizer, “Adiabatic Engine Components,” Vice-Chairman, 

"High Temperature Engine Heat Transfer," SAE International Congress and 

Exposition, Detroit, MI, 1991.

Chairman and Co-Organizer, “Adiabatic Engine Components,” Vice-Chairman, 

"High Temperature Engine Operation," SAE International Congress and 

Exposition, Detroit, MI, 1990.

Vice-Chairman, “Basic Engine Processes,” Energy Technology Conference and 

Exhibition, Houston, TX, 1989.

Chairman and Co-Organizer, “Adiabatic Engine Components,” Vice-Chairman, 

"High Temperature Tribology," SAE International Congress and Exposition, 

Detroit, MI, 1989.

Vice-Chairman and Co-Organizer, “International Symposium on Flows in 

Reciprocating Internal Combustion Engines,” ASME Winter Annual 

Meeting, Chicago, IL, 1988.

Vice-Chairman, “Basic Engine Processes,” American Society of Mechanical 

Engineers, Energy Technology Conference and Exhibition, New Orleans, 

LA, 1988.

Assistant Chairperson, “High Temperature Tribology,” SAE International Congress 

and Exposition, Detroit, MI, 1988.

Chairman, “Engine Simulation Studies,” International Association for Vehicle 

Design Fourth International Congress, Genera, Switzerland, 1987.

Assistant Chairperson, “Adiabatic Engines,” SAE International Congress and 

Exposition, Detroit, MI, 1987.

5. Service as Consultant to Government and Industry

Assanis and Associates, Inc., President, Ann Arbor, MI (2000-date) 

Optimetrics, Inc., Ann Arbor, MI (1999) 
Assanis, 14 

Textron Automotive, Southfield, MI (1998) 

M.A.N.A.G.E., Inc., President, Ann Arbor, MI (1995-1998) 

Automated Analysis Corporation, Ann Arbor, MI (1996) 

Mobil Technology Company, New Jersey (1996-1997) 

GM Electromotive Division, La Grange, IL (1988-1992) 

National Aeronautics and Space Administration, Cleveland, OH (1988) 

Adiabatics, Inc., Columbus, IN (1986-1991) 

Science Application International Corp., Seattle, WA (1986-1987) 

CONTRIBUTIONS TO EDUCATION 

Sustained Commitment to Education 

I have sustained my passionate commitment to education for over 20 years. 

As an Assistant and Associate Professor at the University of Illinois at UrbanaChampaign, I have taught a range of thermal science courses with student 

evaluations of my teaching consistently placing me at the very top in a group of 50 

faculty members. After joining the University of Michigan, my teaching 

evaluations (4.74/5.0 average for the quality of the courses I have taught and 

4.85/5.0 for the effectiveness of my teaching) have continued to be among the 

highest in the Mechanical Engineering Department (55 tenured or tenure track 

faculty) and the College of Engineering (more than 320 faculty members). 

 In 1987, I was honored with the Society of Automotive Engineers Ralph 

Teetor Award, given to 20 outstanding engineering educators nationwide each year. 

In 1988, I was one of six young UIUC faculty members selected in campus-wide 

competition to receive Lilly Teaching Fellow Awards. In 1990, I received the 

American Society of Mechanical Engineers/Pi Tau Sigma Gold Medal Award given 

annually in nationwide competition to the best mechanical engineer 10 years after 

graduation. In 1991-94, I was named University of Illinois Scholar for my 

contributions to research and teaching. I am truly gratified to have been honored 

with the 1997-98 MEAM Excellence in Teaching Award, the 2000 College of 

Engineering Teaching Excellence Award, the distinguished Arthur F. Thurnau 

Chaired Professorship, and as the inaugural recipient of the Jon R. and Beverly S. 

Holt Chaired Professorship. 

Teaching Philosophy 

I have always felt that a successful educator must love teaching and be able 

to convey excitement for learning to his/her students. Many of my activities as a 

teacher and mentor are governed by my strong belief that the key to effective 

teaching is to be enthusiastic about your teaching and to genuinely care about 

passing your knowledge to your students. I personally strive to show my students 

my own excitement about the material and to motivate them to make a sincere 
Assanis, 15 

effort to master the subject. I have always emphasized the importance of an 

engaging and interactive teaching-learning process, and created an open and 

informal atmosphere in the class that encourages students to ask or answer 

questions. I have taken some bold steps to shift the paradigms of teaching 

theoretical concepts to engineers, infused my own scholarly activities into the 

classroom and shared my teaching techniques with my colleagues and future 

educators. I have stressed my belief that the only way to learn a subject is through 

hard work and application of your knowledge to real projects, and repeatedly found 

that students will work hard as long as they are motivated, encouraged when they 

face adversity and rewarded for their intellectual accomplishments. 

Beyond the traditional classroom teaching, I have adopted a holistic 

approach to the teaching/learning process and utilized effectively the time outside 

the classroom to advise, mentor, coach and teach the students. I have advised 

more than 50 doctoral, 100 Master’s and M.Eng. students and hundreds of 

undergraduate students. I believe that sound advice and broadening of their 

perspective can have a critical impact in the students’ future careers. I am gratified 

that several of my students have emulated me as a role model and have joined 

academia, including (within the past five years) Clemson University, The Cooper 

Union for the Advancement of Science and Art, Kansas University, Texas A&M 

University, United States Merchant Marine Academy and the University of 

Michigan. I have also greatly enjoyed being the Faculty Advisor of the student 

chapters of the Society of Automotive Engineers and the American Society of 

Mechanical Engineers, working with the various student project teams, helping 

them in their fundraising efforts, and addressing their technical and administrative 

needs. Getting to know the undergraduate students better and contributing to their 

education outside the classroom through special projects is time consuming, but can 

be extremely rewarding to both the students and the teacher. 

Teaching Innovations 

 I am particularly proud of the new perspective I have brought to the student 

teaching and learning process. The traditional way of teaching undergraduate 

courses in thermo-sciences and their applications to energy conversion and internal 

combustion engines has been through lectures and the use of highly idealized 

models. These ideal models inherently make crude assumptions so that results are 

often far from reality. Without compromising teaching of the fundamentals, I have 

introduced an innovative approach to further the education of my students through 

the incorporation and coordinated use of a series of hands-on laboratories, computer 

simulation tools, scientific movies, and real life case studies that are presented 

within and in parallel with the lectures. Sophisticated laboratory experiments and 

realistic simulation programs provide a more complete understanding of the 

important physical processes. Students can use the simulation models to compare 

and analyze their experimental data under similar operating conditions, and suggest 

ways to improve either the simulation models or the experimental techniques. 
Assanis, 16 

In my continuing efforts to enrich the class content, I have also relied on the 

use of the internet and distance learning. With my graduate student instructors, we 

have developed integrated learning environments that can be used asynchronously, 

and at the student’s learning pace, to bring together lecture notes, the blackboard, 

assignments, solutions, clipboards, laboratory demos, simulation runs and engine 

movies in digital media. We are now planning to run laboratory experiments live 

from the classroom, or for that matter from any internet connection, to enable 

students to appreciate lecture content and theory in the light of reality with live 

demonstrations. Through these innovative approaches, I constantly strive to add 

another dimension to the student learning. 

Infusion of Scholarly Contributions into Teaching-Learning Process 

 My teaching interests parallel and complement my research interests, as my 

philosophy is that an excellent teacher must be at the same time a leader in his field 

of research. Only this way I feel I can give my students the best and most relevant 

education to enable them become leaders in their fields. In the course of my 

group’s research activities, we have developed a large body of engine simulation 

software that is extensively used by automotive manufacturers in engine 

development. With the ever-increasing capabilities of personal computers and 

graphical programming languages such as C++ and MATLAB-SIMULINK, it has 

become possible to infuse user-friendly, student versions of these computer 

simulations to the classroom, thus greatly contributing to my effective teaching. 

My research activities have also enabled me to rejuvenate the Walter Lay 

Automotive Laboratory, thus contributing advanced engine experiments to our 

classes and exposing our students to state-of-the-art laboratory set-ups 

(http://me.engin.umich.edu/autolab/). These activities have contributed to 

reaffirming U of M’s leadership in automotive engineering. 

Contributions to New Course Development 

 Although the University of Michigan has had a long tradition of excellence 

in the instruction of internal combustion engines, when I started my career as a 

Professor at Michigan I realized that our engine-related courses and research 

facilities were not adequate to meet the current demands of the industrial and 

research communities for automotive engineers. In order to give our students the 

best possible education in the field, I have taken a series of steps. First, I 

completely revised the lectures of our undergraduate/beginner graduate course (ME 

438) in internal combustion engines. In addition, I developed and incorporated a 

series of laboratories as part of the course, which was thus converted from three to 

four credit hours. This course enrollment has almost doubled in size following my 

revisions, and has been offered simultaneously via distance learning to industry. 

Second, based on my scholarly activities, I developed a graduate level course 

(originally ME 534 and now renumbered as ME538) that deals with the application 

of thermal sciences to the simulation and design of modern combustion engines. 

Third, I have developed with my undergraduate and graduate students a single-
Assanis, 17 

cylinder engine laboratory experiment that has been used as part of our thermal 

science laboratory class. 

As part of my activities as the Director of the Automotive Program, I 

oversaw the development of the curriculum for the new degree program and 

contributed a number of the new modules that were essential to achieving the goals 

M.Eng. program. In order to broaden the horizons of automotive engineers, I 

introduced a two semester sequence of automotive seminars (ME 591 and ME 592, 

now renumbered as ME 501), delivered by industry leaders, that exposed the 

students to the wide spectrum of interdisciplinary engineering activities involved in 

the process of development, design, and manufacturing of complex automotive 

systems. In one of its offerings, the UM automotive seminar class was focused on 

Vehicle Energy, in global collaboration with Aachen University, Germany, and 

Ford Motor Company. Furthermore, to provide our automotive engineering 

students with practical experience in team building, carrying out projects in 

interdisciplinary teams, and in developing and managing projects, I introduced the 

capstone M.Eng. Automotive project (ME 593, now renumbered as ME 502). The 

Automotive Seminars and Project experiences we provide our students have been a 

model for similar “practimum” programs introduced by several Departments in the 

College of Engineering. 

As part of my activities as the Director of the Michigan Memorial Phoenix 

Energy Institute, I have co-developed and moderated a graduate level 

interdisciplinary seminar on “The Power of And: Energy Systems and Policy 

Opportunities for the U.S.” The objective of the seminar series is to introduce the 

audience to the power of integrated energy systems and the promise it holds to craft 

an energy policy for the United States that ensures plentiful and low-cost energy, 

national security and sustainable economic growth. The seminar series draws on 

the collective knowledge and experience of U-M faculty, staff and students. 
Assanis, 18 

Courses Taught at University of Michigan 

Date Course Course Title Enroll Crs Eval Instr Eval 

Winter 

95 

ME 534 Advanced Internal Combustion 

Eng. 

23 4.45 4.54 

Fall 95 ME 438 Internal Combustion Engines 42 4.85 4.85 

Winter 

96 

ME 534 Advanced Internal Combustion 

Eng. 

21 4.87 4.97 

Winter 

96 

ME 592 Automotive Eng. Seminar II 8 n/a 1 n/a 

Fall 96 ME 438 Internal Combustion Engines 69 (43+26)2 4.83 4.85 

Fall 96 ME 591 Automotive Eng. Seminar I 18 n/a N/A

Winter 

97 

ME 534 Advanced Internal Combustion 

Eng. 

18 4.86 4.94 

Winter 

97 

ME 592 Automotive Eng. Seminar II n/a n/a 

Fall 97 ME 438 Internal Combustion Engines 68 (37+31) 4.80 4.88 

Fall 97 ME 591 Automotive Eng. Seminar I 12 n/a n/a 

Winter 

98 

ME 534 Advanced Internal Combustion 

Eng. 

32 4.17 4.72 

Winter 

98 

ME 592 Automotive Eng. Seminar II 40 (15+25) n/a n/a 

Fall 98 ME 438 Internal Combustion Engines 50 4.86 4.94 

Fall 98 ME 591 Automotive Eng. Seminar I n/a n/a 

Winter 

99 

ME 592 Automotive Eng. Seminar II n/a n/a 

Fall 99 ME 438 Internal Combustion Engines 88 (53+35) 4.83 4.95 

Fall 99 ME 591 Automotive Eng. Seminar I 33 (18+15) n/a n/a 

Winter 

00 

ME 534 Advanced Internal Combustion 

Eng. 

23 4.71 4.85 

Winter 

00 

ME 592 Automotive Eng. Seminar II 

(Vehicle Energy Seminar) 

38 (23+15) n/a n/a 

Fall 00 ME 591 Automotive Eng. Seminar I 33 (18+15) n/a n/a 

Fall 01 ME 438 Internal Combustion Engines 66 (41+25) 4.85 4.90 

Fall 01 ME 591 Automotive Eng. Seminar I 40 (15+25) n/a n/a 

Fall 02 ME 438 Internal Combustion Engines 53 4.85 4.85 

Fall 03 ME 438 Internal Combustion Engines 72 (32+40) 4.97 4.97 

Fall 04 ME 438 Internal Combustion Engines 54 4.91 4.93 

Fall 05 ME 438 Internal Combustion Engines 70 (50+20) 4.88 4.88 

Fall 06 ME 438 Internal Combustion Engines 50 4.92 4.91 

Winter 

08 

ME 599 Analysis and Control of 

Alternative Powertrains 

26 (20+6) 4.42 4.22 

Fall 08 ME 438 Internal Combustion Engines 40 4.94 4.94 

Wint 09 ME 538 Advanced ICEs 32 4.54 4.80 

 

1

 Organizer and host of Automotive Engineering Seminar Series I and II. 

 Standard course evaluation forms not applicable (n/a). 2

 Distribution designates student enrollment for on-campus and distance learning students.
Assanis, 19 

Offerings of Short Courses and Workshops

I am a proponent of life-long learning and have frequently taught short 

courses and workshops to practicing engineers in industry. Examples are: 

“Modeling and Computer Simulation of Internal Combustion Engines,” Chair, 

Continuing Engineering Education, University of Michigan, 

September 9-13, 1996; July 7-11, 1997; June 29-July 3, 1998; July 5-

9, 1999; July 10-14, 2000. 

“Basic Engines and Their Controls,” Chair, Continuing Engineering 

Education, Motorola, Deerfield, IL, two-day offerings, 1996-2005. 

One-on-One Student Instruction and Mentorship 

Post-Doctoral Fellows Mentored 

1. George Papageorgakis (now with ExxonMobil) 

2. Dohoy Jung (now Assistant Professor at UM-Dearborn) 

3. George Delagrammatikas (now Assistant Professor at Cooper Union) 

4. Sang-Jin Hong (now with Ford Motor) 

5. Chris Depcik (now Assistant Professor at University of Kansas) 

6. Timothy Jacobs (now Assistant Professor at Texas A&M) 

7. Christos Chryssakis (now Research Scientist at NTU, Athens) 

8. Vassilis Hamosfakidis (now with Risk Metrics) 

9. Andreas Malikopoulos (now at ORNL) 

10. Robert Prucka (now Assistant Professor at Clemson University) 

11. Chaitanya Sampara (now at NanoStellar) 

12. Andrew Ickes (now at Argonne National Laboratories) 

13. Hee Jun Park (now at Samsung Heavy Industries, Korea) 

14. Seung Hwan Keum (continuing in my group) 

15. Byungchan Lee (now at UM- Dearborn) 

16. Will Northrop (now at GM R&D) 

17. Michael Smith (now at University of Michigan) 

Ph. D. Committees Chaired at University of Michigan 

1. Xiaobo Sun, 1996, Chair

2. George Papageorgakis, 1997, Chair 

3. Apoorva Agarwal, 1998, Chair 

4. Dohoy Jung, 2000, Chair 

5. George Delagrammatikas, 2001, Co-Chair (with P. Papalambros) 

6. Sang-Jin Hong, 2001, Co-Chair (with M. Wooldridge) 

7. Scott Fiveland, 2001, Chair 

8. Stani Bohac, 2002, Chair 

9. Kukwon Cho, 2003, Co-Chair (with Z. Filipi) 
Assanis, 20 

10. Guntram Lechner, 2003, Chair 

11. Christopher Depcik, 2003, Chair 

12. Bruno Vanzieleghem, 2004, Co-Chair (with H. Im)

13. Pin Zeng, 2004, Chair 

14. Wooheum Cho, 2004, Chair 

15. Junseok Chung, 2004, Co-Chair (with Z. Filipi) 

16. Tim Jacobs, 2005, Chair 

17. Aris Babajimopoulos, 2005, Chair 

18. Ron Grover, 2005, Chair 

19. Christos Chryssakis, 2005, Chair 

20. Bin Wu, 2005, Co-Chair (with Z. Filipi) 

21. Sangseok Yu, 2006, Co-Chair (with D. Jung) 

22. Vassilis Hamosfakidis, 2006 (Chair) 

23. Kyoung Joon Chang, 2007, Chair 

24. Alex Knafl, 2007, Chair 

25. Manbae Han, 2007, Co-Chair (with S. Bohac) 

26. Melody Papke, 2007, Co-Chair with Jun Ni 

27. Andreas Malikopoulos, 2007, Co-Chair (with P. Papalambros) 

28. Jonathan Hagena, 2007, Co-Chair (with Z. Filipi) 

29. Robert Prucka, 2007, Co-Chair (with Z. Filipi) 

30. Orgun Guralp, 2008, Co-Chair (with Z. Filipi) 

31. Chaitanya Sampara, 2008, Co-Chair (with E. Bissett, GM) 

32. Yanbin Mo, 2008, Chair 

33. Shawn Grannell, 2008, Co-Chair (with S. Bohac) 

34. Andrew Ickes, 2009, Co-Chair (with S. Bohac) 

35. Hee Jun Park, 2009, Co-Chair (with D. Jung) 

36. Seung Hwan Keum, 2009, Co-Chair (with H. Im) 

37. Byungchan Lee, 2009, Co-Chair (with D. Jung) 

38. Will Northrop, 2009, Co-Chair (with S. Bohac) 

39. Michael Smith, 2010, Co-Chair (with S. Bohac) 

40. Jason Martz, 2010, Chair 

41. Sung Jin Park, candidate, 2011 (expected), Co-Chair (with D. Jung) 

42. Mehdi Abarham, candidate, 2011 (expected), Co-Chair (with J. Hoard) 

43. Matt Spears, candidate, 2011(expected), Chair 

44. Jerry Fuschetto, candidate, 2011 (expected), Chair 

45. Russel Truemner, pre-candidate, 2011(expected), Co-Chair (with R. Beck) 

46. Stefan Klinkert, pre-candidate, 2011 (expected), Co-Chair (with S. Bohac) 

47. Sotiris Mamalis, pre-candidate, 2012 (expected), Co-Chair (with A. 

Babajimopoulos) 

48. Robert Middleton, pre-candidate, 2013 (expected), Chair 

49. Kevin Zaseck, pre-candidate, 2013 (expected), Co-Chair (with Z. Filipi) 

50. Janardhan Kodavasal, pre-candidate, 2013 (expected), Co-Chair (with A. 

Babajimopoulos) 

51. Prasad Shigne, candidate, 2013 (expected), Co-Chair (with A. 

Babajimopoulos)

52. Ashwin Salvi, pre-candidate, 2013 (expected), Co-Chair (with Z. Filipi) 

53. Elliott Alexander Ortiz Soto, pre-candidate, 2013 (expected), Chair

54. Vijai Manikandan, candidate, 2013 (expected), Chair

55. Luke Hagen, pre-candidate, 2013 (expected), Chair
Assanis, 21 

56. Brandon Lee, pre-candidate, 2013 (expected), Co-Chair (with A. 

Babajimopoulos)

 Ph. D. Committees Chaired at University of Illinois in Urbana-Champaign 

1. Qiong Li, 1991, Chair 

2. Leonard Shih, 1992, Chair 

3. Panos Tamamidis, 1992, Chair 

4. Constantine Varnavas, 1994, Chair 

5. Douglas Baker, 1995, Chair 

6. Michalis Syrimis, 1996, Chair 

 M. S. Committees Chaired at University of Michigan 

1. James Wallace, 1997, Chair 

2. Michael Mshar, 1998, Chair 

3. Scott Fiveland, 1999, Chair 

4. George Seaward, 2000, Chair 

5. Chris Depcik, 2000, Chair 

6. Salih Mahameed, 2001, Chair 

7. Ron Grover, 2001, Chair 

8. Selim Buyuktur, 2001, Co-Chair (with M. Wooldridge) 

9. Cheol Su Lee, 2001, Chair 

10. Brian Baldwin, 2001, Chair 

11. Tim Jacobs, 2002, Chair 

12. John Matsushima, 2002, Co-Chair (with Z. Filipi) 

13. Aris Babajimopoulos, 2002, Chair 

14. Christos Chryssakis, 2002, Chair 

15. Berrin Daran, 2002, Co-Chair (with Z. Filipi) 

16. Scott Thompson, 2003, Chair 

17. Chad Jagmin, 2003, Co-Chair (with Z. Filipi) 

18. Andrew Ickes, 2003, Chair 

19. Matthew Leustek, 2003, Chair

20. Wesley Williamson, 2004, Co-Chair (with Z. Filipi) 

21. Robert Prucka, 2004, Chair 

22. Jonathan Hagena, 2004, Chair 

23. Chaitanya Sampara, 2004, Chair 

24. Orgun Guralp, 2004, Co-Chair (with Z. Filipi) 

25. Gerald Fernandes, 2006, Co-Chair (with Z. Filipi) 

26. Chandra Sandrasekaran, 2006, Co-Chair (with S. Bohac) 

27. Steve Busch, 2007, Co-Chair (with S. Bohac) 

28. Vijayaraghavan Shriram, 2007, Co-Chair (with Z. Filipi) 

29. Alberto Lopez, 2008, Co-Chair (with S. Bohac) 

30. Challa Prasad, 2008, Co-Chair (with A. Babajimopoulos) 

31. Mark Hoffman, 2008, Co-Chair (with Z. Filipi) 

32. Michael Smith, 2009, Chair 

33. Anastasios Amoratis, 2009, Co-Chair (with A. Babajimopoulos) 

34. Sotiris Mamalis, 2009, Chair 
Assanis, 22 

35. Ashwin Salvi, 2009, Co-Chair (with Z. Filipi) 

36. Robert Middleton, 2009, Chair 

37. Samuel Olesky, 2009, Chair 

38. Elliott Alexander Ortiz Soto, 2010, Chair

39. Janardhan Kodavasal, 2010, Co-Chair (with A. Babajimopoulos) 

40. Prasad Shigne, 2010, Co-Chair (with A. Babajimopoulos)

41. Jeremy Spater, 2010, Chair 

42. Laura Manofsky, 2011 (expected), Chair 

43. Ann Marie Lewis, 2011 (expected), Chair

44. Luke Hagen, 2011 (expected), Chair

45. Srinath Gopinath, 2011 (expected), Chair

46. Kyoung Hyun Kwak, 2011 (excepted), Co-Chair (with D. Jung)

47. Tejas Chafekar, 2011 (expected), Co-Chair (with J. Hoard)

M. S. Degrees Chaired at University of Illinois in Urbana-Champaign 

1. Edward Badillo, 1989, Chair 

2. Matthew Polishak, 1989, Chair 

3. Michael Bonne, 1989, Chair 

4. James McLeskey, 1989, Chair 

5. Riadh Namouchi, 1990, Chair 

6. Tarun Mathur, 1990, Chair 

7. Constantine Varnavas, 1990, Chair 

8. Francis Friedmann, 1990, Chair 

9. Andrew Phillips, 1990, Chair 

10. Kevin Wiese, 1990, Chair 

11. Brian Bolton, 1990, Chair 

12. Panos Tamamidis, 1990, Chair 

13. Thomas Leone, 1990, Chair 

14. Timothy Burt, 1990, Chair 

15. Douglas Baker, 1991, Chair 

16. Gregory Clampitt, 1991, Co-Chair (with White) 

17. Daniel Clark, 1991, Chair 

18. Evangelos Karvounis, 1991, Chair 

19. Matthew Lipinski, 1992, Co-Chair (with White) 

20. Michalis Syrimis, 1992, Chair 

21. Matthew Schroder, 1993, Co-Chair (with White) 

22. Donald Nakic, 1994, Co-Chair (with White) 

23. George Papageorgakis, 1994, Chair 

24. Scott Butzin, 1994, Chair 

25. Cristopher Bare, 1995, Chair 

26. Thomas Brunner, 1995, Chair 

27. Paul Herring, 1995, Chair 

28. Stani Bohac, 1995, Chair 

29. Timothy Frazier, 1995, Chair 
Assanis, 23 

M. Eng. Automotive Projects Directed at University of Michigan 

 (ME 593/503, 4 credit hours) 

1. Winter 1996; Fadi Kanafani 

2. Winter 1996; Richard Sellschop 

3. Spring 1996; Philip Glazatov 

4. Spring 1996; David Silberstein 

5. Fall 1996; Caleo Tsai 

6. Fall 1997, Marc Allain 

7. Winter 1997; Osvaldo Corona 

8. Winter 1997; Fabien Redon 

9. Winter 1997; Steven Siegal 

10. Spring 1997; Eric Mokrenski 

11. Winter 1998; Lee Choon Hyong 

12. Winter 1998; Yu-Min Lin 

13. Winter 1998; Faisal Mahroogi 

14. Winter 1998; Bruno Vanzieleghem 

15. Summer 1998; Yuri Rodrigues 

16. Fall 1998; Claude Bailey 

17. Fall 1998; John Emley 

18. Fall 1998; Ghosh Ranajay 

19. Fall 1998; Islam Kazi 

20. Winter 1999; Stephanie Lacrosse 

21. Winter 1999; Russell Thompson 

22. Winter 1999; Carlos Armesto, Greg Christensen, Eugene Cox, John Dent 

23. Winter 1999; John Joyce 

24. Winter 1999; Marcus Branner 

25. Winter 1999; Michael McGuire 

26. Summer 1999; Steven Hoffman 

27. Summer 1999; Alejandro Sales 

28. Summer 1999; David Wheatley 

29. Fall 1999; Todd Petersen 

30. Fall 1999; John Matsushima 

31. Fall 1999; Michelle Chaka and Mary Wroten 

32. Fall 1999; Julie D'Annunzio, Timothy Veenstra, and Todd Glance 

33. Winter 2000; Bhargav SriParakash 

34. Winter 2000; Douglas Iduciani and Ronald Kruger 

35. Winter 2000; Timothy Gernant, Allen Lehmen and Jeffrey Kaiser 

36. Winter 2000; Brian Young, Mark Dipko and Andrew Slankard 

37. Winter 2000; Stephen White 

38. Winter 2000; Tomoyuki Takada, Mami Takada and Milton Wong 

39. Winter 2000; Cristian Arnou and Soon Low 

40. Winter 2000; Elaine Kelley 

41. Spring 2000; Joseph Fedullo, Colin Roberts and John Celmins 

42. Summer 2000; Frank Voorburg and Marie Mann 

43. Summer 2000; Ping (Pete) Yu 

44. Winter 2001; Jason Martz; 

45. Winter 2001; Kwang Yong Kang 

46. Summer 2002; Jonathan Jackson 
Assanis, 24 

47. Summer 2002; David Swain and Dan Yerrace 

48. Winter 2009; Peter Andruskiewicz 

49. Winter 2009; Dan Murray 

50. Winter 2009; Amit Goje 

AUTO 503 Capstone Special Project 

1. Fall 2008, Peter Andruskiewicz, 3 credit hours 

2. Winter 2009, Amit Goje, 3 credit hours

Ph.D. at Korea Advanced Institute of Science and Technology (KAIST), Korea 

(carried-out in part at W. Lay Automotive Laboratory under my direction) 

 Tong Won Lee, 2003 

Diplomarbeit at Technical University of Graz, Austria 

(carried-out at W. E. Lay Automotive Laboratory under my direction) 

Guntram Lechner, 1999

 Alex Knafl, 2001 

Studenarbeit at Rheinisch-Westfalische Technische Hochschule Aachen 

(carried-out at W. E. Lay Automotive Laboratory under my direction) 

 Michalis Panagiotidis, 1999 

 Christof Schultze, 1999 

Graduate Special Projects (ME 590) Directed at University of Michigan 

1. Winter 1995; Teresa Schulke; 3 credit hours 

2. Winter 1995, Fadi Kanafani; 3 credit hours 

3. Winter 1995, Karl Ondersma; 3 credit hours 

4. Spring/Summer1995; M. Mubbashir Abbas; 2 credit hours 

5. Winter 1996-98; Paul L. Powell III; 6 credit hours 

6. Fall 1997; Erik Koehler; 3 credit hours 

7. Winter 1998; Kukwon Cho; 3 credit hours 

8. Winter 1998; Scott Fiveland; 3 credit hours 

9. Winter 1999; Russell Thompson, 3 credit hours 

10. Winter 1999; Stephanie LaCrosse, 3 credit hours 

11. Summer 1999; Thomas Veling, 3 credit hours 

12. Fall 1999, John Matsushima, 3 credit hours 

13. Winter 2000, Carlos Armesto, 3 credit hours 

14. Winter 2000, Lee Byungchan, 3 credit hours 

15. Winter 2000 and Winter 2001, Cheol Su Lee, 6 credit hours 

16. Winter 2000, Jeff Sanko, 3 credit hours 

17. Winter 2000, Ryan Nelson, 3 credit hours 

18. Winter 2000, Selim Buyuktur, 3 credit hours 

19. Winter 2000, George Seaward, 3 credit hours 
Assanis, 25 

20. Winter 2000, Ping Yu, 3 credit hours 

21. Fall 2000, Marie Mann, 3 credit hours 

22. Fall 2000, Matthew Schwab, 3 credit hours 

23. Winter 2001, Cheol Su Lee, 3 credit hours 

24. Winter 2002, Josh Richards, 3 credit hours 

25. Winter 2002 and Fall 2002, Brett Thompson, 6 credit hours 

26. Fall 2002, Mengkai Zhang, 3 credit hours 

27. Fall 2003, Krishna Kumar, 3 credit hours 

28. Fall 2003 and Winter 2004, Andreas Malikopoulos, 6 credit hours 

29. Fall 2003 and Winter 2004, Christopher Morgan, 6 credit hours 

30. Winter 2004, Mark Hoffman, 3 credit hours 

31. Winter 2004, Weibin Zhu, 3 credit hours 

32. Fall 2004, Seung Hwan Keum, 3 credit hours 

33. Fall 2004, John Zeilstra, 3 credit hours 

34. Fall 2004 and Winter 2005, Kwangsoon Choi, 6 credit hours 

35. Fall 2004 and Winter 2005, Qi Wang, 6 credit hours 

36. Fall 2004 and Winter 2005, Qingan Zhang, 6 credit hours 

37. Fall 2005, Jarrod Robertson, 3 credit hours 

38. Fall 2005, Gudiseva Satya Varun, 3 credit hours 

39. Winter 2005, Stephen Busch, 3 credit hours 

40. Winter 2005, Abigail Mechtenberg, 3 credit hours 

41. Winter 2005, Richard Niedzwiecki, 3 credit hours 

42. Winter 2005, Choi Kwangsoon, 3 credit hours 

43. Winter 2006, Nikolas Anderson, 3 credit hours 

44. Winter 2007, David Ault; 3 credit hours 

45. Winter 2007, Michael Christianson, 3 credit hours 

46. Winter 2007, Matthew Freddo, 3 credit hours (with S. Bohac) 

47. Winter 2007, Dong Han, 3 credit hours 

48. Winter 2007, Stefan Klinkert, 3 credit hours (with S. Bohac) 

49. Winter 2007, Mahesh Kumar Madurai. 3 credit hours 

50. Winter 2007, Robert Middleton, 3 credit hours 

51. Winter 2007, Challa Prasad, 3 credit hours (with A. Babajimopoulos) 

52. Winter 2007, Ashutosh Sajwan, 3 credit hours (with S. Bohac) 

53. Winter 2007, Jaskirat Singh, 3 credit hours (with D. Jung) 

54. Winter 2007, Ashwin Salvi, 3 credit hours (with Z. Filipi) 

55. Winter 2007, Vishnu Nair, 3 credit hours 

56. Fall 2007; Vivek Srinivasan Narayanan; 3 credit hours 

57. Winter 2008, Ramamurthy Vaidyanathan; 3 credit hours

58. Spring 2008, Alphonso King, 6 credit hours

59. Fall 2008, Amit Goje, 3 credit hours (with J. Hoard)

60. Fall 2008, Doohyun Kim, 3 credit hours

61. Fall 2008, Kyoung-Hyun Kwak, 3 credit hours

62. Fall 2008, Saktish Sathasivam, 3 credit hours

63. Fall 2008, Prasad Shingne, 3 credit hours

64. Winter 2009, Sourabh Goel, 3 credit hours

65. Winter 2009, Chang-Ping Lee, 3 credit hours

66. Winter 2009, Kevin Zaseck, 3 credit hours

67. Winter 2009, Elliott Ortiz-Sotto, 3 credit hours 

68. Fall 2009, Vishnu Vitala, 3 credit hours 
Assanis, 26 

69. Winter 2010, Tejas Chafekar, 3 credit hours (with J. Hoard) 

70. Fall 2010, Saradhi Rengarajan, 3 credit hours (with J. Hoard) 

Undergraduate Special Projects (ME 490) Directed at University of Michigan 

1. Winter 1995, Maurice Moulton; 3 credit hours 

2. Winter 1995; George Papageorgakis; 3 credit hours 

3. Winter 1996; David Messih; 3 credit hours 

4. Winter 1996; Eric Morenski; 3 credit hours 

5. Winter 1996; Benedict J. Baladad; 3 credit hours 

6. Winter 1996; Kevin Ferraro; 3 credit hours 

7. Spring 1997, Andreas Athanassopoulos, 3 credit hours 

8. Fall 1998, Ryan Nelson, 3 credit hours 

9. Winter 1999; Nicholas Bellovary and Daniel Kulick, 3 credit hours 

10. Winter 1999; Daniel Herrera and Joel Hartter, 3 credit hours 

11. Winter 1999; Larry Mercier and Reza Sharifi, 3 credit hours 

12. Winter 2000; Nicolas Wetzler, 3 credit hours 

13. Winter 2001; Andrew Ickes, 3 credit hours 

14. Winter 2002; Keith DeMaggio, 3 credit hours 

15. Fall 2003; Marvin (Bob) Riley 

16. Fall 2004; Katherine Chia-Chun Ho, 3 credit hours 

17. Fall 2004, Liang Xue, 3 credit hours 

18. Winter 2005, Levi Roodvoets, 3 credit hours 

19. Fall 2005; Erin Robbins, 3 credit hours 

20. Winter 2006; David Ault, 3 credit hours 

21. Winter 2006; Tommaso Gomez, 3 credit hours 

22. Winter 2007; Daniel Murray, 3 credit hours 

23. Spring 2007, Dimitri Karatsinides, 2 credit hours 

24. Winter 2009; Anthony Mansoor, 3 credit hours 

25. Winter 2009, Lucas Vanderpool, 3 credit hours

ME 450 Senior Design Project 

1. Winter 2006, Dan Murray, Chris Marchese, Dave Ault, Randy Jones, 

“Design of a Hydraulic Dynamometer,” 3 credit hours

2. Winter 2007, Qioghui Fung, Chun Yang Ong, Chee Chian Seah, Joann 

Tung, “Heated Catalyst Test-Rig for Single Cylinder Engine”

Undergraduate Research Opportunity Program (UROP) 

1. Fall 2006, Christine Siew, “Determination of Operational Limits and 

Stability Analysis of HCCI Engine Using 1-D Simulation”

2. Fall 2006, Nathan Shoemaker, “Challenge X- Crossover to Sustainable 

Mobility”
Assanis, 27 

CONTIBUTIONS TO RESEARCH 

Major Research Accomplishments 

Dr. Assanis’ research interests lie in the thermal sciences and their 

applications to energy conversion, power and propulsion, and automotive 

systems design. His research focuses on analytical and experimental studies 

of the thermal, fluid and chemical phenomena that occur in internal 

combustion engines, after-treatment systems, and fuel processors. His efforts 

to gain new understanding of the basic energy conversion processes have 

made significant impact in the development of energy and power systems with 

significantly improved fuel economy and dramatically reduced emissions. His 

group’s research accomplishments have been published in over 250 articles in 

journals and international conference proceedings. More specifically: 

• Over the past 25 years, he has made major contributions in modeling and computer 

simulation of internal combustion engine processes and systems, under steady-state 

and transient operation, and in carrying-out sophisticated in-situ experimental 

techniques, applicable to operating engine combustion chambers, to validate their 

fidelity. His innovative work has shed light into complex fuel-air mixing, 

combustion, pollutant formation and transient heat transfer phenomena in metal and 

ceramic-insulated engine combustion chambers. His simulation models and 

experimental insights are used by engine researchers and developers (e.g., General 

Motors, Caterpillar, Argonne, Lawrence Livermore and Sandia National 

Laboratories) to improve vehicle fuel economy while at the same time satisfying 

ultra-stringent emissions standards. 

• His group has pioneered the integration of high fidelity engine models with driveline 

and vehicle models and used these comprehensive tools for realistic assessment and 

design optimization of conventional and hybrid powertrain systems. His engine-invehicle simulation methodologies have contributed significantly to the dual need-dual 

use heavy-duty industry/U.S. Army ground mobility mission through the 

development and optimization of advanced propulsion systems with 2-3 times higher 

fuel efficiency and ultra low smoke and particulate emissions. 

• He has made lasting contributions to the fundamental understanding of the chemical 

and physical processes that govern the operation of Homogeneous Charge 

Compression Ignition (HCCI) engines and their exhaust aftertreatment systems. His 

revolutionary insights make possible to operate engines in ultra clean, low 

temperature combustion, fuel economical regimes that constitute a paradigm shift 

from the traditional, high temperature, pollutant forming engine combustion. His 

HCCI combustion strategies and patents have assisted industry to improve fuel 

economy of clean gasoline and diesel cars by 15%-20%, while virtually eliminating 

NOx and particulate emissions. 

• Over the past 15 years, Dr. Assanis has led the efforts to revitalize the University of 

Michigan’s automotive engineering activities and transformed the Walter E. Lay 

Automotive Laboratory into a beehive of research activity (see the URL link: 
Assanis, 28 

http://me.engin.umich.edu/autolab/). He has initiated large-scale projects involving 

partnerships among academia, government and industry, led the fundraising efforts 

through writing major proposals, and directed the research activities. He has 

collaborated extensively with faculty members, research scientists and post-doctoral 

scholars from various Universities and disciplines. He has directed the research of 

more than 50 Ph.D. and more than 100 MS and M.Eng. graduate students. His

group’s research accomplishments have been published in over 250 articles in 

journals and international conference proceedings. His group’s engine and 

powertrain system simulations are used in industry, academia and government. 

 Grants and Contracts 

Dr. Assanis has been the project director, principal or co-principal investigator for 

more than $100M in grants and contracts funded by automotive industry (General 

Motors, Ford Motor Co., Chrysler LLC and DaimlerChrysler Corporation, 

Mitsubishi Motors Co., Honda Motor Co., Borg Warner, Ricardo), the heavy-duty 

truck industry (Detroit Diesel Corporation, Caterpillar, Inc., International, 

Cummins, Caterpillar, Yanmar Diesel Engine Co, Komatsu), the oil industry 

(ExxonMobil Corporation, Lubrizol, Amoco Oil, Chevron, Ethyl Corporation), 

the U.S. government (Department of Defense, Department of Energy, NASA, 

EPA, National Science Foundation) and National Laboratories (Sandia, Argonne). 

Major collaborative research partnerships he has led or co-led include: 

• Department of Energy, Office of Policy and International Affairs, “U.S.-

China Clean Energy Research Center - Clean Vehicle Consortium CERCCVC,” Sept. 2010-Sept. 2015. The strategic intent of the CERC-CVC is to 

forge a strong partnership between the U.S. and China, the largest 

greenhouse gas emitters and the largest existing and emerging vehicle 

markets, for breakthrough research and development. The CERC-CVC is 

led by the University of Michigan in partnership with Ohio State University, 

M.I.T., national labs (Sandia National Laboratories, Oak Ridge National 

Laboratory, Argonne National Laboratory, Joint BioEnergy Institute, 

Fraunhofer Institutes, Germany), and industry (Ford Motor Company, 

General Motors, Cummins Engine Co., Toyota Motor Co., Chrysler, 

Cummins, MAGNET, A123, American Electric Power, First Energy and the 

Transportation Research Center). The total value of the U.S. effort is nearly 

$30M, of which the US DOE will contribute $12.5M over a five-year 

period, and industry and academia will contribute $17M. The Chinese 

government will match the US effort with a $25M of funding to a 

consortium of Chinese academic partners, led by Tsinghua University, and 

industry.

• General Motors-University of Michigan Engine Systems Research 

Collaborative Research Laboratory (GM/UM ESR CRL). This successful 

research partnership between the two institutions, initiated in 1998 and 

currently in its third, five-year phase ($15M in total funding, 1998-2013) 

uses the special expertise of UM to conduct fundamental research into core 

competitive areas for GM in order to significantly improve fuel economy 
Assanis, 29 

and dramatically reduce emissions of next generation engines. The CRL has 

also motivated the growth and strengthening of additional areas of 

excellence of importance to GM and commensurate with the scholarly 

expertise and intellectual pursuits of the University faculty. As of December 

2010, Professor Assanis has stepped down as GM-UM ESR CRL Founding 

Co-Director to become the Founding Director for the United States Clean 

Vehicle Consortium, U.S.-China Clean Energy Research Center, 2010-2015. 

• UM$led!Multi$University!Consortium!on!Homogeneous!Charge!

Compression!Ignition!(HCCI)/!Low!temperature!Combustion!(LTC)

Engine!Research,!funded!since!2001!by!the!Department!of!Energy!

(approx.!$10M!of!funding!to!12/31/09).!!This!innovative!research!

holds!the!promise!of!delivering!high!fuel!economy!with!dramatically!

reduced!emissions!through!a!paradigm-shift approach compared to the 

traditional, high temperature, pollutant forming engine combustion in 

today's engines. University!of!Michigan!partners!include!Stanford,!MIT,!

and!UC!Berkeley.!! In!2011,!our consortium has won a third-phase DOE 

award (3 years, $3.75M) to explore high-pressure, lean burn (HPLB) 

combustion, with the potential to improve engine efficiency by 20-40%.

• Automotive Research Center, (ARC), a UM-led, eight-university, U.S. 

Army Center of Excellence founded in 1994 to advance the state-of-the-art 

modeling and simulation of military and civilian ground vehicles. The 

current third phase ($40M in funding, July 2004 – July 2010) emphasizes 

research into the design of vehicles propelled by next-generation 

powertrain systems for a variety of energy supply sources. The ARC is 

the most advanced university-based automotive research center in the 

country and has provided both educational opportunities and a unique 

cooperative partnership among the military, academia and the automotive 

industry. Current University partners include Clemson University, 

Oakland University, University of Alaska-Fairbanks, University of Iowa, 

Virginia Tech University, and Wayne State University. As of October 

2009, Professor Assanis has stepped down as ARC Director to become the 

Director of the Michigan Memorial Phoenix Energy Institute. 

Other Current Grants at The University of Michigan 

Department of Energy, Office for Energy Efficiency and Renewable Energy, 

Robert Bosch LLC, AVL Powertrain Engineering Inc., University of 

Michigan and Stanford University, “Advanced Combustion Controls – 

Enabling Systems and Solutions (ACCESS) for High Efficiency Light Duty 

Vehicles, $24,000,000, Project Director: Hakan Yilmaz (Bosch); Co-PI and 

Lead for Combustion Modeling: Dennis Assanis; my group’s portion of the 

budget $4,000,000 ($2,000,000 from DOE, $680,360 from Bosch, $480,000 

from AVL and $839,640 from UM), 4/1/2010- 6/30/2014. 

Department of Energy, Office for Energy Efficiency and Renewable Energy, 

“A University Consortium for Efficient and Clean High Pressure Lean Burn 
Assanis, 30 

Engines,” The University of Michigan in partnership with Massachusetts 

Institute of Technology and University of California-Berkeley, 10/1/09-

8/31/12, $3,750,000, Principal Investigator and Consortium Director. 

Collaborative Development of Clean Diesel Exhaust Aftertreatment System 

Through Modeling and Testing, Michigan Economic Development 

Corporation, 21st Century Jobs Fund, $1,650,000, 1/1/07-6/30/10, Principal 

Investigator (proposal selection process conducted by American Association 

for the Advancement of Science; 61 awards from 505 submitted proposals). 

General Motors R&D Center, “Modeling and Experimental Study of Boosted 

HCCI Engine,” 7/1/07-6/30/2011, $1,400,000, Principal Investigator.

Ford Motor Company, “EGR Cooler Fouling Research,” 4/1/10-12/31/11, 

$281,000, Principal Investigator.

U.S. Environmental Protection Agency, “Center for Engineering Excellence 

through Hybrid Technology,” 11/1/09-10/31/12, $1,560,000, Co-Principal 

Investigator; PI: Z. Filipi. 

University of Tennessee-Battelle, LLC., “Simulation of High Efficiency 

Stoichiometric GDI Combustion,” 5/1/10-4/30/11, $100,000, Principal 

Investigator. 

ConocoPhillips, Inc., “Fuel Effects on HCCI Combustion Limits,” 6/30/2011, 

$100,000, Principal Investigator. 

Michigan Public Service Commission, “Integrated Assessment of Feasibility 

and Deployment of Offshore Wind Technologies in the Great Lakes,” 1/1/11-

12/31/12, $800,000, Principal Investigator. 

Competed for 

National Science Foundation, “A Proposal for the Establishment of an 

Engineering Research Center for Carbon Neutral Vehicles (ERC-CNV)”, The 

University of Michigan in partnership with Massachusetts Institute of 

Technology, University of California-Berkeley, University of Illinois at 

Urbana-Champaign, Michigan State University, North Carolina A&T State 

University, 9/1/08-8/31/13, $18,500,000, Principal Investigator and ERC 

Director; invited among 34/143 pre-proposals to submit a full proposal, and 

reached site visit round of 8 finalists. 

 

 Past Grants 

Automotive Research Center (ARC) of Excellence in Modeling and 

Simulation of Ground Vehicles, Department of Defense: Phase I: 9/94-7/98, 
Assanis, 31 

$9,000,000, Co-Principal Investigator and Deputy Director (1/96-7/98); Phase 

II: 7/98-6/04, $25,000,000, Co-Principal Investigator (7/98-9/02) and 

Principal Investigator (9/02-6/04); Deputy Director (7/98 to 9/00) and 

Director (9/00-6/04).

Experimental Investigation of Heat Rejection Characteristics of I-4 and V-6 

Engine Designs, Ford Motor Co., 1/95 to 6/96, $142,000, Principal 

Investigator. 

Prediction of Engine Heat Rejection, Ford University Research Program, 

1995, $50,000 (unrestricted grant), Principal Investigator. 

Direct Injection of Natural Gas: In Cylinder CFD Computations, DOE/NASA, 

1/95 to 12/96, $214,506, Principal Investigator 

Engine Heat Transfer and Engine/Fuels Interaction Technology, Chevron 

Oronite Technology Group, 5/95 to 4/99, $8,000, Principal Investigator 

Engine Friction Studies with Boundary-Friction Reducing Additives, Mobil 

Technology Group and ExxonMobil Research and Engineering Company, 

1/96-8/15/00, Total Funding $919,362, ($183,540, 1/96-6/96; $135,822, 6/96-

5/97; $250,000, 1/97-12/97; $200,000, 1/98-12/98; $100,000, 1/99-6/99; 

$50,000, 1/00-8/00), Principal Investigator. 

Experimental Investigation of Heat Rejection Characteristics of Diesel Engine 

Designs, Ford Motor Co., 6/96-6/97, $20,000, Principal Investigator. 

Study of Unburned Hydrocaron Emissions Mechanisms, Ricardo, 1997, 

$90,000 (gift), Principal Investigator. 

Direct Injection of Natural Gas: In Cylinder CFD Computations, SANDIA, 

3/97-2/98, $25,000, Principal Investigator. 

Fuel Economy and Power Benefits of Cetane-Improved Fuels in Heavy-Duty 

Diesel Engines, Ethyl, 1997, $20,000 (gift), Principal Investigator. 

Investigation of Thermal and Strength Characteristics of Metal Matrix 

Composite Pistons for Heavy-Duty Diesel Engines, Focus Hope, 1997-98, 

$60,000, Principal Investigator. 

Effect of Metal Matrix Composite Liners on Engine Friction and Wear, Inco 

Limited, 1997-99, $50,000 (gift), Principal Investigator. 

Optimizing the Performance and Emissions of a Direct-Injection SparkIgnition Engine Using Multi-Dimensional Modeling, Honda Initiative Grant 

Program, 8/1/97-7/31/98, $25,000, Principal Investigator. 
Assanis, 32 

General Motors/UM Collaborative Research Laboratory (formerly Satellite 

Research Laboratory), 5/98-12/31/02, $5,000,000, GMCRL Co-Principal 

Investigator and Director, Advanced Powertrain Systems Division. 

Effect of Exhaust Valve Opening on Cold Start Hydrocarbon Emissions, Ford 

Motor Company, 6/98 to 12/01, Total Funding $380,000 ($230,000, 6/98-

12/99; $150,000, 1/00–12/00), Principal Investigator. 

Ricardo Single Cylinder Research Engines, Mobil Technology Company, 

9/1/98, $230,000 (gift), Principal Investigator. 

Optimizing the Performance and Emissions of Direct-Injection CompressionIgnition Engines Using Multi-Dimensional Modeling, EPA, 9/1/98-8/31/99, 

$40,000, Principal Investigator. 

Diesel Spray Combustion Modeling, Yanmar Diesel Engine Company, Japan, 

9/1/98, $27,000 (gift), Principal Investigator. 

Using Chemical Kinetics to Simulate Engine Performance and Emissions, 

Caterpillar, Inc., 1/1/99-12/31/99, $40,000 (gift), Principal Investigator. 

Mixture Preparation and Nitric Oxide Formation in a GDI Engine Studied by 

Combined Laser Diagnostics and Numerical Modeling DOE/Sandia National 

Laboratory, 4/1/1999-3 /31/2002, $383,505, Co-Principal Investigator. 

Development of Pressure Reactive Piston Technology for Improved 

Efficiency and Low NOx Emissions in Spark-Ignition (SI) and Compression 

Ignition (CI) Engines, Ford Motor Company/DOE PNGV Program, 10/12/99-

5/31/2003, $436,825, Principal Investigator. 

In Cylinder Pressure Sensors Using Thin Film Shape Memory Alloys, Orbital 

Research, 6/00-8/31/02, $120,000, Principal Investigator. 

Systems Approach for Demonstrating Very Low Nox Emissions from a 

Direct-Injection Compression-Ignition (CIDI) Engine with a NOx Catalyst, 

EPA, 1/01-6/30/02, $100,000, Principal Investigator. 

Concurrent Design of Next Generation Powertrains, Manufacturing Processes 

and Materials: A Simulation-Based Approach, US ARMY/TACOM under the 

Dual Use Science and Technology program DUST 2000, 4/3/01-4/2/03, 

$3,000,000, Co-Principal Investigator. 

Simulation-Based Design and Demonstration of Next Generation Advanced 

Diesel Technology, Ford Motor Company/US ARMY TACOM under the 

Dual Use Science and Technology program DUST 2001, $2,420,000, 9/1/01 

to 12/31/03, Principal Investigator. 

A University Consortium on Homogeneous Charge Compression Ignition, 

Low Temperature Combustion for High Efficiency, Ultra-Low Emission 
Assanis, 33 

Engines, The University of Michigan in partnership with Massachusetts 

Institute of Technology, Stanford University, and University of CaliforniaBerkeley, Department of Energy, Phase I: 10/1/01-3/31/06, $4,800,000, 

Principal Investigator and Consortium Director. 

General Motors/UM Collaborative Research Laboratory on Engine Systems 

Research, “Advanced Diesel Combustion System Optimization Tools 

Implementation,” 6/1/04-8/31/04, $17,160, Principal Investigator and 

GMCRL Co-Director. 

General Motors/UM Collaborative Research Laboratory on Engine Systems 

Research, “Advanced Diesel Combustion System Development and 

Measurement of Hydrocarbon Species and Unregulated Emissions from 

Diesel Engines Operating in Advanced Combustion Modes,” 9/1/03-8/31/04, 

$116,206, Principal Investigator and GMCRL Co-Director. 

General Motors/UM Collaborative Research Laboratory on Engine Systems 

Research, “Experimental Assessment of Design Concepts for Robust SprayGuided Stratified-Charge Combustion,” 8/1/04-7/31/05, $135,168, Principal 

Investigator and GMCRL Co-Director. 

Precision Heat Management in SI Engines, DaimlerChrysler Challenge Fund 

Project, $180,000, 9/1/01 to 12/31/04. 

Detailed Exhaust Hydrocarbon Measurements in a Multi-Cylinder Engine, 

Ford Motor Company, 9/1/03 to 8/31/05, $98,000, Principal Investigator. 

Engine-In-Vehicle Modeling, Navistar, 1/1/99-12/06, $300,000, unrestricted 

grant, Co-Principal Investigator. 

General Motors/UM Collaborative Research Laboratory on Engine Systems 

Research, “PCCI Diesel Engine Combustion and Aftertreatment Systems,” 

9/19/2006, $85,000, unrestricted grant, Principal Investigator. 

Fuel Processors for PEM Fuel Cells, Department of Energy, 10/01-9/06, 

$4,545,471, Co-Principal Investigator. 

Eaton Corporation Innovation Center, “Assessment of the NOx Reducing 

Potential of NOx Adsorber-NH3 SCR Exhaust Aftertreatment Systems,” 

Phase I: 7/1/04 to 6/30/05, $114,876; Phase II: 7/1/05-12/31/06, $60,000, 

Principal Investigator. 

General Motors/UM Collaborative Research Laboratory on Engine Systems 

Research, “Discovery Project: Free Piston Linear Alternator,” 6/1/05-8/31/07, 

$528,245, Principal Investigator. 

Investigation of VVT Fuel Economy and Emissions Benefits under ColdStart, Idle and Low Load Conditions, DaimlerChrysler Challenge Fund 

Project, 1/1/05 to 6/30/08, $300,000, Principal Investigator. 
Assanis, 34 

U.S. Environmental Protection Agency, “Integrated Hydraulic Hybrid 

Propulsion System and Advanced Components for Maximizing Fuel 

Efficiency and Emissions Benefits,” 4/2006-10/2009, $226,000, Co-Principal 

Investigator; PI: Z. Filipi. 

Advanced Powertrain Modeling, Borg Warner, 1/06-6/10, $300,000, Principal 

Investigator. 

Ford Motor Company, “Development of Diesel EGR Cooler Fouling Model,” 

Ford-UM Alliance, 9/1/07-12/31/09, $200,000, Principal Investigator.

 Grants and Contracts at University of Illinois in Urbana-Champaign 

Effect of Combustion Chamber Insulation on Turbocharged Diesel Engine 

Performance, UIUC-Research Board, 3/20/86 - 6/30/87, $20,000 (grant), 

Principal Investigator 

 

Intake Valve Event Optimization for Specified Engine Operating Conditions, 

General Motors Pontiac Group, 8/21/86 to 6/30/88, $31,000, Co-Principal 

Investigators: J. E. Peters and D.N. Assanis, Project Director: D.N. Assanis 

Development of a Modern Engine Test Cell for Studies of Low-HeatRejection Engine Performance, UIUC-Research Board, $6,000 (grant), 

1/15/87 to 1/15/88, Principal Investigator 

NSF, An Experimental and Analytical Study of Unsteady Heat Transfer in 

Low-Heat-Rejection Engine Combustion Chambers, $69,983, 7/1/87 to 

11/30/89, Principal Investigator 

Development of an Integrated Rankine Bottoming Cycle for Diesel Engine 

Exhaust Heat Recovery, UIUC-Research Board, $7,624 (grant), 8/21/87 to 

5/21/88, Principal Investigator 

Adiabatics, Inc., Development and Use of a Computer Simulation Code for 

LHR Vehicle Fuel Economy, $30,926, 9/1/87 to 7/31/88, Co-Principal 

Investigators: D. N. Assanis, R. A. White, Project Director: D.N. Assanis 

Analysis and Testing of Ceramic-Coated Engine Components, Adiabatics, 

Inc., $14,466, 9/1/87 to 12/31/88, Principal Investigator 

Fluidized Bed Heat Recovery from Diesel Engines, U.S. Army CERL, 

$13,692, 9/15/87 - 5/31/88, Principal Investigator 

Engineering Research Equipment Grant: A Modern Single-Cylinder Engine 

Test Facility for Diesel Engine Research, NSF, $51,400 (equipment grant), 

from 5/1/88 to 10/31/89, Principal Investigator 
Assanis, 35 

Presidential Young Investigator Award: Engine Combustion and Emissions 

Studies, NSF, $312,500, 6/88 to 12/93, Principal Investigator 

A Modern Single Cylinder Diesel Research Engine, Caterpillar, $27,000 

(gift), 7/7/88, Principal Investigator 

Development of Multi-Dimensional Heat Transfer Models for LHR Engine 

Studies, National Center for Supercomputing Applications, 35 CPU hours on 

CRAY X/MP, 3/88 to 12/89, Principal Investigator 

Combustion and Emissions of Low-Heat-Rejection Diesel Engines, $129,223, 

U.S. Army TACOM, 8/88 to 8/90, Principal Investigator 

The Effect of Light Weight Reciprocating Components on Engine 

Combustion, Frictional Losses, and Heat Transfer, Chrysler, 8/88 - 8/90, 

$115,992, Principal Investigator 

An Optical Table for Laser Velocimetry, $6,311 (gift), Newport Corp., from 

4/89, Principal Investigator 

Support for Women, Minorities, and Disabled Engineering Research 

Assistants, NSF, 2/89 - 2/90, $4,958, Principal Investigator 

Development of an Improved Combustion Model for Use in a Multidimensional Engine Simulation, National Center for Supercomputing 

Applications, 90 CPU hours on CRAY X/MP and CRAY 2, 12/89 - 12/90, 

Principal Investigator 

An Experimental and Analytical Study of Unsteady Heat Transfer in LHR 

Engines - REU Supplement, NSF, 2/1/90 to 7/31/90, $8,973, Principal 

Investigator 

Investigation of a Fluidized Bed Heat Exchanger, U.S. Army CERL, 8/90 to 

5/91, $16,935, Principal Investigator 

Development of a Hydrocarbon Emissions Model for Multi-Dimensional 

Engine Simulation, National Center for Supercomputing Applications, 80 

CPU hours on CRAY X/MP and CRAY 2, 4/90 - 4/91, Principal Investigator 

Effect of Reed Valves in the Intake Ports on SI Engine Performance and 

Knock, Ford Motor Company, 8/21/90 to 12/93, $169,377, Co-Principal 

Investigators: D.N. Assanis, J. E. Peters, R. A. White, Director: D. N. Assanis 

A Study of Fuel-Air Distribution in the Intake System of a Spark-Ignited 

Natural Gas Engine, Cummins, 8/21/90 - 5/31/94, $140,000 (gift), CoPrincipal Investigators: D. N. Assanis, R. A. White 

 
Assanis, 36 

Lignin-Augmented Bituminous Coal Depolymerization: A Route to Clean 

Fuels, Center for Research on Sulfur in Coal, $105,036, Co-PI, 8/21/90 to 

8/31/91, Co-Principal Investigators: D. N. Assanis, C. Kruse, PD: C. Kruse 

Prediction of 3-D Turbulent Flows Using a BFC Computer Code, National 

Center for Supercomputing Applications, $24,000 and 50 CPU hours on 

CRAY 2, 9/90 - 8/92, Principal Investigator 

Joint Research Program between Mitsubishi Motors Corp. and University of 

Illinois, Mitsubishi Motors Corp., $340,000 6/1/91 to 5/31/93, Co-Principal 

Investigators: D. N. Assanis, R. A. White, H. Sehitoglu, D. Socie, Project 

Director: D. N. Assanis 

Octane Requirement Increase and its Relation to Combustion Chamber 

Deposits, Amoco Oil Company, $130,798, 9/1/91to 12/93, Co-Principal 

Investigators: D. N. Assanis, R. A. White, Project Director: R. A. White 

Integrated Production/Use of Ultra Low Ash Coal, Center for Research on 

Sulfur in Coal, $148,959, Co-PI, 8/91- 8/92, Co-Principal Investigators: D. N. 

Assanis, C. Kruse, Project Director: C. Kruse 

Development, Optimization, and Testing of a 3-D Computational Fluid 

Dynamics Code, National Center for Supercomputing Applications, 96 hours 

on CRAY Y-MP, 11/91 to 12/92, Principal Investigator 

A Modern Set of Emissions Analyzers for Internal Combustion Engine 

Pollution Studies, UIUC Research Board, $42,000 (grant), 10/91, PI 

Development of a Comprehensive Evaporation Model for Use in a MultiDimensional Engine Simulation, National Center for Supercomputing 

Applications, 85 CPU hours on CRAY X/MP and CRAY 2, 11/92 - 12/93, 

Principal Investigator 

Effects of Combustion Characteristics on Heat Loss under Knocking and NonKnocking Conditions, Mitsubishi Motor Company, 6/93 - 5/95, $200,085, CoPrincipal Investigator: D. N. Assanis 

An Improved Model for Droplet Evaporation in High Pressure Diesel Sprays, 

UIUC Research Board, $6,728 (grant), 6/93 to 12/93, Principal Investigator 

Design of Low Distortion Insulated Piston/Liner System, Inco Ltd., $25,000 

(gift), from 8/93 - 8/95, Principal Investigator 

RISC-6000 Workstations for Computation and Visualization of Reactive 

Engine Flows, IBM, $39,888 (gift), from 12/93, Co-Principal Investigators: D. 

N. Assanis, R. A. White 
Assanis, 37 

Direct Injection of Natural Gas: In Cylinder CFD Computations, DOE/NASA, 

1/94 to 12/94, $231,174, Co-Principal Investigators: D. N. Assanis, J. E. 

Peters, R. L. Lucht, Project Director: D.N. Assanis 

Direct Injection of Natural Gas: In Cylinder Laser Measurements, GRI, 1/94 

to 12/96, $488,178, Co-Principal Investigators: D. N. Assanis, J. E. Peters, R. 

L. Lucht, Project Director: R.L. Lucht 

Prediction of Engine Heat Rejection, Ford University Research Program, from 

1/94, $50,000 (grant), Principal Investigator 

 

Evaluation of Hydrated Ethanol for DI Compression Ignition Engines, Illinois 

Department of Energy and Natural Resources, 1/94 to 6/96, $60,000 per year, 

Co-Principal Investigators: D. N. Assanis, C. Goering. 

Publications 

 Articles in Refereed Journals, Transactions or Archives 

1. D. N. Assanis, and J. B. Heywood, "Development and Use of a Computer 

Simulation of the Turbocompound Diesel System for Engine Performance and 

Component Heat Transfer Studies," selected for SAE 1986 Transactions, 95:2, 

2.451-2.476, 1987. (Presented as SAE Paper 860329, SAE International 

Congress and Exposition, Detroit, MI, Feb. 24-28, 1986; and included in The 

Adiabatic Diesel Engine: Global Developments, SAE Special Publication 650, 

95-120, 1986.)

2. Assanis, D. N., and Heywood, J. B., "Simulation Studies of the Effects of LowHeat-Rejection on Turbocompound Diesel Engine Performance," International 

Journal of Vehicle Design, 8:3, 282-299, 1987. (Based on Presentation at 3rd 

International Conference on Turbocharging and Turbochargers, Institute of 

Mechanical Engineers, London, United Kingdom, May 6-8, 1986.)

3. Assanis, D. N., and E. Badillo, "Transient Heat Conduction in Low-Heat 

Rejection Engine Combustion Chambers," selected for SAE 1987 Transactions,

96:4, 4.82-4.92, 1988. (Presented as SAE Paper 870156, SAE International 

Congress and Exposition, Detroit, MI, Feb. 23-27, 1987; and included in 

Adiabatic Engines and Systems, SAE Special Publication 700, 153-163, 1987.)

4. Assanis, D. N., and E. Badillo, "Transient Analysis of Piston-Liner Heat 

Transfer in Low-Heat-Rejection Diesel Engines," selected for SAE 1988 

Transactions: Journal of Engines, 97:6, 6.295-6.305, 1989. (Presented as SAE 

Paper 880189, SAE International Congress and Exposition, Detroit, MI, Feb. 

29-March 4, 1988; and included in Recent Developments in the Adiabatic 

Engine, SAE Special Publication 738, 97-107, 1988.)

5. Assanis, D. N., "Effect of Combustion Chamber Insulation on the Performance 

of a Low-Heat-Rejection Diesel Engine with Exhaust Heat Recovery," Journal 

of Heat Recovery Systems & Combined Heat and Power, 9:5, 475-484, 1989. 

(Based on Paper 869486, presented at 21st Intersociety Energy Conversion 

Engineering Conference, San Diego, CA, Aug. 25-29, 1986.)
Assanis, 38 

6. Assanis, D. N., and E. Badillo, "On Heat Transfer Measurements in Diesel 

Engines using Co-Axial Fast-Response Thermocouples," ASME Transactions: 

Journal of Engineering for Gas Turbines and Power, 111:3, 458-465, 1989. 

(Presented at ASME-ETCE Technical Conference, Houston, TX, Jan. 22-25, 

1989; and included in Basic Processes in Internal Combustion Engines, ICE6, 25-32, 1989.)

7. Assanis, D. N., "Thin Thermal Barrier Coatings for Internal Combustion 

Engine Components," International Journal of Materials and Product 

Technology, 4:3, 232-243, 1989. (Presented with R. Kamo and W. Bryzik as 

SAE Paper 890143, SAE International Congress and Exposition, Detroit, MI, 

Feb. 27 - March 3, 1989 and selected for SAE 1989 Transactions: Journal of 

Engines, 98:3, 131-136, 1990.) 

8. Phillips, A., and D. N. Assanis, "A PC-Based Vehicle Powertrain Simulation 

for Fuel Economy and Performance Studies," International Journal of Vehicle 

Design, 10:6, 639-658, 1989. (An improved version of the simulation was 

presented with A. Phillips and P. Badgley in SAE Paper 900619, SAE 

International Congress and Exposition, Detroit, MI, Feb. 26-March 2, 1990; 

and selected for SAE 1990 Transactions: Journal of Passenger Cars, 99:6, 

1991.)

9. Assanis, D. N. and M. Polishak, "Valve Event Optimization in a Spark-Ignition 

Engine," International Journal of Vehicle Design, 10:6, 625-638, 1989. 

(Presented at ASME-ICED Technical Conference, Dearborn, MI, Oct. 15-18, 

1989; and selected for ASME Transactions: Journal of Engineering for Gas 

Turbines and Power, 112:3, 341-347, 1990.)

10. Assanis, D. N., and E. Badillo, "Evaluation of Alternative Thermocouple 

Designs for Transient Heat Transfer Measurements in Metal and Ceramic 

Engines," selected for SAE 1989 Transactions: Journal of Engines, 98:3, 1036-

1051, 1990. (Presented as SAE Paper 890571, SAE International Congress and 

Exposition, Detroit, MI, Feb. 27 - March 3, 1989; and included in Worldwide 

Progress on Adiabatic Engines, SAE Special Publication 785, 169-184, 1990.)

11. Tamamidis, P., and D. N. Assanis, "Generation of Orthogonal Grids with 

Control of Spacing," Journal of Computational Physics, 94:2, 437-453, 1991. 

12. Sekar, R. R., W. W. Marr, D. N. Assanis, R. L. Cole, T. J. Marciniak, and J. E. 

Schaus, "Oxygen Enriched Diesel Engine Performance: A Comparison of 

Analytical and Experimental Results," ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 113:3, 365-369, 1991. (Presented at 

ASME-ICED Technical Conference, Rockford, IL, Oct. 1990; and included in 

New Technology in Large Bore Engines, ICE-13, 57-62, 1990.)

13. Filipi, Z., and D. N. Assanis, "Quasi-Dimensional Computer Simulation of the 

Turbocharged Spark-Ignition Engine and its Use for Two and Four Valve 

Engine Matching Studies," selected for SAE 1991 Transactions: Journal of 

Engines, 100:3, 52-68, 1992. (Presented as SAE Paper 910075, SAE 

International Congress and Exposition, Detroit, MI, Feb. 25-March 1, 1991.)

14. Assanis, D. N., Wiese, K., Schwarz, E., and W. Bryzik, "The Effects of 

Ceramic Coatings on Diesel Engine Performance and Exhaust Emissions," 

selected for SAE 1991 Transactions: Journal of Engines, 100:3, 657-665, 1992. 
Assanis, 39 

(Presented as SAE Paper 910460, SAE International Congress and Exposition, 

Detroit, MI, Feb. 25-March 1, 1991.)

15. Varnavas, C., and D. N. Assanis, "The Effects of Spray, Mixing, and 

Combustion Model Parameters on KIVA-II Predictions," selected for SAE 

1991 Transactions: Journal of Engines, 1488-1497, 100:3, 1992. (Presented as 

SAE Paper 911785, SAE International Off-Highway and Powerplant Congress, 

Milwaukee, WI, Sept. 9-12, 1991.)

16. Shih, L., and D. N. Assanis, "Implementation of a Fuel Spray Wall Interaction 

Model in KIVA-II," selected for SAE 1991 Transactions: Journal of Engines, 

100:3, 1498-1512, 1992. (Presented as SAE Paper 911787, SAE International 

Off-Highway and Powerplant Congress, Milwaukee, WI, Sept. 9-12, 1991.)

17. Yerramareddy, S., Tcheng, D. T., Lu, S. C-Y., and D.N. Assanis, "Creating and 

Using Models for Engineering Design: A Machine Learning Approach," IEEE 

Expert, Special Track on Machine Learning, 52-59, June 1992.

18. Assanis, D.N., "The Effect of Thin Ceramic Coatings on Petrol Engine 

Performance and Emissions," International Journal of Vehicle Design, 13:4, 

378-388, 1992. (Based on SAE Paper 900903, presented with T. Mathur at 

SAE 41st Annual Earthmoving Industry Conference, Peoria, IL, April 3-5, 

1990; and selected for SAE 1990 Transactions: Journal of Materials and 

Manufacturing, 99:5, 1991.)

19. Assanis, D. N., and F. A. Friedmann, "A Thin-Film Thermocouple for 

Transient Heat Transfer Measurements in Ceramic-Coated Combustion 

Chambers," International Communications in Heat and Mass Transfer, 20, 

459-468, 1993.

20. Karvounis, E., and D. N. Assanis, "The Effect of Inlet Flow Distribution on 

Catalytic Conversion Efficiency", International Journal of Heat and Mass 

Transfer, 36:6, 1495-1504, 1993.

21. Tamamidis, P., and D. N. Assanis, "Evaluation of Various High Order 

Schemes With and Without Flux Limiters," International Journal for 

Numerical Methods in Fluids, 16, 931-948, 1993.

22. Tamamidis, P., and D. N. Assanis, "Three Dimensional Incompressible Flow 

Calculations with Alternative Discretization Schemes," Numerical Heat 

Transfer, Part B, 24, 57-76, 1993. 

23. Tamamidis, P., and D. N. Assanis, "Prediction of Three-Dimensional Steady 

Incompressible Flows using Body-Fitted Coordinates," ASME Transactions: 

Journal of Fluids Engineering, 115, 457-462, 1993. (Based on paper presented 

at ASME-WAM Symposium on Multidisciplinary Applications of 

Computational Fluid Mechanics, Atlanta, GA, Dec. 1-6, 1991.)

24. Assanis, D. N., Karvounis, E., Sekar, R., and W. Marr, "Heat Release Analysis 

of Oxygen-Enriched Diesel Combustion," ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 115, 761-768, 1993. (Presented as 

ASME Paper 93-ICE-8, ASME-ETCE Technical Conference, Houston, TX, 

Jan. 31- Feb. 3, 1993.)
Assanis, 40 

25. Karvounis, E. and D. N. Assanis, "A Novel Methodology for Engine Design 

and Optimization," International Journal of Vehicle Design, 14:3, 261-277, 

1993.

26. Karvounis, E. and D. N. Assanis, "FIND: A Framework for Intelligent Design," 

SAE 1993 Transactions: Journal of Engines, 102:3, 1605-1620, 1994. 

(Presented as SAE Paper 931180, SAE Earthmoving Conference, Peoria, IL, 

April 20-21, 1993.)

27. Baker, D., and D. N. Assanis, "Multi-Dimensional Finite Element Code for 

Transient Heat Transfer Calculations," Numerical Heat Transfer, Part B, 25:4, 

395-414, 1994.

28. Baker, D., and D. N. Assanis, "A Methodology for Coupled Thermodynamic 

and Heat Transfer Analysis of a Diesel Engine," Applied Mathematical 

Modeling, 18, 590-601, 1994. 

29. Tamamidis, P., and D. N. Assanis, "Optimization of Inlet Port Design in a 

Uniflow-Scavenged Engine Using a 3-D Turbulent Flow Code," SAE 1993 

Transactions: Journal of Engines, 102:3, 1621-1633, 1994. (Presented as SAE 

Paper 931181, SAE Earthmoving Conference, Peoria, IL, April 20-21, 1993.)

30. Shih, L., and D. N. Assanis, "Effect of Ring Dynamics and Crevice Flows on

Unburned Hydrocarbon Emissions,” ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 116:4, 784-792, 1994. (Presented at 

ASME-ICED Fall Technical Conference, Morgantown, WV, September 26-29, 

1993; and included in Alternate Fuels, Engine Performance and Emissions, 

ICE-20, 195-206, 1993.)

31. Mavinahally, N., Assanis, D. N., Govinda Mallan, K.R., and K. V. 

Gopalakrishnan, "Torch Ignition: Ideal for Lean Burn Premixed-Charge 

Engines," ASME Transactions: Journal of Engineering for Gas Turbines and 

Power, 116:4, 793-798, 1994. (Presented as ASME Paper 94-ICE-6, ASME 

ETCE Conference, New Orleans, LA, January 23-26, 1994.)

32. Nakic, D., Assanis, D. N., and R. A. White, "Effect of Elevated Piston 

Temperature on Combustion Chamber Deposit Growth," SAE 1994 

Transactions, 103:3, 1454-1466, 1995. (Presented as SAE Paper 940948, SAE 

International Congress and Exposition, Detroit, MI, March 1-5, 1994.)

33. Papageorgakis, G., and Assanis, D.N., "A Spray Breakup Model for Low 

Injection Pressures," International Communications in Heat and Mass 

Transfer , 23 (1), 1-10, 1996. (Based on ATA Paper 94A1097, New Design 

Frontiers for More Efficient, Reliable, and Ecological Vehicles, Vol. 2, pp. 

793- 802, presented at 4th International Conference Florence ATA 1994, 

March 16-18, 1994.)

34. Tamamidis, P., Zhang, G., and D. N. Assanis, "Comparison of Pressure-Based 

and Artificial Compressibility Methods for Solving 3-D Steady Incompressible 

Flows," Journal of Computational Physics, 124, 1-13, 1996.

35. Zhang, G., Assanis, D. N., and Tamamidis, P., "Segregated Prediction of 3-D 

Compressible Subsonic Fluid Flows Using Collocated Grids," Numerical Heat 

Transfer, Part A, 29:757-775, 1996.
Assanis, 41 

36. Bohac, S., Baker, D., and D. N. Assanis, "A Global Model for Steady-State and 

Transient S.I. Engine Heat Transfer Studies," SAE 1996 Transactions: Journal 

of Engines. (Presented as SAE Paper 960073, 1996 SAE International 

Congress, Detroit, MI, February 26-29, 1996.)

37. Syrimis, M., Shigahara, K., and D. N. Assanis, "Correlation between Knock 

Intensity and Heat Transfer under Light and Heavy Knocking Conditions in a 

Spark Ignition Engine," SAE 1996 Transactions: Journal of Engines. 

(Presented as SAE Paper 960495, 1996 SAE International Congress, Detroit, 

MI, February 26-29, 1996.)

38. Sun, X., Assanis, D. N., and G. Brereton, "Assessment of Alternative Strategies 

for Reducing Hydrocarbon and Carbon Monoxide Emissions from Small TwoStroke Engines," SAE 1996 Transactions: Journal of Engines. (Presented as 

SAE Paper 960743, 1996 SAE International Congress, Detroit, MI, February 

26-29, 1996.)

39. Badillo, E., Assanis, D. N., and H. Servati, “One-Dimensional Transient 

Dynamics of Fuel Evaporation and Diffusion in Induction Systems,” SAE 1997 

Transactions: Journal of Engines. (Presented as SAE Paper 970058, 1997 

SAE International Congress and Exposition, Detroit, MI, February 24-27, 

1997.)

40. Alsterfalk, M., Filipi, Z. S., and D. N. Assanis, “The Potential of the Variable 

Stroke Spark-Ignition Engine,” SAE 1997 Transactions: Journal of Engines. 

(Presented as SAE Paper 970067, 1997 SAE International Congress and 

Exposition, Detroit, MI, February 24-27, 1997.)

41. Syrimis, M., and D. N. Assanis, “Piston Heat Transfer Measurements Under 

Varying Knock Intensity in A Spark-Ignition Engine,” SAE 1997 Transactions: 

Journal of Engines. (Presented as SAE Paper 971667, 1997 SAE International 

Fuels and Lubricants Meeting, Dearborn, MI, May 5-8, 1997).

42. Murrell, J. D., Lewis, G. M., Baker, D. M., and D. N. Assanis, “An EarlyDesign Methodology for Predicting Transient Fuel Economy and Catalyst-Out 

Exhaust Emissions,” SAE 1997 Transactions: Journal of Engines. (Presented 

as SAE Paper 971838, Vehicle Thermal Management Systems VTMS-3 

International Conference, Indianapolis, IN, May 19-22, 1997.)

43. Green, G. J., Henly, T. J., Starr, M. E., Assanis, D. N., Syrimis, M., and F. 

Kanafani, "Fuel Economy and Power Benefits of Cetane-Improved Fuels in 

Heavy-Duty Diesel Engines,” SAE 1997 Transactions: Journal of Fuels and 

Lubricants. (Presented as SAE Paper 972900, SP-1302, SAE International Fall 

Fuels and Lubricants Meeting, Tulsa, Oklahoma, October 13-16, 1997.)

44. Syrimis, M., and D. N. Assanis, "The Effect of the Location of Knock 

Initiation on Heat Flux into an SI Combustion Chamber,” SAE 1997 

Transactions: Journal of Engines. (Presented as SAE Paper 972935, SP-1300, 

SAE International Fall Fuels and Lubricants Meeting, Tulsa, Oklahoma, 

October 13-16, 1997.)
Assanis, 42 

45. Zhang, G., Filipi, Z. S., and D. N. Assanis, “A Flexible, Reconfigurable, 

Transient Multi-Cylinder Diesel Engine Simulation for System Dynamic 

Studies,” Mechanics of Structures and Machines, 25(3), 357-378, 1997.

46. Agarwal, A., Filipi, Z. Assanis, D. N., and D. Baker, “Assessment of Singleand Two-ZoneTurbulence Formulations for Quasi-Dimensional Modeling of 

Spark Ignition Engine Combustion,” Combustion Science and Technology, 

136: 13-39, 1998.

47. Anderson, M., Assanis, D.N., and Filipi, Z. S., “First and Second Law 

Analyses of a Naturally-Aspirated, Miller Cycle, SI Engine with Late Intake 

Valve Closure,” SAE 1998 Transactions: Journal of Engines. (Presented as 

SAE Paper 980889, SAE International Congress and Exposition, Detroit, MI, 

Feb. 23-26, 1998.)

48. Papageorgakis, G., and Assanis, D.N., “Optimizing Gaseous Fuel-Air Mixing 

in Direct Injection Engines Using an RNG-Based k-ε Model,” SAE 1998 

Transactions: Journal of Engines. (Presented as SAE Paper 980135, SAE 

International Congress and Exposition, Detroit, MI, Feb. 23-26, 1998.)

49. Papageorgakis, G., and Assanis, D.N., “Comparison of Linear and Non-Linear 

RNG-Based k-ε models for Incompressible Turbulent Flows,” Numerical Heat 

Transfer, Part B, 35: 1-22, 1999.

50. Assanis, D. N., Delagrammatikas, G., Fellini, R., Filipi, Z. S., Liedtke, J., 

Michelena, N., Papalambros, P., Reyes, D., Rosenbaum, D., Sales, A., Sasena, 

M., “Optimization Approach to Hybrid Electric Propulsion System Design,” 

Mechanics of Structures and Machines, 27(4), 393-421, 1999.

51. Assanis, D. N., Bryzik, W., Castanier, M. P., Darnell, I. M., Filipi, Z. S., 

Hulbert, G. M., Jung, D., Ma, Z., Perkins, N. C., Pierre, C., Scholar, C. M., 

Wang, Y., Zhang, G., “Modeling and Simulation of an M1 Abrams Tank with 

Advanced Track Dynamics and Integrated Virtual Diesel Engine,” Mechanics 

of Structures and Machines, 27(4), 453-505, 1999.

52. Assanis, D. N., Bryzik, W., Chalhoub, N., Filipi, Z., Henein, N., Jung, D., Liu, 

X., Louca, L., Moskwa, J., Munns, S., Overholt, J., Papalambros, P., Riley, S., 

Rubin, Z., Sendur, P., Stein, J., and G. Zhang, “Integration and Use of Diesel 

Engine, Driveline and Vehicle Dynamics Models for Heavy Duty Truck 

Simulation,” selected for 1999 SAE Transactions: Journal of Engines. 

(Presented as SAE Paper 1999-01-0970, SAE International Congress and 

Exposition, Detroit, MI, March 1-4, 1999.)

53. Lee, K. S., Assanis, D. N., Lee, J. H., and K. M. Chun, “Measurements and 

Predictions of Steady-State and Transient Stress Distributions in a Diesel 

Engine Cylinder Head,” selected for 1999 SAE Transactions: Journal of 

Engines. (Presented as SAE Paper 1999-01-0973, SAE International Congress 

and Exposition, Detroit, MI, March 1-4, 1999.)

54. Assanis, D. N., Hong, S. J., Nishimura, A., Papageorgakis, G., and B. 

VanZieleghem, “Studies of Spray Breakup and Mixture Stratification in a 

Gasoline Direct Injection Engine Using KIVA-3V,” ASME Transactions: 

Journal of Gas Turbines and Power, 122:3, 485-492, 2000. (Presented at 

ASME-ICE Spring Technical Conference, Columbus, IN, April 24-28, 1999.)
Assanis, 43 

55. Filipi, Z. S., and D. N. Assanis, “The Effect of the Stroke-to-Bore Ratio on 

Combustion, Heat Transfer and Performance of a Homogeneous Charge SI 

Engine of Given Displacement,” International Journal of Engine Research,

1:2, 191-208, 2000.

56. Assanis, D.N., Filipi, Z. S., Gravante, S., Grohnke, D., Gui, X., Louca, L., 

Rideout, G., Stein, J., and Wang., Y., “Validation and Use of SIMULINK 

Integrated, High Fidelity, Engine-In-Vehicle Simulation of the International 

Class VI Truck,” selected for 2000 SAE Transactions: Journal of Engines. 

(Presented as SAE Paper 2000-01-0288, included in Vehicle and Engine 

Systems Models, SP-1527, SAE 2000 World Congress, Detroit, MI, March 6-9, 

2000.)

57. Fiveland, S.B., and D. N. Assanis, “A Four-Stroke Homogeneous Charge 

Compression Ignition Engine Simulation for Combustion and Performance 

Studies,” selected for 2000 SAE Transactions: Journal of Engines. (Presented 

as SAE Paper 2000-01-0332, included in Compression Ignition Combustion 

Processes, SP-1530, SAE 2000 World Congress, Detroit, MI, March 6-9, 

2000.)

58. Panagiotidis, M., Delagrammatikas, G., and D. N. Assanis, “Development and 

Use of a Regenerative Braking Model for a Parallel Hybrid Electric Vehicle,” 

selected for 2000 SAE Transactions: Journal of Engines. (Presented as SAE 

Paper 2000-01-0995, SAE 2000 World Congress, Detroit, MI, March 6-9, 

2000.)

59. Assanis, D.N., Filipi, Z.S., Fiveland, S.B., and Syrimis, M., “A Methodology 

for Cycle-by-Cycle Transient Heat Release Analysis in a Turbocharged Direct 

Injection Diesel Engine,” selected for 2000 SAE Transactions: Journal of 

Engines. (Presented as SAE Paper 2000-01-1185, and included in 

Compression Ignition Combustion Processes, SP-1530, SAE 2000 World 

Congress, Detroit, MI, March 6-9, 2000.

60. Noorman, M.T., Assanis, D. N., Patterson, D., Tung, S. C., and Tseregounis, 

S., “Overview of Techniques for Measuring Friction Using Bench Tests and 

Fired Engines," selected for 2000 SAE Transactions: Journal of Fuels and 

Lubricants. (Presented as SAE Paper 2000-01-1780, and included in Advances 

in Powertrain Tribology, SP-1548, SAE 2000 Fuels and Lubricants 

International Conference, Paris, France, June 19-22, 2000.)

61. Agarwal, A. and Assanis, D. N., “Multi-Dimensional Modeling of Ignition, 

Combustion and Nitric Oxide Formation in Direct Injection Natural Gas 

Engines," selected for 2000 SAE Transactions: Journal of Fuels and 

Lubricants. (Presented as SAE Paper 2000-01-1839 and included in Novel SI 

and CI Combustion Systems, SP-1549, SAE 2000 Fuels and Lubricants 

International Conference, Paris, France, June 19-22, 2000.)

62. Lee, K.S., and D. N. Assanis, “Thermo-Mechanical Analysis of Optically 

Accessible Quartz Cylinder Under Fired Engine Operation,” International 

Journal of Automotive Technology, Vol. 1, No. 2, 79 -87, 2000.

63. Assanis, D. N., Poola, R., Sekar, R., and G. R. Cataldi, “Study of Using 

Oxygen-Enriched Combustion Air for Locomotive Diesel Engines,” ASME 
Assanis, 44 

Transactions: Journal of Gas Turbines and Power, 123:1, 157-166, 2001. 

(Presented in Diamond Anniversary Conference of the ASME-ICE Division, 

Fairborn, OH, October 20-23, 1996.)

64. Filipi, Z. S., and D. N. Assanis, “A Non-Linear, Transient, Single-Cylinder 

Diesel Engine Simulation for Predictions of Instantaneous Engine Speed and 

Torque,” ASME Transactions: Journal of Gas Turbines and Power,123:4, 

951-959, 2001. 

65. Agarwal, A., and D. N. Assanis, “Multi-Dimensional Modeling of Natural Gas 

Autoignition using Detailed Chemical Kinetics,” Combustion Science and 

Technology, 163: 177-210, 2001.

66. Fiveland, S. B. and D. N. Assanis, “Development of a Two-Zone HCCI 

Combustion Model Accounting for Boundary Layer Effects,” selected for 2001 

SAE Transactions: Journal of Engines. (Presented as SAE Paper 2001-01-

1028, SAE World Congress, Detroit, MI, March 5-8, 2001.)

67. Delagrammatikas, G. J., and D. N. Assanis, “The Reverse Engineering of a 

Turbocharged Diesel Engine through a Unified Systems Approach,” selected 

for 2001 SAE Transactions: Journal of Engines. (Presented as SAE Paper 

2001-01-1244, SAE World Congress, Detroit, MI, March 5-8, 2001.)

68. Jung, D. and D. N. Assanis, “Multi-Zone DI Diesel Spray Combustion Model

for Cycle Simulation Studies of Engine Performance and Emissions,” selected 

for 2001 SAE Transactions: Journal of Engines. (Presented as SAE Paper 

2001-01-1246, SAE World Congress, Detroit, MI, March 5-8, 2001.)

69. Michelena, N. Louca, L., Kokkolaras, M., Lin, C. C., Jung, D., Filipi, Z., 

Assanis, D. N., Papalambros, P., Peng, H, Stein, J. and M. Feury, “Design of 

an Advanced Heavy Tactical Truck: A Target Cascading Case Study,” 

selected for 2001 SAE Transactions, Journal of Commercial Vehicles. 

(Presented as SAE Paper 2001-01-2793, SAE International Truck and Bus 

Exposition, Chicago, IL, November 12-14, 2001).

70. Kim, H.M., Kokkolaras, M., Louca, L.S., Delagrammatikas, G.J., Michelena, 

N. F., Filipi, Z. S., Papalambros, P. Y., Stein, J.L. and D.N. Assanis, “Target 

Cascading in Vehicle Redesign: A Class VI Truck Study,” Int. J. of Vehicle 

Design, Vol. 29, No.3, 2002.

71. Hong, S. J., M. Wooldridge and D. N. Assanis, “Modeling of Chemical and 

Mixing Effects on Autoignition Under Direct Injection Stratified Charge 

Conditions,” Proceedings of 29th International Symposium on Combustion, 

Sapporo, Japan, July 21-26, 2002. 

72. Fiveland, S., Agama, R., Christensen, M., Johansson, B., Hiltner, J., Mauss, F., 

and D. N. Assanis, “Experimental and Simulated Results Detailing the 

Sensitivity of Natural Gas HCCI Engines to Fuel Composition,” selected for 

2001 SAE Transactions: Journal of Fuels and Lubricants, 110:4, 2123-2134. 

(Presented as SAE Paper 2001-01-3609, 2002 SAE World Congress, Detroit, 

MI, March 4-7, 2002.)

73. Olsson, J. O, Tunestal, P., Johansson, B., Fiveland, S., Agama, R., Willi, M. 

and D. N. Assanis, “Compression Ratio Influence on Maximum Load of a 
Assanis, 45 

Natural Gas Fueled HCCI Engine,” selected for 2002 SAE Transactions: 

Journal Engine, 111:3, pp. 442-458. (Presented as SAE Paper 2002-01-0111, 

Session on Homogeneous Charge Compression Ignition Engines, 2002 SAE 

World Congress, Detroit, MI, March 4-7, 2002.)

74. Depcik, C. and D. N. Assanis, “A Universal Heat Transfer Correlation for 

Intake/Exhaust Flow in a Spark-Ignition Internal Combustion Engine”, selected 

for 2002 SAE Transactions: Journal of Engines, 111:3, pp. 734-740.

(Presented as SAE Paper 2002-01-0372, Session on Engine Modeling, 2002 

SAE World Congress, Detroit, MI, March 4-7, 2002.)

75. Lee, T., Bae, C., Bohac, S.V. and D. N. Assanis, “Estimation of Air Fuel Ratio 

of an SI Engine from Exhaust Gas Temperature at Cold Start Condition,” 

selected for 2002 SAE Transactions: Journal of Fuels and Lubricants, 111:4, 

pp. 592-600. (Presented as SAE Paper 2002-01-1667, 2002 SAE Spring Fuels 

and Lubricants Meeting and Exhibition, Reno, NV, May 6-9, 2002.)

76. Fiveland, S. B. and D. N. Assanis, “Development and Validation of a QuasiDimensional Model for HCCI Engine Performance and Emissions Studies 

under Turbocharged Conditions,” selected for 2002 SAE Transactions: Journal 

of Fuels and Lubricants, 111:4, pp. 842-860. (Presented as SAE Paper 2002-

01-1757, 2002 SAE Spring Fuels and Lubricants Meeting and Exhibition, 

Reno, NV, May 6-9, 2002.)

77. Lechner, G., Knafl, A., Assanis, D. N., Tseregounis, S.I., McMillan, M.L., 

Tung, S.C., Mulawa, P.A., Bardasz, E. and S. Cowling, “Engine Oil Effects on 

the Friction and Emissions of a Light-Duty, 2.2L Diesel CIDI Engine,” 

selected for 2002 SAE Transactions: Journal of Fuels and Lubricants, 111:4, 

pp. 1165-1181. (Presented as SAE Paper 2002-01-2681, SAE Powertrain & 

Fluid Systems Conference & Exhibition, San Diego, CA, October 21-24, 2002; 

selected for 2002 Award for Research on Automotive Lubricants.)

78. Babajimopoulos, A. Assanis, D. N. and S. Fiveland, “Modeling the Effects of 

Gas Exchange Processes on HCCI Combustion and an Evaluation of Potential 

Control Through Variable Valve Actuation,” selected for 2002 SAE 

Transactions: Journal of Fuels and Lubricants, 111:4, pp. 1794-1809. 

(Presented as SAE Paper 2002-01-2829, SAE Powertrain & Fluid Systems 

Conference & Exhibition, San Diego, CA, October 21-24, 2002.)

79. Assanis, D.N., Filipi, Z.S., Fiveland, S.B., and Syrimis, M., “A Predictive 

Ignition Delay Correlation Under Steady-State and Transient Operation of a 

Direct-Injection Diesel Engine,” ASME Transactions: Journal of Engineering 

for Gas Turbines and Power, 125:2, 450-457, 2003.

80. Syrimis, M., and D. N. Assanis, “Knocking Cylinder Pressure Data 

Characteristics in a Spark-Ignition Engine,” ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 125:2, 494-499, 2003.

81. Zhang, G., and D. N. Assanis, “Manifold Gas Dynamic Modeling and its 

Coupling with Single Cylinder Engine Models using SIMULINK,” ASME 

Transactions: Journal of Engineering for Gas Turbines and Power, 125:2, 

563-571, 2003.
Assanis, 46 

82. Nelson II, S. A., Filipi, Z., and D. N. Assanis, “The Use of Neural Networks 

for Matching Compressors with Diesel Engines,” ASME Transactions: Journal 

of Engineering for Gas Turbines and Power, 125:2, 572-579, 2003.

83. Bohac, S., Assanis, D. N., and H. Holmes, “Speciated Hydrocarbon Emissions 

and the Associated Local Ozone Production from an Automotive Gasoline 

Engine,” International Journal of Engine Research, 5:1, 53-70, 2004.

84. Delagrammatikas, G.J., and D. N. Assanis, “Development of a Neural Network 

Model of an Advanced, Turbocharged Diesel Engine for Use in Vehicle-Level 

Optimization Studies,” I. Mech. E. Proceedings, Part D, Journal of Automobile 

Engineering, 218:5, 521-533(13), 2004.

85. Li, Z, Kokkolaras, M. Jung, D., Papalambros, P. Y., and D. N. Assanis, “An 

Optimization Study of Manufacturing Variation Effects on Diesel Injector 

Design with Emphasis on Emissions,” selected for inclusion in 2004 SAE 

Transactions: Journal of Materials and Manufacturing (Presented as SAE 

Paper 2004-01-1560, 2004 SAE World Congress, Detroit, MI, March 8-11, 

2004.)

86. Filipi, Z., Wang, Y., and D. N. Assanis “Effect of Variable Geometry Turbine 

Control (VGT) on Vehicle System Transient Response,” International Journal 

of Heavy Vehicle Systems, 11:3/4, 303-326, 2004.

87. Lin, C.C., Filipi, Z., Wang, Y., Louca, L., Peng, H., Assanis, D., and J. Stein, 

“Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in SIMULINK 

and its Use for Power Management Studies,” International Journal of Heavy 

Vehicle Systems, 11:3/4, 349-371, 2004.

88. Filipi, Z., Louca, L., Kokkolaras, M., Daran, B., Lin, C.C., Yildir, U., Wu, B., 

Assanis, D., Peng, H., Papalambros, P., Stein, J., Szkubiel, D., and R. Chapp, 

“Combined Optimization of Design and Power Management of the Hydraulic 

Hybrid Propulsion System for the 6x6 Medium Truck,” International Journal 

of Heavy Vehicle Systems, 11:3/4, 372-402, 2004.

89. Kokkolaras, M., Louca, L.S., Delagrammatikas, G.J., Michelena, N.F., Filipi, 

S.V., Papalambros, P.Y., Stein, J.L. and D.N. Assanis, “Simulation-Based 

Optimal Design of Heavy Trucks by Model-Based Decomposition: An 

Extensive Analytical Target Cascading Case Study,” International Journal of 

Heavy Vehicle Systems, 11:3/4, 403-433, 2004.

90. Jung, D., and D. N. Assanis, “Modeling of a Direct-Injection Diesel Engine 

Emissions for a Quasi-Dimensional Multi-Zone Spray Model,” International 

Journal of Automotive Technology, 5:3, 165-172, 2004.

91. Jung. D., and D. N. Assanis, “Reduced Quasi-Dimensional Combustion Model 

of the Direct Injection Diesel Engine for Performance and Emissions 

Predictions,” KSME International Journal, Vol. 18, No. 5, pp.865-876, 2004.

92. Wu, B., Lin, C.-C., Filipi, Z., Peng, H., and D. N. Assanis, “Optimal Power 

Management for a Hydraulic Hybrid Delivery Truck“, Vehicle System 

Dynamics, 42:1-2, 23-40, 2004.
Assanis, 47 

93. Snyder, J., R., Grover, R. O., Sick, V., and D. N. Assanis, “Transient Spray 

Cone Angles in Pressure-Swirl Injector Sprays,” selected for 2004 SAE 

Transactions: Journal of Fuels and Lubricants. (Presented as SAE Paper 

2004-01-2939, SAE Powertrain & Fluid Systems Conference & Exhibition, 

Tampa, Florida, October 25-28, 2004.)

94. Sjoberg, M., Dec, J.E., Babajimopoulos, A., and D. N. Assanis, “Comparing 

Enhanced Natural Thermal Stratification against Retarded Combustion Phasing 

for Smoothing of HCCI Heat Release Rates,” selected for 2004 SAE 

Transactions: Journal of Fuels and Lubricants. (Presented as SAE Paper 

2004-01-2994, SAE Powertrain & Fluid Systems Conference & Exhibition, 

Tampa, Florida, October 25-28, 2004.)

95. Chang, J., Gurap, O., Filipi, Z, Assanis, D. N., Kuo, T. W., Najt, P., and R. 

Rask, “New Heat Transfer Correlation for the HCCI Engine Derived from 

Measurements of Instantaneous Surface Heat Flux,” selected for 2004 SAE 

Transactions: Journal of Fuels and Lubricants. (Presented as SAE Paper 

2004-01-2996, SAE Powertrain & Fluid Systems Conference & Exhibition, 

Tampa, Florida, October 25-28, 2004.)

96. Wu, B., Filipi, Z.S., Assanis, D. N., Kramer, D. M., Ohl, G. L., Prucka, M. J., 

and E. DiValentin, “Using Artificial Neural Networks for Representing the Air 

Flow through a 2.4 Liter VVT engine,” selected for 2004 SAE Transactions: 

Journal of Fuels and Lubricants. (Presented as SAE Paper 2004-01-3054, 

SAE Powertrain & Fluid Systems Conference & Exhibition, Tampa, Florida, 

October 25-28, 2004.) 

97. Bohac, S. and D. N. Assanis, “Effect of Exhaust Valve Timing on Gasoline 

Engine Performance and Hydrocarbon Emissions,” selected for 2004 SAE 

Transactions: Journal of Fuels and Lubricants. (Presented as SAE Paper 

2004-01-3058, SAE Powertrain & Fluid Systems Conference & Exhibition, 

Tampa, Florida, October 25-28, 2004.)

98. Depcik, C., van Leer, B., and D. N. Assanis, “The Numerical Simulation of 

Variable-Property Reacting Gas Dynamics: New Insights and Validation,” 

Numerical Heat Transfer - Part A: Applications, 47:1, 27-56, 2005.

99. Depcik, C. and D. N. Assanis, “Graphical User Interfaces in an Engineering 

Educational Environment,” Computer Applications in Engineering Education, 

13:1, 48-59, 2005.

100. Cho, H., Jung, D, and D. N. Assanis, “Control Strategy of Electric Coolant 

Pumps for Fuel Economy Improvement,” International Journal of Automotive 

Technology, 6:3, 269-275, 2005.

101. Chang, J., Filipi, Z., Assanis, D., Kuo, T-W., Najt, P. and R. Rask, 

“Characterizing the Thermal Sensitivity of a Gasoline HCCI Engine with 

Measurements of Instantaneous Wall Temperature and Heat Flux,” Special 

HCCI issue, International Journal of Engine Research, 289-310, 6:4, 2005.

102. Babajimopoulos, A., Assanis, D.N., Flowers, D., Aceves, S., and R. Hessel, 

“A Fully Coupled Computational Fluid Dynamics and Multi-Zone Model with 

Detailed Chemical Kinetics for the Simulation of Premixed Charge 
Assanis, 48 

Compression Ignition Engines,” Special HCCI issue of International Journal 

of Engine Research, 497-512, 6:5, 2005.

103. Depcik, C. and D. N. Assanis, “One Dimensional Automotive Catalyst 

Modeling,” Progress in Energy and Combustion Science, 308-369, 31:4, 2005.

104. Kokkolaras, M., Mourelatos, Z.P., Louca, L.S., Filipi, Z.S., Delagrammatikas, 

G.J., Stefanopoulou, A.G., Papalambros, P.Y., and D.N. Assanis, “Design 

under Uncertainty and Assessment of Performance Reliability for a Dual-Use 

Medium Truck with Hydraulic-Hybrid Powertrain and Fuel Cell Auxiliary 

Power Unit,” selected for 2005 SAE Transactions: Journal of Engines. 

(Presented as SAE Paper 2005-01-1396, SP-1956, 2005 SAE World Congress, 

Detroit, MI, April 11-14, 2005.)

105. Jacobs, T. J., Bohac, S. V., Assanis, D. N., Szymkowicz, P. G., “Lean and Rich 

Premixed Compression Ignition Combustion in a Light-Duty Diesel Engine,” 

selected for 2005 SAE Transactions: Journal of Engines. (Presented as SAE 

Paper 2005-01-0166, SP-1963, 2005 SAE World Congress, Detroit, MI, April 

11-14, 2005.) 

106. Lechner, G., Jacobs, T., Chryssakis, C., D. N. Assanis, and R. Siewert, 

“Evaluation of Narrow Spray Cone Angle, Advanced Injection Timing 

Strategy to Achieve Partially Premixed Compression Ignition Combustion in a 

Diesel Engine,” selected for 2005 SAE Transactions: Journal of Engines. 

(Presented as SAE Paper 2005-01-0167, SP-1963, 2005 SAE World Congress, 

Detroit, MI, April 11-14, 2005.)

107. Aceves, S., Flowers, D.L., Espinosa-Loza, F., Babajimopoulos, A., and D.N. 

Assanis, “Analysis of Premixed Charge Compression Ignition Combustion 

with a Sequential Fluid Mechanics-Multizone Chemical Kinetics Model,” 

selected for 2005 SAE Transactions: Journal of Engines. (Presented as SAE 

Paper 2005-01-0115, SP-1963, 2005 SAE World Congress, Detroit, MI, April 

11-14, 2005.)

108. Hong, S.J., Wooldridge, M. S., Im, H.G., Assanis, D.N., and Pitsch, H., 

“Development and Application of a Comprehensive Soot Model for 3D CFD 

Reacting Flow Studies in a Diesel Engine,” Combustion and Flame, 143:1-2,

11-26, 2005. 

109. Jung, D. and D. N. Assanis, “Quasi-Dimensional Modeling of Direct Injection 

Diesel Engine Nitric Oxide, Soot and Unburned Hydrocarbon Emissions”, 

ASME Transactions: Journal of Engineering for Gas Turbines and Power, 

128:2, 388-396, 2006.

110. Chryssakis, C.A., Hagena, J.R., Knafl, A., Hamosfakidis, V.D., Filipi, Z.S., 

and D.N. Assanis, “In-Cylinder Reduction of PM and NOx Emissions from 

Diesel Combustion with Advanced Injection Strategies,” special issue on “New 

Strategies in Automotive Diesel Engines for Meeting Upcoming Pollutant 

Emissions Restrictions,” International Journal of Vehicle Design, 83-102, 

41:1-4, 2006. 
Assanis, 49 

111. Bohac, S.V., Han, M., Jacobs, T.J., Lopez, A.J., Assanis, D.N., and P.G. 

Szymkowicz, “Speciated Hydrocarbon Emissions from an Automotive Diesel 

Engine and DOC Utilizing Conventional and PCI Combustion,” 2006 SAE 

Transactions: Journal of Fuels and Lubricants, 115, 41-52, 2007. (Presented as 

SAE Paper 2006-01-0201, SP-2005, 2006 SAE Congress, Detroit, MI, April 3-

6, 2006.) 

112. Knafl, A., Busch, S. B., Han, M., Bohac, S.V., Assanis, D.N., Szymkowicz, 

P.G., and R.D. Blint, “Characterizing Light-Off Behavior and SpeciesResolved Conversion Efficiency during In-Situ Diesel Oxidation Catalyst 

Degreening,” 2006 SAE Transactions: Journal of Fuels and Lubricants, 115, 

53-62, 2007. (Presented at 2006 SAE Congress, SP-2022, Detroit, MI, April 3-

6, 2006.) 

113. Chang, K.J., Babajimopoulos, A., Lavoie, G.A., Filipi, Z.S., and D.N. Assanis, 

“Analysis of Load and Speed Transitions in an HCCI Engine Using 1-D Cycle 

Simulation and Thermal Networks,” 2006 SAE Transactions: Journal of 

Engines, 115, 621-633, 2007. (Presented as SAE Paper 2006-01-1087, SP2005, 2006 SAE Congress, Detroit, MI, April 3-6, 2006.)

114. Guralp, O.A., Hoffman, M.A., Assanis, D.N., Filipi, Z.S., Kuo, T.W., Najt, P., 

and R. Rask, “Characterizing the Effect of Combustion Chamber Deposits on a 

Gasoline HCCI Engine,” 2006 SAE Transactions, Journal of Engines, 115,

824-835, 2007. (Presented as SAE Paper 2006-01-3277, Powertrain and Fluid 

Systems Conference and Exhibition,” October 2006, Toronto, ON, Canada).

115. Filipi, Z., Fathy, H., Hagena, J., Knafl, A., Ahlawat, R., Liu, J., Jung, D., 

Assanis, D.N., Peng, H., and J. Stein, “Engine-in-the-Loop Testing for 

Evaluating Hybrid Propulsion Concepts and Transient Emissions – HMMWV 

Case Study,” 2006 SAE Transactions: Journal of Commercial Vehicles, 115, 

23-41, 2007. (SAE Paper 2006-01-0443, SP-2008, 2006 SAE Congress, 

Detroit, MI, April 3-6, 2006.) 

116. Jacobs, T. J. and D. N. Assanis, “The Attainment of Premixed Compression 

Ignition Low-Temperature Combustion in a Compression Ignition Direct 

Injection Engine,” Proceedings of Combustion Institute, vol. 31, 2913-2920, 

2007. (Presented at 31st International Symposium on Combustion, Heidelberg, 

Germany, August 6-11, 2006). 

117. Cho, H., Jung, D., Filipi, Z.S., Assanis, D.N., Vanderslice, J., and W. Bryzik, 

“Application of Controllable Electric Cooling Pumps for Fuel Economy and 

Cooling Performance Improvement,” ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 127:1, 239-244, 2007.

118. Babajimopoulos, A., Lavoie, G.A., and D.N. Assanis “On the Role of Top 

Dead Center Conditions in the Combustion Phasing of Homogeneous Charge 

Compression Ignition Engines,” Combustion Science and Technology, 179:9, 

2039-2063, 2007.

119. Depcik, C. and D.N. Assanis, “Merging Undergraduate and Graduate Fluid 

Mechanics Through the Use of the SIMPLE Method for the Incompressible 
Assanis, 50 

Navier-Stokes Equations,” International Journal of Engineering Education, 

23:4, 816-833, 2007.

120. Jacobs, T. Depcik, C, Hagena, J. and D. N. Assanis, “Instructional Use of a 

Single Zone, Pre-Mixed Spark Ignition Heat Release Simulation,” 

International Journal of Mechanical Engineering Education, 35:1, 1-31, 2007.

121. Fernandes, G., Fuschetto, J., Filipi, Z., Assanis, D.N., and H. McKee, “Impact 

of Military JP-8 Fuel on Heavy Duty Diesel Engine Performance and 

Emissions,” Journal of Automobile Engineering, Proceedings of the Institution 

of Mechanical Engineers, Part D, 221:8, 957-970, 2007.

122. Northrop, W., Jacobs, T., Assanis, D., and Bohac, S., “Deactivation of a Diesel 

Oxidation Catalyst due to Exhaust Species from Rich Premixed Compression 

Ignition in a Light-Duty Diesel Engine,” Int. J. Engine Res., 8:6, 487-498, 

2007.

123. Sampara, C.S., Bissett, E.J., Chmielewski, M., and D.N. Assanis, “Global 

Kinetics for Platinum Diesel Oxidation Catalysts,” Industrial and Engineering 

Chemistry Research, 46:24, 7993-8003, 2008. 

124. Sampara, C.S., Bissett, E.J., and D.N. Assanis, “Hydrocarbon Storage 

Modeling for Platinum Diesel Oxidation Catalysts,” Chemical Engineering 

Science, 63, 5279-5192, 2008. 

125. Hong, S. J., Wooldridge, M.S., Im, H.G., Assanis, D.N., and E. Kurtz, 

“Modeling of Diesel Combustion, Soot and NO Emissions Based on a 

Modified Eddy Dissipation Concept,” Combustion Science and Technology, 

180:8, 1421-1488, 2008.

126. Chryssakis, C. and D. N. Assanis, “A Unified Spray Break-up Model for 

Internal Combustion Engine Applications,” Atomization and Sprays, 18:5, 375-

426, 2008.

127. Jung, D., and D. N. Assanis, “A Reduced Quasi-Dimensional Model to Predict 

the Effect of Nozzle Geometry on Diesel Engine Performance and Emissions,” 

submitted to Journal of Automobile Engineering (IMechE Proc. Part D), 

222:D1, 131-141, 2008.

128. Jung, D., Wang, W.L., Knafl, A., Jacobs, T.J., Hu, S.J., and D.N. Assanis, 

“Experimental Investigation of the Abrasive Flow Machining Effects on 

Injector Nozzle Geometries, Engine Performance and Emissions in a DI Diesel 

Engine,” International Journal of Automotive Technology, 9:1, 9-15, 2008. 

129. Depcik, C., Assanis, D.N., and K. Bevan, “A One-Dimensional Lean NOx Trap 

Model with a Global Kinetic Mechanism that includes NH3 and N2O,” Int. J. 

Engine Res., 9:1, 57-77, 2008.

130. Skjoedt, M., Butts, R., Assanis, D. N., and S.V., Bohac, “Effects of Base Oil, 

Friction Modifier and Viscosity Grade on Firing Engine Friction in an 

Automotive Engine,” Tribology International, 41:6, 556-563, 2008.
Assanis, 51 

131. Granell, S. M., Assanis, D. N., Bohac, S. V., and D. E. Gillespie`, “The Fuel 

Mix Limits and Efficiency of a Stoichiometric, Ammonia and Gasoline Dual 

Fueled Spark Ignition Engine,” ASME Journal of Engineering for Gas 

Turbines and Power, 130:4, 042802:1-8, 2008. 

132. Han, M., Jacobs, T. J., Assanis, D. N. and S. V. Bohac, “Method and Detailed 

Analysis of Individual Hydrocarbon Species from Diesel Combustion Modes 

and Diesel Oxidation Catalyst,” ASME Journal of Engineering for Gas 

Turbines and Power, 130:4, 042803:1-10, 2008. (Presented at ASME ICE Fall 

Technical Conference, Charleston, SC, October 14-17, 2007.)

133. Jacobs, T.J., and D.N. Assanis, “Characteristic Response of a Production 

Diesel Oxidation Catalyst Exposed to Lean and Rich PCI Exhaust,” ASME 

Transactions: Journal of Engineering for Gas Turbines and Power, 130:4, 

042805:1-9, 2008. (Presented at ASME ICE Fall Technical Conference, 

Charleston, SC, October 14-17, 2007.)

134. Busch, S., Bohac, S.V., and D.N. Assanis, “A Study of the Transition Between 

Lean Conventional Diesel Combustion and Lean, Premixed, Low-Temperature 

Diesel Combustion,” ASME Transactions: Journal of Engineering for Gas 

Turbines and Power, 130:5, 052804:1-8, 2008. (Presented at ASME ICE Fall 

Technical Conference, Charleston, SC, October 14-17, 2007.)

135. Chang, J., Filipi, Z.S., Assanis, D.N., Najt, P., Rask, R., Kuo, T.W., 

“Investigation of Mixture Preparation Effects on Gasoline HCCI Combustion 

Aided by Measurements of Wall Heat Flux,” ASME Transactions: Journal of 

Engineering for Gas Turbines and Power, 130, 062806:1-9, 2008. (Presented 

at ASME ICE Fall Technical Conference, Charleston, SC, October 14-17, 

2007.)

136. Depcik, C. and D. N. Assanis, “Simulating Area Conservation and the GasWall Interface for One-Dimensional Based Diesel Particulate Filter Models,”

ASME Transactions: Journal of Engineering for Gas Turbines and Power, 130, 

062807:1-18, November 2008.

137. Jung, D., Yu, S., and D. N. Assanis, “Modeling of a Proton Exchange 

Membrane Fuel Cell with a Large Active Area for Thermal Behavior 

Analysis,” ASME Transactions: Journal of Fuel Cell Science and Technology, 

5, 044502:1-6, 2008. 

138. Jacobs, T.J., Jagmin, C., Williamson, W.J., Filipi, Z.S., Assanis, D.N., and W. 

Bryzik, “Performance and Emission Enhancements of a Variable Geometry 

Turbocharger on a Heavy-Duty Diesel Engine,” Special Issue on Performance 

and Dynamics of Multi-Wheeled and Tracked Military Vehicles, International 

Journal of Heavy Vehicle Systems, 15, 170-187, 2008. 

139. Han, M., Assanis, D. N. and S. V. Bohac, “Comparison of HC Species from 

Diesel Combustion Modes and Characterization of a Heat-up DOC 

Formulation,” International Journal of Automotive Technology, 9:4, 405-413, 

2008.

140. Han, M., Assanis, D. N. and S. V. Bohac, “Characterization of Heat-Up Diesel 

Oxidation Catalysts through Gas Flow Reactor and In-situ Engine Testing,” I. 
Assanis, 52 

Mech. E. Part D, Journal of Automobile Engineering, 222:9, pp. 1705-1716, 

2008.

141. Malikopoulos, A., Assanis, D.N., and P.Y. Papalambros, “Real-Time SelfLearning Optimization of Diesel Engine Calibration,” ASME Transactions: 

Journal of Engineering for Gas Turbines and Power, 131, 022803:1-7, March 

2009. (Based on ASE Paper ICEF2007-1603, Proceedings of ASME ICE Fall 

Technical Conference, 537-545, Charleston, SC, October 14-17, 2007.)

142. Prucka, R.G., Filipi, Z.S., Assanis, D.N., Kramer, D.M., and G.L. Ohl, “An 

Evaluation of Residual Gas Fraction Measurement Techniques in a High 

Degree of Freedom Spark Ignition Engine,” SAE Journal of Engines, 1:1, 71-

84, April 2009. (Presented at 2008 SAE International Congress and Exposition, 

Detroit, MI, April 14-17, 2008.)

143. Mosburger, M., Fuschetto, J., Assanis, D.N., Filipi, Z. and H. McKee, “Impact 

of High Sulfur JP-8 Fuel on Heavy Duty Diesel Engine EGR Cooler 

Condensate,” 2008 SAE Transactions, Journal of Commercial Vehicles, 1:1, 

100-107, April 2009. (Presented as SAE Paper 2008-01-1081 at 2008 SAE 

International Congress and Exposition, Detroit, MI, April 14-17, 2008.)

144. Han, M., Assanis, D. N., Bohac, S. V., 2008, “Sources of Hydrocarbon 

Emissions from Low Temperature Premixed Compression Ignition 

Combustion in a Common Rail Direct Injection Engine,” Combustion Science 

and Technology, 181:3, 496-517, 2009.

145. Malikopoulos, A.A., Papalambros, P.Y., and Assanis, D.N., “A Real-Time 

Computational Learning Model for Sequential Decision-Making Problems 

Under Uncertainty,” ASME J. Dyn. Sys., Meas., Control, 131:4, 041010(8), 

2009.

146. Hamosfakidis, V., Im, H., and D.N. Assanis, “A Regenerative Multiple Zone 

Model for HCCI Combustion,” Combustion and Flame, 156:4, 928-941 2009.

147. Ickes, A.M., Bohac, S.V., and D.N. Assanis, “Effect of Fuel Cetane Number on 

a Premixed Diesel Combustion Mode,” International Journal of Engine 

Research, 10:4, 251-263, 2009.

148. Ickes, A., Bohac, S., and D.N. Assanis, “Effect of Ethylhexyl Nitrate Cetane 

Improver on NOx Emissions from Premixed Low-Temperature Diesel 

Combustion,” Energy and Fuels, 23, 4943-4948, 2009.

149. Lee, B., Filipi, Z., Assanis, D.N., and D. Jung, “Simulation-Based Assessment 

of Various Dual-Stage Boosting Systems in Terms of Performance and Fuel 

Economy Improvements,” SAE Int. J. Engines, 2(1): 1335-1346, 2009. 

(Presented as SAE Paper 2009-01-1471, SAE 2009 International Congress and 

Exposition, Detroit, MI, April 20-23, 2009.)

150. Northrop, W. Bohac, S. and D.N. Assanis, “Premixed Low Temperature 

Combustion of Biodiesel and Blends in a High Speed Compression Ignition 

Engine,” SAE Int. J. Fuels Lubr., 2(1): 28-40, 2009. (Presented as SAE Paper 

2009-01-0133, SAE 2009 International Congress and Exposition, Detroit, MI, 

April 20-23, 2009.)
Assanis, 53 

151. Abarham, M., Hoard, J., Assanis, D.N., Styles, D., Curtis, E., Ramesh, N., 

Sluder, C.S., and J, Storey, “Modeling of Thermophoretic Soot Deposition and 

Hydrocarbon Condensation in EGR Coolers,” SAE Int. J. Fuels Lubr., 2(1): 

921-931, 2009. (Presented as SAE Paper 2009-01-1939, SAE 2009 

International Powertrains, Fuels and Lubricants Meeting, Florence, Italy, June 

15-17, 2009).

152. Cho, K., Grover, R., Assanis, D.N., Filipi, Z., Najt, P., Szekely, G., and R. 

Rask, “Combining Instantaneous Temperature Measurements and CFD for 

Analysis of Fuel Impingement on the DISI Engine Piston Top,” ASME 

Transactions: Journal of Engineering for Gas Turbines and Power, 132:7, 

2010. (Presented as ICES2009-76117, Proceedings of the ASME Internal 

Combustion Engine Division 2009 Spring Technical Conference ICES2009, 

Milwaukee, WI, May 3-6, 2009. 

153. Lavoie, G., Martz, J., Wooldridge, M.S. and D.N. Assanis, “Multi-Mode 

Combustion Diagram for Spark Assisted Compression Ignition,” Combustion 

and Flame, 157, 1106-1110, 2010.

154. Malikopoulos, A.A., Papalambros, P.Y., and Assanis, D.N., “Online 

Identification and Stochastic Control for Autonomous Internal Combustion 

Engines,” ASME J. Dyn. Sys., Meas., Control, 132:2, 6 pages, 2010.

155. Depcik, C., Kobiera, A., and D.N. Assanis, “Influence of Density Variation on 

One-Dimensional Modeling of Exhaust Assisted Catalytic Fuel Reforming,” 

Heat Transfer Engineering: An International Journal, 31:13, 1098 – 1113, 

2010. 

156. Abarham, M., Hoard, J., Assanis, D.N., Styles, D., Curtis, E.W., Ramesh, N., 

Sluder, C.S., Storey, J.M., and M. Lance, “Review of Soot Deposition and 

Removal Mechanisms in EGR Coolers,” SAE Int. J. Fuels Lubr., 2010.

(Presented at SAE 2010 World Congress, Detroit, MI, April 13-15, 2010.)

157. Mamalis, S., Nair, V., Andruskiewicz, P., Olesky, S., Assanis, D.N., 

Babajimopoulos, A., Wermuth, N., and P. Najt, “Comparison of Different 

Boosting Strategies for Homogeneous Charge Compression Ignition Engines - 

A Modeling Study,” SAE Int. J. Engines, 2010. (Presented at SAE 2010 World 

Congress, Detroit, MI, April 13-15, 2010.)

158. Cho, K., Assanis, D.N., Filipi, Z., Szekely, G., Najt, P. and R. Rask, 

“Experimental Investigation of Combustion and Heat Transfer in a DirectInjection Spark-Ignition (DISI) Engine via Instantaneous Combustion Chamber 

Surface Temperature Measurements,” Mech. E. Part D, Journal of Automobile 

Engineering, 222:11, pp. 2219-2233, 2008.

159. Northrop, W., Vanderpool, L.M., Madathil, P.V., Assanis, D.N., and S.V. 

Bohac, “Investigation of Hydrogen Emissions in Partially Premixed Diesel 

Combustion,” ASME Transactions: Journal of Engineering for Gas Turbines 

and Power. J. Eng. Gas Turbines and Power, 132, 112803, 2010. (Presented as 

ASME Paper ICEF 2009-14063, ASME ICE Division Fall Technical 

Conference, Lucerne, Switzerland, September 20-24, 2009).
Assanis, 54 

160. Martz, J., Middleton, R., Lavoie, G., Babajimopoulos, A., and D.N. Assanis, 

“A Computational Study and Correlation of Premixed Isooctane-Air Laminar 

Reaction Front Properties under Spark Ignited and Spark Assisted 

Compression Ignition Engine Conditions,” Combustion and Flame, 

doi:10.1016/j.combustflame.2010.09.014, 2010. 

161. Abarham, M., Hoard, J.W., Assanis, D.N., Styles, D., Sluder, S., and J. Storey, 

“An Analytical Study of Thermophoretic Particulate Deposition in Turbulent 

Pipe Flows,” Aerosol Science and Technology, Vol. 44 (9), pp. 785-795, 2010.

162. Northrop, W., Madathil, P. Bohac, S, and D.N. Assanis, “Condensational 

Growth of Particulate Matter from Partially Premixed Low Temperature 

Combustion of Biodiesel in a Compression Ignition Engine,” accepted for 

publication in Aerosol Science and Technology, 2010.

163. Ortiz-Sotto, E., Assanis, D.N, and A. Babajimopoulos, “A Comprehensive 

Engine to Drive-Cycle Modeling Framework for the Fuel Economy 

Assessment of Advanced Engine and Combustion Technologies,” accepted for 

publication in International Journal of Energy Research, 2011.

Refereed Conference or Symposium Presentations 

1. Assanis, D. N., and A. D. Carmichael, “A Study of Wave Energy Conversion 

for an Offshore Structure," Proceedings of the American Society of Mechanical 

Engineers 3rd International Offshore Mechanics and Arctic Engineering 

Symposium, II, 287-294, 1984. 

2. Kamo, R. and D. N. Assanis, “Thin Thermal Barrier Coatings for Engines,” 

ASME Paper 89-ICE-14, ASME-ETCE Technical Conference, Houston, TX, 

1989.

3. Wiese, K., M. Bonne, F. Friedmann, and D. N. Assanis, “Combustion and Heat 

Transfer Studies in a Direct-Injection Diesel Engine," SAE Paper 891902, SAE 

International Off-Highway Meeting and Exposition, Milwaukee, WI, Sept. 11-

14, 1989.

4. Assanis, D. N., R. R. Sekar, D. Baker, C. Siambekos, R. L. Cole, and T. 

Marciniak, “Simulation Studies of Diesel Engine Performance with Oxygen 

Enriched Air and Water Emulsified Fuels," ASME Paper 90-ICE-17, ASMEETCE Technical Conference, New Orleans, LA, Jan. 1990. 

5. Assanis, D. N., Friedmann, F. A., Wiese, K. L., Zaluzec, M. J., and J. M. 

Rigsbee, “A Prototype Thin-film Thermocouple for Transient Heat Transfer 

Measurements in Ceramic-Coated Combustion Chambers," SAE Paper 

900691, SAE International Congress and Exposition, Detroit, MI, Feb. 26-

March 2, 1990. 

6. Varnavas, C., and D. N. Assanis, “Combustion Studies in a Diesel Engine 

Using a Multidimensional Engine Simulation," ASME-ETCE Technical 

Conference, Houston, TX, Jan. 20-23, 1991.
Assanis, 55 

7. Assanis, D. N., and F. Friedmann, “A Telemetry Linkage System for Piston 

Temperature Measurements in a Diesel Engine," SAE Paper 910299, SAE 

International Congress and Exposition, Detroit, MI, Feb. 25-March 1, 1991. 

8. Assanis, D. N., and C. Varnavas, “On the Prediction of Diesel Engine 

Combustion Using a Multi-Dimensional Computer Code," Proceedings of 

International Conference on the Analysis of Thermal and Energy Systems, pp. 

539-554, Athens, Greece, June 1991. 

9. Assanis, D. N., and D. Baker, “Thermodynamic and Heat Transfer Analysis of 

Ceramic-Insulated Diesel Engine Part I: Methodology," Proceedings of 

International Conference on the Analysis of Thermal and Energy Systems, pp. 

571-583, Athens, Greece, June 3-6, 1991.

10. Assanis, D. N., and D. Baker, “Thermodynamic and Heat Transfer Analysis of 

Ceramic-Insulated Diesel Engine Part II: A Case Study," Proceedings of 

International Conference on the Analysis of Thermal and Energy Systems, pp. 

585-599, Athens, Greece, June 3-6, 1991.

11. Tamamidis, P., and D. N. Assanis, "2-D and 3-D Computations of Engine 

Scavenging Flows," ASME Paper 92-ICE-1, ASME-ETCE Technical 

Conference, Houston, TX, Jan. 26-29, 1992. 

12. Karvounis, E. and D.N. Assanis, “An Integrated Framework for Internal 

Combustion Engine Simulation and Design," ASME Paper 92-ICE-2, ASMEETCE Technical Conference, Houston, TX, Jan. 26-29, 1992. 

13. Shih, L., and D. N. Assanis, “Modeling Hydrocarbon Absorption and 

Desorption Processes into Cylinder Wall Oil Films," Proceedings of American 

Chemical Society National Meeting, Washington, D.C., Aug. 23-25, 1992. 

14. Syrimis, M. and D. N. Assanis, “Combustion of Low-Ash Coal-Diesel Slurries 

in a High-Speed, Direct-Injection Diesel Engine," Coal-Fueled Diesel Engines 

1993, 53-61, ICE-19, ASME-ETCE Technical Conference, Houston, TX, Jan. 

31- Feb. 3, 1993.

15. Assanis, D. N., Gavaises, M., and G. Bergeles, “Calibration and Validation of 

the Taylor Analogy Breakup Model for Diesel Spray Calculations," ASME 

Paper 93-ICE-11, ASME-ETCE Technical Conference, Houston, TX, Jan. 31- 

Feb. 3, 1993.

16. Assanis, D. N. and Karvounis, E., and J. A.E. Bell, “Design Optimization of 

the Piston Compounded Adiabatic Diesel Engine Through Computer 

Simulation", SAE Paper 930986, SAE International Congress and Exposition, 

Detroit, MI, March 1-5, 1993.

17. Varnavas, C., and D. N. Assanis, "Evaluation of an Improved Model for 

Droplet Evaporation in High Temperatures," Sixth Annual Conference on 

Liquid Atomization and Spray Systems, ILASS 93 Americas, Worcester, MA, 

May 17-19, 1993. 

18. Shih, L., and D. N. Assanis, "Experimental Validation of Spray Dynamics and 

Wall Interaction Models in Quiescent Chambers", Sixth Annual Conference on 
Assanis, 56 

Liquid Atomization and Spray Systems, ILASS 93 Americas, Worcester, MA, 

May 17-19, 1993. 

19. Tamamidis, P., and D. N. Assanis, “Benchmarking High Resolution Schemes 

in Two-Dimensional Unsteady Flows," Forum on Unsteady Flows, FED-157, 

pp. 95-106, ASME International Fluids Engineering Conference, Washington, 

D.C. June 20-24, 1993.

20. Tamamidis, P., and D. N. Assanis, “Numerical Simulation of Internal Flows in 

Complex Geometries Using Curvature-Modified k-ε Models," ASME 

International Fluids Engineering Conference, Forum on Turbulent Flows, 

Washington, D.C. June 20-24, 1993.

21. Li, Q., and D. N. Assanis, “A Quasi-Dimensional Combustion Model for 

Diesel Engine Simulation," Alternate Fuels, Engine Performance and 

Emissions, ICE-20, 109-118, ASME-ICED Fall Technical Conference, 

Morgantown, WV, September 26-29, 1993.

22. Assanis, D. N., and B. Bolton, “Variable Valve Timing Strategies for Optimum 

Engine Performance and Fuel Economy," ASME Paper 94-ICE-5, ASME 

ETCE Conference, New Orleans, LA, January 23-26, 1994.

23. Bolton, B., and D. N. Assanis, “Optimum Breathing Strategies for 

Turbocharged Diesel Engines Based on the Miller Cycle Concept," ASME PDVol. 64-8.2, pp. 253-262, Second Biennial European Joint Conference on 

Engineering Systems Design and Analysis ESDA, London, England, July 4-7, 

1994.

24. Varnavas, C., and D. N. Assanis, “An Improved Model for Predicting 

Evaporation of High Pressure Engine Sprays", ICLASS 94, Sixth International 

Conference on Liquid Atomization and Spray Systems, Rouen, France, July 

18-22, 1994.

25. Herring, P., and D. N. Assanis, “A Low Heat Rejection and Low Thermal 

Distortion Piston-Liner Design," ASME-ICED Fall Technical Conference, 

Lafayette, IN, October 2-5, 1994.

26. Varnavas, C., and D. N. Assanis, “A High Temperature and High Pressure 

Evaporation Model for the KIVA-3 Code," SAE Paper 960629, 1996 SAE 

International Congress, Detroit, MI, February 26-29, 1996.

27. Agarwal, A., Filipi, Z., Assanis, D. N., and D. Baker, “On Turbulence 

Modeling for a Quasi-Dimensional Spark Ignition Engine Simulation," Sixth 

International Conference on Numerical Combustion, SIAM (Society for 

Industrial and Applied Mathematics), New Orleans, March 4-6, 1996.

28. Nelson II, S. A., Filipi, Z., and D. N. Assanis, “The Use of Neural Networks 

for Matching Compressors with Diesel Engines,” Presented at ASME-ICE 

Spring Technical Conference, Youngstown, OH, April 21-24, 1996.

29. Papageorgakis, G., Agarwal, A., Zhang, G., and D. N. Assanis, “MultiDimensional Modeling of Natural Gas Injection, Glow Plug Ignition, and 
Assanis, 57 

Combustion with the KIVA-3 Code," ASME-ICE Spring Technical 

Conference, Youngstown, OH, April 21-24, 1996.

30. Sun, X., Assanis, D. N., and G. Brereton, “Numerical Modeling and 

Experimental Validation of Steady-State Hydrocarbon Emissions from Small 

Utility Four-Stroke Engines," Presented in Session on Engine Emissions, 

ASME-ICE Spring Technical Conference, Youngstown, OH, April 21-24, 

1996.

31. Zhang, G., and D. N. Assanis, “Application of 1-D and 3-D Gas Dynamic 

Modeling to Engine Manifolds," invited paper, Symposium on Supercomputer 

Applications in the Automotive Industries, 29th ISATA, Florence, Italy, June 

3-6, 1996.

32. Filipi, Z., and D.N. Assanis, “On Determining the Optimum Stroke-to-Bore 

Ratio for a Spark-Ignition Engine of a Given Displacement," Powertrain 

Systems Session, 26th International FISITA Congress, Prague, June 16-23, 

1996.

33. Zhang, G., and D. N. Assanis, “3-D Turbulent Flow Predictions Using HighOrder Schemes and Comparison with Measurements," Presented in 

Symposium on Numerical Developments in CFD, ASME Fluids Engineering 

Division Summer Meeting, San Diego, CA, July 7-11, 1996.

34. Zhang, G., and D. N. Assanis, “Finite Volume Predictions of 3D Turbulent 

Compressible Flows Using a Segregated Solution Approach and High Order 

Schemes," International Symposium on Finite Volumes for Complex 

Applications - Problems and Perspectives, Rouen, France, July 15-18, 1996.

35. Poola, R., Assanis, D. N., Sekar, R., and G. R. Cataldi, “Study of Using 

Oxygen-Enriched Combustion Air for Locomotive Diesel Engines,” Presented 

in Diamond Anniversary Conference of the ASME-ICE Division, Fairborn, 

OH, October 20-23, 1996.

36. Filipi, Z. S., and D. N. Assanis, “A Non-Linear, Transient, Single-Cylinder 

Diesel Engine Simulation for Predictions of Instantaneous Engine Speed and 

Torque,” Presented at ASME-ICE Spring Technical Conference, Fort Collins, 

Colorado, April 27-30, 1997.

37. Assanis, D. N., Atreya, A., Borgnakke, C., Dowling, D., Filipi, Z., Hoffman, 

S., Homsy, S., Kanafani, F., Morrison, K., Patterson, D., Syrimis, M., Winton, 

D., Zhang, G., and Bryzik, W., “Development of a Modular Multi-Cylinder 

Transient Diesel Engine Simulation for System Performance and Vibration 

Studies,” Presented at ASME-ICE Spring Technical Conference, Fort Collins, 

Colorado, April 27-30, 1997.

38. Zhang, G., and D. N. Assanis, “Manifold Gas Dynamic Modeling and its 

Coupling with Single Cylinder Engine Models using SIMULINK,” Presented 

at ASME-ICE Spring Technical Conference, Fort Collins, Colorado, April 27-

30, 1997.
Assanis, 58 

39. Syrimis, M., and D. N. Assanis, “Knocking Cylinder Pressure Data 

Characteristics in a Spark-Ignition Engine,” Presented at ASME-ICE Spring 

Technical Conference, Fort Collins, Colorado, April 27-30, 1997.

40. Agarwal, A., and D. N. Assanis, “Modeling the Effect of Natural Gas 

Composition on Ignition Delay Under Compression Ignition Conditions,” 

Presented as SAE Paper 971711, 1997 SAE International Fuels and Lubricants 

Meeting, Dearborn, MI, May 5-8, 1997.

41. Baker, D. M., and D. N. Assanis, “A Coupled Methodology for Modeling the 

Transient Thermal Response of SI Engines Subject to Time-Varying Operating 

Conditions,” Presented as SAE Paper 971859, Vehicle Thermal Management 

Systems VTMS-3 International Conference, Indianapolis, IN, May 19-22, 

1997.

42. Assanis, D. N., Filipi, Z. S., and G. Zhang, “Development of Interactive 

Graphical Software Tools in the Context of Teaching Modeling of Internal 

Combustion Engines in a Multimedia Classroom,” Presented at 1997 ASEE 

Annual Conference, Milwaukee, WI, June 15-18, 1997.

43. Filipi, Z. S., Homsy, S. C., Morrison, K. M., Hoffman, S., Dowling, D. R., and 

D. N. Assanis, “Strain Gage-Based Instrumentation for In-Situ Diesel Fuel 

Injection System Diagnostics,” Presented at 1997 ASEE Annual Conference, 

Milwaukee, WI, June 15-18, 1997.

44. Nishida, K., Ceccio, S., Assanis, D. N., Tamaki, N, and Hiroyasu, H., 

“Characterization of Cavitation Flow in a Simple Hole Nozzle,” Seventh 

International Conference on Liquid Atomization and Spray Systems, Seoul, 

Korea, Aug. 18-22, 1997.

45. Syrimis, M., and D. N. Assanis, “Characterization of Knocking Combustion 

and its Dispersion,” ASME-ICE Fall Technical Conference, Madison, WI, 

Sept. 27 - Oct. 1, 1997.

46. Agarwal, A., and Assanis, D. N., “Multi-Dimensional Modeling of Natural Gas 

Ignition under Compression Ignition Conditions Using Detailed Chemistry,” 

SAE Paper 980136, SAE International Congress and Exposition, Detroit, MI, 

Feb. 23-26, 1998.

47. Agarwal, A., and Assanis, D. N., “Multi-Dimensional Modeling of Nitric 

Oxide Formation in Direct-Injection Natural Gas Engines, COMODIA 98, 

Proceedings of the Fourth International Symposium on Diagnostics and 

Modeling of Combustion in Internal Combustion Engines, 561-566, Kyoto, 

July 20-23, 1998.

48. Assanis, D.N., Filipi, Z.S., Fiveland, S.B., and Syrimis, M., “A Predictive 

Ignition Delay Correlation Under Steady-State and Transient Operation of a 

Direct-Injection Diesel Engine,” Presented at ASME-ICE Fall Technical 

Conference, Ann Arbor, MI, October 16-20, 1999.

49. Nishimura, A., and D. N. Assanis, “A Model for Primary Diesel Fuel 

Atomization Based on Cavitation Bubble Collapse Energy," Eight International 
Assanis, 59 

Conference on Liquid Atomization and Spray Systems, ICLASS-2000, 

Pasadena, CA, July 16-20, 2000.

50. Filipi, Z., Wang, Y., and Assanis, D. N., “Effect of Variable Geometry Turbine 

(VGT) on Diesel Engine and Vehicle System Transient Response,” Presented 

as SAE Paper 2001-01-1247, SAE World Congress, Detroit, MI, March 5-8, 

2001.

51. Lin, C. C., Filipi, Z., Wang, Y., Louca, L., Peng, H., Assanis, D. N. and Stein, 

J., “Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in 

SIMULINK and its Use for Power Management Studies,” Presented as SAE 

Paper 2001-012-1334, included in Advanced Hybrid Vehicle Powertrains, SP1607, SAE World Congress, Detroit, MI, March 5-8, 2001.

52. Buyuktur, S., Wooldridge, M. and D. N. Assanis, “Development of a ForwardLooking Fuel Cell Vehicle Simulation,” 2001 Global Powertrain Congress, 

June 5-7, 2001, Detroit, MI.

53. Grover, R. and D. N. Assanis, “A Spray Wall Impingement Model Based Upon 

Conservation Principles,” COMODIA 2001, Proceedings of the Fifth 

International Symposium on Diagnostics and Modeling of Combustion in 

Internal Combustion Engines, Nagoya, Japan, July 1-4, 2001.

54. Hong, S. J., D. N. Assanis and M. Wooldridge, “Multi-Dimensional Modeling 

of NO and Soot Emissions with Detailed Chemistry and Mixing in a Direct 

Injection Natural Gas Engine,” SAE Paper 2002-01-1112, Session on MultiDimensional Engine Modeling, 2002 SAE World Congress, Detroit, MI, 

March 4-7, 2002.

55. Bohac, S. and D. N. Assanis, “Quantification of Local Ozone Production 

Attributable to Automobile Hydrocarbon Emissions,” SAE Paper 2001-01-

3760, 2001; presented at Environmental Sustainability Conference and 

Exhibition: Land, Sea and Air Mobility, Graz, Austria, April 8-10, 2002.

56. Grover, R.O., Assanis, D. N., Lippert, A.M., El Tahry, S.H., Drake, M.C., 

Fansler, T.D., Harrington, D.L., “A Critical Analysis of Splash Criteria for 

SIDI Spray Impingement,” ILASS Americas 2002, Madison, WI, May 14-17, 

2002.

57. Chryssakis, C. A., Driscoll, K.D., Sick, V., and D. N. Assanis, “Validation of 

an Enhanced Liquid Sheet Atomisation Model Against Quantitative Laser 

Diagnostic Measurements,” ILASS-Europe 2002, Zaragoza, Spain, September 

9-11, 2002.

58. Wu, B., Lin, C.-C., Filipi, Z., Peng, H., and D. N. Assanis, “Optimization of 

Power Management Strategies for a Hydraulic Hybrid Medium Truck“, 

Proceedings of the 6th International Symposium on Advanced Vehicle Control, 

Hiroshima, Japan, September 2002. 

59. Louca, L., Kokkolaras, M., Delagrammatikas, G., Michelena, N., Filipi, Z., 

Papalambros, P., and D. N. Assanis, “Analytical Target Cascading for the 

Design of an Advanced Technology Truck,” IMECE 2002-32860, 2002 ASME 
Assanis, 60 

International Mechanical Engineering Congress and Exposition, New Orleans, 

LA, November 17-22, 2002.

60. Delagrammatikas, G. and D. N. Assanis, “Optimization of Advanced Engine 

Controls for Conventional Vehicles: A Driving Cycle Perspective,” submitted 

to Symposium on Advanced Automotive Technologies, IMECE 2002-32087, 

ASME International Mechanical Engineering Congress and Exposition, New 

Orleans, LA, November 17-22, 2002.

61. Jacobs, T. J., Assanis, D. N., and Z. S. Filipi, “The Impact of Exhaust Gas 

Recirculation on Performance and Emissions of a Heavy-Duty Diesel Engine,” 

SAE Paper, 2003-01-1068, 2003 SAE World Congress, Detroit, MI, March 3-

6, 2003.

62. Chryssakis, C. A., Assanis, D. N., Lee, J. K., Nishida, K., “Fuel Spray 

Simulation of High-Pressure Swirl-Injector for DISI Engines and Comparison 

with Laser Diagnostic Measurements,” SAE Paper 2003-01-0007, 2003 SAE 

World Congress, Detroit, MI, March 3-6, 2003.

63. Chryssakis, C. A., Assanis, D. N., Lee, J. K., Nishida, K., “An Investigation of 

the Breakup Mechanisms for Swirl Sprays From High-Pressure Swirl 

Injectors,” ICLASS 2003, Sorrento, Italy, July 13-18, 2003.

64. Kazancioglu, E., Wu, G., Ko, J., Bohac, S., Filipi, Z., Hu, S. J., Assanis, D. N., 

and Saitou, K., Robust Optimization of an Automotive Valvetrain Using a 

Multi-Objective Genetic Algorithm, Paper DETC 2003/DAC-48714, 

Proceedings of DETC’03 ASME 2003 Design Technical Conference, Chicago, 

IL, Sept. 2-6, 2003.

65. Zeng, P. and D. N. Assanis, “Time-Resolved Heat Transfer in Engine Intake 

Manifold,” TRCON-03 International Symposium on Transient Convective 

Heat and Mass Transfer in Single and Two-Phase Flows, Cesme, Turkey, 

August 17-22, 2003.

66. Babajimopoulos, A., Lavoie, G. A. and D. N. Assanis, “Modeling HCCI 

Combustion with High Levels of Residual Gas Fraction – A Comparison of 

Two VVA Strategies”, SAE Paper 2003-01-3220, 2003 SAE International 

Powertrain and Fluid Systems Conference, Oct.27-30, Pittsburgh, PA.

67. Hong, S.J, Assanis, D. N., Wooldridge, M. S., Im, H.G., Kurtz, E. M., and H. 

Pitsch, “Modeling of Diesel Combustion and NO Emissions Based on a 

Modified Eddy Dissipation Concept,” SAE Paper 2004-01-0107, 2004 SAE 

World Congress, Detroit, MI, March 8-11, 2004. 

68. Zeng, P. and D. N. Assanis, “Cylinder Pressure Reconstruction and its 

Application to Heat Transfer Analysis,” SAE Paper 2004-01-0922, 2004 SAE 

World Congress, Detroit, MI, March 8-11, 2004.

69. Grover, R. O., Assanis, D. N., and A.M. Lippert, “A Comparison of Classical 

Atomization Models against Current Experimental Measurements within a 

Zero-Dimensional Framework,” ILASS Americas, 17th Annual Conference on 

Liquid Atomization and Spray Systems, Arlington, VA, May 2004.
Assanis, 61 

70. Vanzieleghem, B., Assanis, D., Im, H.G., “Modeling of Gasoline Direct 

Injection Combustion using KIVA-3V: Development of an Extended Coherent 

Flamelet Model and Validation with Optical Engine Planar Laser Induced 

Fluorescence Measurements”, COMODIA 2004, Yokohama, Japan, August 

2004.

71. Vanzieleghem, B.P., C.A. Chryssakis, R.O. Grover, V. Sick and D.N. Assanis, 

“Modeling of Gasoline Direct Injection Mixture Formation with KIVA-3V and 

Validation with Optical Engine Planar Laser Induced Fluorescence 

Measurements: Development of Spray Breakup and Wall Impingement 

Models,” COMODIA 2004, Yokohama, Japan, August 2004.

72. Zeng, P., Prucka, R. G., Filipi, Z. S., and D. N. Assanis, “Reconstructing 

Cylinder Pressure of a Spark-Ignition Engine for Heat Transfer and Heat 

Release Analysis,” ASME Paper ICEF 2004-886, ASME Internal Combustion 

Engine Technical Conference, Long Beach, CA, October 24-27, 2004.

73. Zeng, P., and D.N. Assanis, “Unsteady Convective Heat Transfer Modeling 

and Application to Engine Intake Manifolds,” IMECE Paper 2004-60068, 2004 

ASME International Mechanical Engineering Congress and R&D Exposition, 

Anaheim, CA, Nov 13-19, 2004.

74. Zeng, P., and D.N. Assanis, “The Development of a Computer-Based Teaching 

Tool for Internal Combustion Engine Courses,” IMECE Paper 2004-61998, 

2004 ASME International Mechanical Engineering Congress and R&D 

Exposition, Anaheim, CA, Nov 13-19, 2004.

75. Cho, H., Jung, D., Filipi, Z., Assanis, D. N., Bryzik, W., and J. Vanderslice, 

“Application of Controllable Electric Coolant Pumps for Fuel Economy and 

Cooling Performance Improvement,” IMECE Paper 2004-61056, 2004 ASME 

International Mechanical Engineering Congress and R&D Exposition, 

Anaheim, CA, Nov 13-19, 2004.

76. Chryssakis, C., Assanis, D.N., Kook, S., and C. Bae, “Effect of Multiple 

Injections on Fuel-Air Mixing and Soot Formation in Diesel Combustion Using 

Direct Flame Visualization and CFD Techniques,” ASME Paper ICES2005-

1016, ASME Internal Combustion Engine Technical Conference, Chicago, IL, 

April 5-7, 2005.

77. Knafl, A., Hagena, J., Filipi, Z., and D. N. Assanis, “Dual Use Engine 

Calibration: Leveraging Modern Technologies to Optimize Performance and 

Emissions Trade-offs,” SAE Paper 2005-01-1549, SP-1962, 2005 SAE World 

Congress, Detroit, MI, April 11-14, 2005.

78. Assanis, D. N., Cho, W., Choi, I., Ickes, A., Jung, D., Martz, J., Nelson, R., 

Sanko, J., Thompson, S., Brevick, J.E., and B. Inwood, “Pressure Reactive 

Piston Technology Investigation and Development for Spark Ignition 

Engines,” SAE Paper 2005-01-1648, Session on CI and SI Power Cylinder 

Systems, SP-1964, 2005 SAE World Congress, Detroit, MI, April 11-14, 2005. 

79. Lee, S., Bae, C., Prucka, R., Fernandes, G., Filipi, Z. S., and D. N. Assanis, 

“Quantification of Thermal Shock in a Piezoelectric Pressure Transducer,” 
Assanis, 62 

SAE Paper 2005-01-2092, SAE Brazil Fuels & Lubricants Meeting & 

Exhibition (Co-sponsored by SAE International), Rio de Janeiro, Brazil, May 

11-13, 2005.

80. Chryssakis, C.A. and D.N. Assanis, “A Secondary Atomization Model for 

Liquid Droplet Deformation and Breakup under High Weber Number 

Conditions,” 18th Annual Conference on Liquid Atomization and Spray 

Systems, Irvine, CA, May 22-25, 2005.

81. Sampara, C. Depick, C., and D.N. Assanis, “Framework for Modeling the 

Components of a Fuel Processing System for Fuel Cell Applications,” IMECE 

Paper 2005-81330, IMECE 2005 Proceedings, 2005 ASME Design 

Engineering Conference, Orlando, FLA, November 5-11, 2005.

82. Hamosfakidis, V, Im, H., and D.N. Assanis, “A Regenerative Multiple 

Flamelet Model for PPCI Engine Simulations,” Eastern States Section 

Combustion Institute Fall Technical Meeting, November 13-16, 2005.

83. Cho, W., Jung, D. and D.N. Assanis, “Numerical Investigation of Pressure 

Reactive Piston Technology in a Spark-Ignition Engine,” Paper 20056083, 

18th International Combustion Engine Symposium, Jeju Island, Korea, 

December 20-22, 2005.

84. Jacobs, T. J., Knafl, A., Bohac, S.V., Assanis, D.N., and P.G. Szymkowicz, 

“The Development of Throttled and Unthrottled PCI Combustion in a LightDuty Diesel Engine,” SAE Paper 2006-01-0202, 2006 SAE Congress, Detroit, 

MI, April 3-6, 2006.

85. Hagena, J.R., Filipi, Z.S., and D.N. Assanis, “Transient Diesel Emissions: 

Analysis of Engine Operation During a Tip-In”, SAE Paper 2006-01-1151, 

2006 SAE Congress, Detroit, MI, April 3-6, 2006. 

86. Chryssakis, C.A., Assanis, D. N., and C. Bae, “Development and Validation of 

a Comprehensive CFD Model of Diesel Spray Atomization Accounting for

High Weber Numbers,” SAE Paper 2006-01-1546, SP-2010, 2006 SAE 

Congress, Detroit, MI, April 3-6, 2006. 

87. Jung, D., and D.N. Assanis, “Numerical Modeling of Cross Flow Compact 

Heat Exchanger with Louvered Fins using Thermal Resistance Concept,” 2006 

SAE Congress, Detroit, MI, April 3-6, 2006.

88. Malikopoulos, A., Filipi, Z., and D.N. Assanis, “Simulation of an Integrated 

Starter Alternator (ISA) System for the HMMWV,” SAE Paper 2006-01-0442, 

SP-2008, 2006 SAE Congress, Detroit, MI, April 3-6, 2006..

89. Yoo, S., Jung, D., and D.N. Assanis, “Numerical Modeling of the Proton 

Exchange Membrane Fuel Cell for Thermal Management,” Paper FUELCELL 

2006-97062, 4th International Conference on Fuel Cell Science, Engineering 

and Technology, Irvine, CA, June 19-21, 2006.

90. Knafl, A., Jacobs, T., Bohac, S.V., and D. N. Assanis, “The Load Limits of 

Low-Temperature Premixed Compression Ignition Diesel Combustion,” ISCE 
Assanis, 63 

20006, 2nd International Symposium on Clean and High Efficiency Combustion 

Engines, Tianjin, China, July 10-13, 2006.

91. Hamosfakidis, V. Im, H. and D.N. Assanis, “A Regenerative Multiple Flamelet 

Model (RMF),” Poster Presentation, 31st International Symposium on 

Combustion, Heidelberg, Germany, August 6-11, 2006.

92. Filipi, Z., Hagena, J., Fathy, H., Assanis, D., Stein, J., “Investigating Effects of 

Transients on Diesel Emissions using Engine-in-the-Loop Testing”, THIESEL 

2006 Conference on Thermo- and Fluid Dynamic Processes in Diesel Engines, 

Valencia, Spain, September 2006. 

93. Grannell, S.M., Assanis, D.N., Bohac, S.V., and D. E. Gillespie, “The 

Operating Features of a Stoichiometric, Ammonia and Gasoline Dual Fueled 

Spark Ignition Engine,” Paper IMECE2006-13048, Proceedings of 

IMECE2006 2006 ASME International Mechanical Engineering Congress and 

Exposition, Chicago, IL, November 5-10, 2006. 

94. Hamosfakidis, V., Kobiera, A., Im, H., and D.N. Assanis, “A Two Conserved 

Scalar Modeling for HCCI Applications,” 5th UC Combustion Meeting, March 

25-28, 2007, San Diego, CA.

95. Jacobs, T.J., and D.N. Assanis, “On the Sensitivity of NOX to Exhaust Gas 

Recirculation in a Premixed Compression Ignition Engine,” 5th US Combustion 

Meeting, March 25-28, 2007, San Diego, CA.

96. Chang, K.J., Lavoie, G.A., Babajimopoulos, A., Filipi, Z.S., and D.N. Assanis, 

“Control of a Multi-Cylinder HCCI Engine during Transient Operation by

Modulating Residual Gas Fraction to Compensate for Wall Temperature 

Effects,” SAE Paper 2007-01-0204, SAE 2007 World Congress, Detroit, MI, 

April 16-19, 2007.

97. Hattori, K., Murotani, T., Sato, E., Chryssakis, C., Babajimopoulos, A., and 

D.N. Assanis, “Simultaneous Reduction of NOx and Soot in a Heavy-Duty 

Diesel Engine by Instantaneous Mixing of Fuel and Water,” SAE Paper 2007-

01-0125, SAE 2007 World Congress, Detroit, MI, April 16-19, 2007.

98. Knafl, A., Han, M., Bohac, S.V., Assanis, D.N., and P.G. Szymkowicz, 

“Comparison of Diesel Oxidation Catalyst Performance on an Engine and a 

Gas Flow Reactor,” SAE Paper 2007-01-0231, SAE 2007 World Congress, 

Detroit, MI, April 16-19, 2007.

99. Baglione, M., Duty, M., Ni, J., and D.N. Assanis, “Reverse Dynamic 

Optimization Methodology for Maximizing Powertrain System Efficiency,” 

Fifth IFAC Symposium on Advances in Automotive Control, Monterey Coast, 

CA, August 20-22, 2007. 

100. Hamosfakidis, V., Kobiera, A., and D.N. Assanis, “A Regenerative Multiple 

Flamelet Model for non-Premixed Combustion with non-Uniform EGR,” 5th 

IASME/WSEAS International Conference on Heat Transfer, Thermal 

Engineering and Environment, Vouliagmeni, Athens, Greece, August 25-27, 

2007.
Assanis, 64 

101. Malikopoulos, A., Papalambros, P.Y., and D.N. Assanis, “A Learning 

Algorithm for Optimal Internal Combustion Engine Calibration in Real Time,” 

ASME 2007 International Design Engineering Technical Conference and 

Computers and Information in Engineering Conference, Las Vegas, Nevada, 

September 4-7, 2007.

102. Sethu, C., Leustek, M., Bohac, S.V., Assanis D.N. and Z.S. Filipi, “An 

Investigation in Measuring Crank-Angle Resolved In-Cylinder Engine Friction 

Using Instantaneous IMEP Method,” Powertrain & Fluid Systems Conference 

& Exhibition, Donald E. Stephens Convention Center, Rosemont (Chicago), 

IL, October 29 - November 1, 2007.

103. Malikopoulos, A., Papalambros, P.Y., and D.N. Assanis, “A New StateRepresentation Learning Model for Sequential Decision-Making Problems 

Under Uncertainty,” IMECE 2007-41258, Proceedings of 2007 ASME

International Mechanical Engineering Congress and Exposition, Seattle, 

Washington, November 10-16, 2007.

104. Cho, K., Assanis, D., Filipi, Z., Szekely, G., Najt, P. and R. Rask, 

“Investigation of Combustion and Heat Transfer in a Direct Injection Spark 

Ignition (DISI) Engine through Instantaneous Combustion Chamber Surface 

Temperature Measurements,” Internal Combustion Engines: Performance, Fuel 

Economy and Emissions, Institution of Mechanical Engineers, Combustion 

Engines and Fuels Group, London, England, December 11-12, 2007.

105. Malikopoulos, A., Papalambros, P.Y., and D.N. Assanis, “Optimal Engine 

Calibration for Individual Driving Styles,” 2008 SAE International Congress 

and Exposition, Detroit, MI, April 14-17, 2008.

106. Lee, B., Jung, D., Assanis, D.N., and Z.S. Filipi, “Dual-Stage Turbocharger 

Matching and Boost Control Options,” ASME Paper ICES 2008-1692, 

Proceedings of the ASME Internal Combustion Engine Division 2008 Spring 

Technical Conference, Chicago, IL, April 27-30, 2008.

107. Guralp, O., Hoffman, M., Assanis, D.N., Filipi, Z., Kuo, T.W., Najt. P. and R. 

Rask, “Thermal Characterization of Combustion Chamber Deposits on the 

HCCI Engine Piston and Cylinder Head Using Instantaneous Temperature 

Measurements,” SAE Paper 2009-01-0668, SAE 2009 International Congress 

and Exposition, Detroit, MI, April 20-23, 2009.

108. Abarham, M., Hoard, J., Assanis, D.N., Styles, D., Curtis, E., Ramesh, N., 

Sluder, C.S., and J, Storey, “Numerical Modeling and Experimental 

Investigations of EGR Cooler Fouling in a Diesel Engine,” SAE Paper 2009-

01-1506, SAE 2009 International Congress and Exposition, Detroit, MI, April 

20-23, 2009.

109. Babajimopoulos, A., Challa, P.V.S.S., Lavoie, G., and D.N. Assanis, “ModelBased Assessment of Two Variable Cam Timing Strategies for HCCI Engines: 

Recompression Vs. Rebreathing,” ICES Paper 2009-76103, Proceedings of the 

ASME Internal Combustion Engine Division 2009 Spring Technical 

Conference ICES2009, Milwaukee, WI, May 3-6, 2009. 
Assanis, 65 

110. Martz, J.B., Kwak, H., Im, H.G., Lavoie, G.A, Assanis, D.N. and S.B. 

Fiveland, “Propagation of a Reacting Front in an Auto-Igniting Mixture”,

Proceedings of the 6th US National Combustion Meeting, Ann Arbor, MI, May 

17-20, 2009.

111. Grannell, S., Assanis, D.N., Gillespie, D. and S.V. Bohac, “Exhaust Emissions 

from a Stoichiometric, Ammonia and Gasoline Dual Fueled Spark Ignition 

Engine,” ICES2009-76131, Proceedings of the ASME Internal Combustion 

Engine Division 2009 Spring Technical Conference ICES2009, Milwaukee, 

WI, May 3-6, 2009.

112. Ickes, A., Assanis D.N. and S. Bohac, “Load Limits with Fuel Effects of a 

Premixed Diesel Combustion Mode,” SAE Paper 2009-01-1972, SAE 2009 

International Powertrains, Fuels and Lubricants Meeting, Florence, Italy, June 

15-17, 2009.

113. Klinkert, S., Hoard, J.W., Sathasivam, S. R., Assanis, D.N., and S.V. Bohac, 

“Design of a Flow Reactor for Testing Multi-Brick Catalysts Systems Using 

Rapid Exhaust Gas Composition Switches,” ASME Paper ICEF2009-14016, 

Presented as ASME Paper ICEF 2009-14063, ASME ICE Division Fall 

Technical Conference, Lucerne, Switzerland, September 20-24, 2009.

114. Keum, S., Im, H., and D.N. Assanis, “Computational Investigation of the 

Effect of Stratification on DI/HCCI Engine Combustion at Low Load 

Conditions,” SAE Paper 2009-01-2703, 2009 Powertrains, Fuels and 

Lubricants Meeting, San Antonio, TX, November 2-4, 2009.

115. Prucka, R., Lee, T.-K., Filipi, Z, and D. Assanis, “Turbulence Intensity 

Calculation from Cylinder Pressure Data in a High Degree of Freedom 

Engine,” SAE 2010 World Congress, Detroit, MI, April 13-15, 2010.

116. Han, D., Ickes, A.M., Bohac, S.V., Huang, Z., Assanis, D.N., “Premixed LowTemperature Combustion of Blends of Diesel and Gasoline in a High Speed 

Compression Ignition Engine,” Proceedings, 33rd Int. Symposium on 

Combustion, Beijing, China, Aug 1-6, 2010.

117. Martz, J.B., Kwak, H., Im, H.G., Lavoie, G.A., and D.N. Assanis, Combustion 

Regime of a Reacting Front Propagating into an Auto-Igniting Mixture, 

Proceedings, 33rd Int.. Symposium on Combustion, Beijing, China, Aug 1-6, 

2010. 

118. Northrop, W., Bohac, S., Assanis, D. and J.Y. Chin, “Comparison of Filter and 

Smoke Number and Elemental Carbon Mass from Partially Premixed Low 

Temperature Combustion in a Direct Injection Diesel Engine,” ASME 2010 

Internal Combustion Engine Division Fall Technical Conference, San Antonio, 

TX, September 12-15, 2010.

119. Smith, M., Filipi, Z., Schihl, P. and D.N. Assanis, “Effect of High Sulfur 

Military JP-8 Fuel on Heavy Duty Diesel Engine Emissions and EGR Cooler 

Condensate,” ICEF2010-35001, ASME 2010 Internal Combustion Engine 

Division Fall Technical Conference, San Antonio, TX, September 12-15, 2010.

120. Shingne, P, Assanis, D.N., Babajimopoulos, A., Keller, P., Roth, D., Becker, 

M., “Turbocharger Matching for a 4-Cylinder Gasoline HCCI Engine Using a 
Assanis, 66 

1D Engine Simulation,” SAE Paper 2010-01-2143, SAE 2010 Powertrain, 

Fuels and Lubricants Meeting, San Diego, CA, October 25-27, 2010.

121. Delorme, A., Rousseau, A., Wallner, T., Babajimopoulos, A. and D.N. Assanis, 

“Evaluation of Homogeneous Charge Compression Ignition (HCCI) Engine 

Fuel Savings for Various Electric Drive Powertrains,” The 25th World Battery, 

Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition,” Shenzhen, 

China, November 5-9, 2010.

122. Manofsky, L., Vavra, J., Assanis, D.N., and A. Babajimopoulos, “Bridging the 

Gap between HCCI and SI: Spark-Assisted Compression Ignition,” SAE Paper 

2011-01-1179, SAE 2011 World Congress, Detroit. MI, April 12-14, 2011.

123. Northrop, W., Assanis, D.N. and S. Bohac, “Evaluation of Diesel Oxidation 

Catalyst Conversion of Hydrocarbons and Particulate Matter from Premixed 

Low Temperature Combustion of Biodiesel,” SAE Paper 2011-01-1186, SAE 

2011 World Congress, Detroit. MI, April 12-14, 2011.

 

 Other Conference or Symposium Presentations 

1. Assanis, D. N., J. A. Ekchian, J. B. Heywood, and K. K. Replogle, "Computer 

Simulation of the Turbocompound Diesel Engine System," Proceedings of the 

Society of Automotive Engineers, 22nd Automotive Technology Development 

Contractor's Meeting, P-l55, 297-316, 1985.

 

2. Assanis, D. N., and E. Badillo, "Unsteady Analysis of Piston-Liner Heat 

Transfer in Insulated Diesel Engines," Invited Paper, Proceedings of the Heat 

Transfer Conference Honoring B. T. Chao, Urbana, IL, Oct. 1-2, 1987. 

3. Assanis, D. N., Wiese, K., Schwarz, E., and W. Bryzik, "Investigation of the 

Effect of Thin Ceramic Coatings on Diesel Engine Performance and Exhaust 

Emissions," Proceedings of the 1990 Coatings for Advanced Heat Engines 

Workshop, Castine, Maine, Aug. 6-10, 1990. 

4. Varnavas, C., and D. N. Assanis, "Critical Evaluation of the KIVA Evaporation 

Model for Engine Spray Calculations," Third International KIVA Users Group 

Meeting, Detroit, MI, Feb. 28, 1993. 

5. Agarwal, A., Papageorgakis, G. C., Paul, M., Rubas, P. J., Yuen, L. S., 

Coverdill, R. E., Lucht, R. P., Peters, J. E., and D. N. Assanis, "Direct Injection 

of Natural Gas: In-Cylinder Measurements and Calculations," Proceedings of 

Annual Automotive Technology Development Contractor's Coordination 

Meeting, Society of Automotive Engineers P-289, 147-156, Dearborn, MI, 

October 24-27, 1994. 

6. Assanis, D. N., "A Methodology for Characterizing the Thermal Behavior of 

Internal Combustion Engine Systems", invited presentation, Proceedings of 

The Best of German/American Automotive Technology Conference, Southfield, 

MI, June 27-28, 1995.

7. Assanis, D. N., "A Methodology for Characterizing the Thermal Behavior of 

Internal Combustion Engine Systems", invited presentation, Engineering 

Foundation Conference, Shonan Village, Japan, September 23-29, 1995.
Assanis, 67 

8. Papageorgakis, G., Agarwal, A., and D. N. Assanis, "Multi-Dimensional 

Modeling of Natural Gas Injection, Glow Plug Ignition, and Combustion with 

the KIVA-3 Code: The Effect of Piston Crown Geometry," Sixth International 

KIVA Users Group Meeting, Detroit, MI, Feb. 25, 1996.

9. Papageorgakis, G., Agarwal, A., and D. N. Assanis, "Multi-Dimensional 

Modeling of Natural Gas Injection, Glow Plug Ignition, and Combustion with 

the KIVA-3 Code, Poster Session, Annual DOE Automotive Technology 

Development Customers’ Coordination Meeting, Dearborn, MI, Oct. 28 - Nov. 

1, 1996. 

10. Assanis, D. N., “3-D Modeling of Engine Reacting Flows: Promises and 

Challenges,” invited paper, Panel on Automotive Applications of CFD, 

Atlanta, 1996 ASME International Mechanical Engineering Congress and 

Exposition, Atlanta, GA, Nov. 17-22, 1996.

11. Papageorgakis, G., and D. N. Assanis, “Implementation and Assessment of 

Alternative Turbulence Models in KIVA-3,” Seventh International KIVA 

Users Group Meeting, Detroit, MI, Feb. 25, 1996.

12. Assanis, D. N., “Engine Friction Measurements,” invited presentation, Panel 

on Surface Engineering and Tribology, SAE International Congress and 

Exposition, Detroit, MI, Feb. 23-26, 1998.

13. Assanis, D. N., “Engine Friction Measurements,” Keynote Presentation, DOE 

Workshop on Research Needs for Reducing Friction and Wear in 

Transportation, Argonne National Laboratory, March 22-23, 1999. 

14. Delagrammatikas, G. and D.N. Assanis, “Development and Use of a 

Regenerative Braking Model in ADVISOR,” ADVISOR User Conference 

Proceedings, Costa Mesa, CA, Aug. 24-25, 2000.

15. Assanis, D.N., Louca, L., and Z. Filipi, “Drivetrain Simulation and Modeling 

Based Upshift Control,” Modern Advances in Automatic Transmission 

Technology TPOTEC, Ypsilanti, MI, Aug. 29-30, 2002.

16. Assanis, D. N. and S. Tung, “Overview of Engine Friction and Wear 

Measurements,” Future Trends in Engine Design and Tribology, Society of 

Tribologists and Lubrication Engineers, Rochester, MI, August 22, 2001.

17. Assanis, D. N., “Modeling of Hybrid Vehicle Systems”, invited presentation, 

7th International Conference on Present and Future Engines for Automobiles, 

Delphi, Greece, May 27-31, 2001.

18. Assanis, D. N., “Discussion of the National Research Council Report on 

Corporate Average Fuel Economy,” SAE President’s Invited Panel, 2002 SAE 

International Congress and Exhibition, 2002 SAE World Congress, Detroit, 

MI, March 4-7, 2002.

19. Fiveland, S. and D. N. Assanis, “A Quasi-Dimensional HCCI Model for 

Performance and Emissions Studies,” Ninth International Conference on 

Numerical Combustion, Sorrento, Italy, April 7-10, 2002.
Assanis, 68 

20. Assanis, D. N., “Does the Internal Combustion Engine Have a Future?”, The 

Advanced Power Technology Forum, Management Briefing Seminars 2002, 

Traverse City, MI, August 5-9, 2002.

21. Assanis D. N., “Does the Internal Combustion Engine Have a Future?”, 

invited plenary speaker, session on “Future Automotive Powertrains,” Global 

Powertrain Congress, Ann Arbor, MI, September 24-26, 2002.

22. Assanis, D.N., “Securing a Successful Academic Career,” invited panelist, 

ASME IMECE, New Orleans, LA, November 17-22, 2002.

23. Bohac, S., Assanis, D.N., and H.L.S Holmes, “Speciated Hydrocarbon 

Emissions from a Contemporary Automotive Gasoline Engine and Local 

Ozone Production,” Anachem Symposium, Livonia, MI, November 21, 2002.

24. Filipi, Z. S., Wu, B., Lin, C.C., and D. N. Assanis, “Fuel Economy Potential of 

Hydraulic Hybrid Propulsion Systems for Medium Trucks,” SAE International 

Truck and Bus Meeting and Exhibition, Cobo Center, Detroit, MI, November 

18-20, 2002.

25. Assanis. D.N., “Internal Combustion Engines and Hybrids: They are Here to 

Stay,” Testimony to State of Michigan’s Senate Technology and Policy 

Committee,” Farnum Building, Lansing, MI, February 19, 2003.

26. Assanis, D.N., “A University Consortium on Homogeneous Charge 

Compression Ignition Engine Research,” invited speaker, International 

Workshop on Advanced Combustion and Fuels,” Argonne National 

Laboratory, Argonne, IL, June 16-17, 2003.

27. Assanis, D.N., “Major Research Issues,” invited panelist, International 

Workshop on Advanced Combustion and Fuels,” Argonne National 

Laboratory, Argonne, IL, June 16-17, 2003.

28. Vanzieleghem, B.P., Chryssakis, C.A., Grover, R.O., Assanis, D.N., Im, H.G., 

and V. Sick, “Gasoline Direct Injection Modeling and Validation with Engine 

Planar Laser Induced Fluorescence Experiments,” 14th International 

Multidimensional Engine Modeling User’s Group Meeting, Detroit, MI, March 

2004. 

29. Depcik, C., and D.N. Assanis, “One-Dimensional Catalyst Modeling and its 

Application to Urea SCR Devices,” Seventh CLEERS Workshop, Detroit 

Diesel, Detroit, MI, June 2004.

30. Assanis, D.N., et al., “Clean and Controllable, Advanced Compression Ignition 

Engine System for Improved Power Density and Fuel Economy”, plenary 

session presentation at the Annual ARC Conference on “Critical Technologies 

for Modeling and Simulation of Ground Vehicles”, Ann Arbor, May 2004.

31. Babajimopoulos, A., Assanis, D.N., Flowers, D.L., Aceves, S.M., and R.P. 

Hessel, “A Fully Integrated CFD and Multi-Zone Model with Detailed 

Chemical Kinetics for the Simulation of PCCI Engines,” 15th International 

Multidimensional Engine Modeling User’s Group Meeting, Detroit, MI, April 

2005. 
Assanis, 69 

32. Assanis, et al., “Engine-In-the-Loop Simulation: A Design and Evaluation Tool 

for Advanced Propulsion Systems”, plenary session presentation at the Annual 

ARC Conference on “Critical Technologies for Modeling and Simulation of 

Ground Vehicles”, Ann Arbor, May 2005. 

33. Assanis, D. N., “Bridging the Gap between Fundamental Physics and 

Chemistry and Applied Models for HCCI Engines”, invited presentation, 9th

International Conference on Present and Future Engines for Automobiles, San 

Antonio, TX, May 29 to June 2, 2005.

34. Assanis, D. N., “Bridging the Gap between Fundamental Physics and 

Chemistry and Applied Models for HCCI Engines”, invited presentation, 11th

International Conference on Diesel Engine Emissions Reduction DEER 2005, 

Chicago, IL, August 21-25, 2005.

35. Leustek, M.E., Sethu, C., Bohac, S., Filipi, Z., and D.N. Assanis, “Crank-angle 

Resolved In-Cylinder Friction Measurements with the Instantaneous IMEP 

Method”, Proceedings of World Tribology Congress III, Washington D.C., 

Sept. 2005. 

36. Assanis, D.N., et al., “Integrative Approach to Advanced Propulsion System 

Design Using Simulation and Engine-In-the-Loop”, plenary session 

presentation at the Annual ARC Conference on “Critical Technologies for 

Modeling and Simulation of Ground Vehicles”, Ann Arbor, May 2006. 

37. Assanis, D. N., “Low Temperature Combustion for High Efficiency Ultra Low 

Emissions Engines”, invited presentation, 12th International Conference on 

Diesel Engine Efficiency and Emissions Reduction DEER 2006, Detroit, MI, 

August 20-24, 2006.

38. Assanis, D. N., “Analysis and Control of HCCI Engine Transient Operation 

Using 1-D Cycle Simulation and Thermal Networks”, invited presentation, 

SAE HCCI Engine Symposium, San Ramon, CA, September 24-26, 2006. 

39. Assanis, D. N., “Next Generation Powertrains and Fuels: Grand Challenges 

and Opportunities”, invited presentation, UM Symposium on Energy Science, 

Technology and Policy, Ann Arbor, MI, February 13-14, 2007.

40. Assanis, D.N., “Energy Research: Grand Challenges and Opportunities,” 

invited talk, Lehigh University, Bethlehem, PA, February 2, 2007.

41. Assanis, D.N., “Today’s Students, Tomorrow’s Engineers,” invited panelist, 

SAE 2007 World Congress, Detroit, MI, April 16-19, 2007.

42. Assanis, D.N., et al, “Energy and Power for Military Vehicles: Alternative 

Fuels and Hybrid Propulsion”, plenary session presentation at the Annual ARC 

Conference on “Critical Technologies for Modeling and Simulation of Ground 

Vehicles”, Ann Arbor, May 2007. 

43. Assanis, D. N., “On Modeling HCCI Engine Transient Behavior”, invited 

presentation, 10th International Conference on Present and Future Engines for 

Automobiles, Rhodes, Greece, May 28 to June 5, 2007.
Assanis, 70 

44. Assanis, D.N., “TechKnow: Alternative Fuel Cars,” invited panelist, Power 

Center, Ann Arbor, MI, June 12, 2007.

45. Assanis, D.N., “Analysis and Control of HCCI Engine Transient Operation”, 

invited presentation, Homogeneous Charge Compression Ignition (HCCI) 

Symposium, Lund, Sweden, September 12-14, 2007.

46. Assanis. D.N., “Low Temperature Combustion for High Efficiency, Ultra-Low 

Emission Engines” invited talk, University of Illinois at Urbana-Champaign, 

April 1, 2008.

47. Middleton, R. and D. N. Assanis, “Nitrogen Oxides Oxidation as a Function of 

Lean NO Trap Loading,” 11th DOE Crosscut Workshop on Lean Emissions 

Reduction Simulation, University of Michigan - Dearborn, May 13 - 15, 2008. 

48. Assanis, D.N., in collaboration with G. Lavoie and A. Babajimopoulos, 

“Advanced Combustion for High Efficiency Ultra-Clean Engines,” Keynote 

Lecture, 6th US National Combustion Meeting, Ann Arbor, MI, May 17-20, 

2009.

49. Assanis, D.N., Invited Panelist on “Secure, Low-Carbon Transportation 

System,” Workshop on Formulation of A Bipartisan Energy and Climate 

Policy: Toward an Open and Transparent Process, The Howard H. Baker Jr. 

Center for Public Policy and the Widrow Wilson International Center for 

Scholars, Washington, DC, June 18-19, 2009.

50. Assanis, D.N., “On the Road to Clean and Efficient Powertrains,” invited 

presentation, UMTRI Symposium on Powertrain Strategies for the 21st

Century: How Are New Regulations Affecting Company Strategies?”, Ann 

Arbor, MI, July 15, 2009.

51. Assanis, D.N., Invited Panelist on “Future Transportation and Energy Policy,” 

5th International IEEE Vehicle Power and Propulsion Conference VPPC 2009, 

Dearborn MI, September 10, 2009. 

52. Assanis, D.N., Invited Keynote Speaker, “Advanced Combustion for High 

Efficiency Ultra Clean Engines,” American Filtration Society, 4th Biennial 

Conference on Emission Solutions in Transportation, Ann Arbor, MI, October 

5-8, 2009. 

53. Assanis, D.N., Invited Keynoter for Opening Ceremony, “The Business of 

Plugging-In”, Motorcity Hotel and Conference Center, October 19-21, 2009.

54. Assanis, D.N, Invited Panelist on “High Efficiency IC Engines,” SAE 2009 

Powertrains, Fuels and Lubricants Meeting, San Antonio, TX, November 2-4, 

2009.

55. Assanis, D.N., Invited Panelist on Alternative Energy Sources, “Meeting the 

Energy Challenge: The Role of Biofuels in Solving Society’s Largest Problem 

in the 21st Century”, Energy for the Future Conference, University of 

Dearborn, MI, March 16, 2010

56. Assanis, D.N, Invited Panelist on “Pathways to High Efficiency IC Engines,” 

SAE 2010 World Congress, Detroit, MI, April 13-15, 2010.
Assanis, 71 

57. Assanis D.N., Invited Speaker, “Assessing Great Lakes Offshore Wind: A 

Partnership between the University of Michigan and Grand Valley State 

University,” University of Michigan Regents’ Meeting, Grand Rapids, MI, 

April 15, 2010.

58. Assanis, D.N., Ortiz-Soto, E., Babajimopoulos, A., and G. Lavoie, “DualMode SI-HCCI Operation for Improved Drive-Cycle Fuel Economy:

Engine Modeling and Map Generation Framework,” Invited presentation to 

USCAR, Southfield. MI, May 12, 2010.

59. Assanis, D. N., “The Road to Clean Vehicles,” invited lecture, Zhejiang 

Automotive Institute, Hangzhou, China, May 29, 2010.

60. Assanis, D.N, Invited Speaker on “Pathways to High Efficiency I.C. Engines,” 

11th International Conference on Present and Future Engines for Automobiles, 

Shanghai, China, May 30-June 3, 2010.

61. Assanis, D.N., Invited Plenary Speaker, “Towards Carbon Neutral Vehicles,” 

Emissions 2010, Ann Arbor, MI, June 14-16, 2010.

62. Assanis, D.N., “A University Consortium on High Pressure Lean Combustion 

for Efficient and Clean Internal Combustion Engines,” 16th Directions 

in Engine-Efficiency and Emissions Research (DEER) Conference, September 

27-30, 2010, Detroit, Michigan.

63. Assanis, D.N., Invited Speaker, “Thermodynamic Lessons Learned from 

Lean/Dilute Burn Diesels to Improve Gasoline Engine Efficiency,” invited 

presentation, Cummins Science and Technology Council Advisory Board 

Meeting, Columbus, IN, October 6-8, 2010.

64. Assanis, D.N., Invited Speaker, “U.S.-China Clean Energy Research Center for 

Clean Vehicles”, UMTRI Focus on the Future Automotive Research 

Conferences, Inside China: Understanding China’s Current and Future 

Automotive Industry, The University of Michigan League, Ann Arbor, MI, 

November 10, 2010.

65. Assanis, D.N., Invited Panelist, Erb Institute Conference, “Michigan-China 

Clean Tech: Collaboration and Competition in Energy, Smart Grid, Green 

Cities and Transportation,” The University of Michigan Union, December 10, 

2010.

 Books Edited 

Uzkan, T., and Assanis, D. N., Editors, “Advanced Engine Simulations, 

Volume 1, Proceedings of the 1997 ASME-ICE Spring Technical 

Conference, ICE-Vol. 28-1, ASME, 1997.

Assanis, D.N., Papalambros, P.Y., and Bryzik, W., Guest Editors, Haug, E., 

Editor, Automotive Research Center Special Edition Issue, Mechanics of 

Structures and Machines, 27:4, 1999.
Assanis, 72 

Zhao, F., Asmus. T., Assanis, D. N., Dec. J. E., Eng, J. A., and P. M. Najt, 

Homogeneous Charge Compression Ignition (HCCI) Engines: Key Research 

and Development Issues, SAE PT-94, Society of Automotive Engineers, 

Warrendale, PA, 2003.

Assanis, D.N., Bryzik, W., Gorsich. D., and Haque, I., Guest Editors,

Automotive Research Center Special Edition Issue, International Journal of 

Heavy Vehicle Systems, 11:3/4, 372-402, 2004.

Cheng, W.K., Dibble, R., and D.N. Assanis, Guest Editors, International 

Journal of Engine Research, Special Issue on HCCI Engines, 6:5, 2005.

Chapters in Books 

Assanis, D.N., Borgnakke, C., Patterson, D.J., and Cole, D., "Internal 

Combustion Engines," Marks’ Standard Handbook for Mechanical 

Engineers, pp. 9-90 to 9-121, 10th Edition, McGraw-Hill Book Company, 

1996.

 Assanis, D.N., Lavoie, G. A. and S. B. Fiveland, “HCCI Engine Modeling 

Approaches,” pp. 529-655, published in Homogeneous Charge Compression 

Ignition (HCCI) Engines: Key Research and Development Issues, SAE PT94, Society of Automotive Engineers, Warrendale, PA, 2003.

Assanis, D.N., Cole, D., Jacobs, T.J., and D.J. Patterson, "Internal 

Combustion Engines," Marks’ Standard Handbook for Mechanical 

Engineers, pp. 9-93 to 9-127, 11th Edition, McGraw-Hill Book Company, 

2007. 

Chryssakis, A., Assanis, D.N. and F.X. Tanner, “Atomization Models,” 

Handbook of Atomization and Sprays: Theory and Applications, Springer, 

2011. 

 

Reports 

Assanis, D. N., “A Study of the Heat Transfer, Combustion and Emissions 

Characteristics of Low-Heat Rejection Diesel Engines,” U.S. Army TankAutomotive Command Research, Development and Engineering Center 

Technical Report No. 13589, June 1991. 

Poola, R. B., Sekar, R., and D.N. Assanis, “Application of Oxygen-Enriched 

Combustion for Locomotive Engines, Phase I,” Argonne National 

Laboratory Report ANL/ESD/TM-135, September 1996.

National Academy of Sciences Committee to Assess Fuel Economy 

Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board, “Technologies and Approaches to 

Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles,” 

Washington, DC, The National Academies Press, September 2010. 
Assanis, 73 

Available electronically from the National Academies Press Web site at 

http://www.nap.edu/catalog.php?record_id=12845 

President’s Council of Advisors on Science and Technology (PCAST) 

Working Group on Energy Technology Innovation System, “Report to the 

President on Accelerating the Pace of Change in Energy Technologies 

through an Integrated Federal Energy Policy,” November 2010.

Inventions and Patents 

 

Church, C., Smith, F., and D.N. Assanis, “Use of Singlet Delta Oxygen to 

Enhance the Performance of Internal Combustion Engines, Diesel Engines in 

Particular,” Patent No. 6,659,088, granted 12/9/2003.

Wu, B., Filipi, Z., Assanis, D.N., Kramer, D., Ohl, G., Prucka, M., and E. 

DiValentin, “Artificial Neural Networks for Estimating the Air Flow Rate 

through a VVT Engine”, Invention Development Record P706964 disclosed 

04/21/2004. Filed by a joint team of UM and DCX researchers.

Shih, A.J., Filipi, Z., and D.N. Assanis, “Pre-Turbocharging Catalyzed Porous 

Metal Foam Filter for Diesel Particulates Treatment”, Invention Disclosure No. 

2924 to UM Tech Transfer Office, July 2004. 

Najt, P.M., Eng, J.A., Chang, J., Filipi, Z.S., Guralp, O., and D.N. Assanis, 

“Method for Mid-Load Operation of Auto-Ignition Combustion,” Patent No. 

7,128,062 B2, granted 10/31/2006.

Kuo, T.W., Najt, P., Eng, J.A., Rask, R.B., Guralp, O., Hoffman, M., Filipi, Z.S., 

and D.N. Assanis, “Method and Apparatus to Determine Magnitude of 

Combustion Chamber Deposits,” Patent No. 7,367,319, granted 12/31/2007.

Najt, P., Kuo, T.W., Rask, R., Babajimopoulos, A., Filipi, Z.S.., Lavoie, G., and 

D. N. Assanis, “Hybrid Powertrain System Using Free Piston Linear Alternator 

Engines,” Utility patent application, US serial no. 12/504,502, filed July 16, 

2009.



S. Midlam-Mohler C.V. Page | 1 

CURRICULUM VITAE

SHAWN W. MIDLAM-MOHLER, PH.D. 

3938 Norbrook Dr. 

Columbus, Ohio 43220 

(614) 307-4176

midlam-mohler.1@osu.edu 

EDUCATION

Engineering Education 

Ph.D. Mechanical Engineering 6/2005

The Ohio State University Columbus, OH 

Dissertation Title: "Modeling, Control, and Diagnosis of a Diesel Lean NOx Trap Catalyst" 

M.S. Mechanical Engineering 3/2001

The Ohio State University Columbus, OH 

Thesis Title: "A Novel Fuel-Operated Heater for Automotive Thermal Management" 

B.S. Mechanical Engineering Summa cum Laude 6/1999

 Wright State University Dayton, OH 

Senior Design Project: “Aerodynamic Design and Simulation of a Wind-Turbine” 

Academic Fellowships 

Graduate Automotive Technology Education Program – Ph.D. Studies Source: Dept. of Energy 

 Awarded to select graduate students conducting research supporting DOE goals for transportation research

University Fellowship – M.S. Studies Source: Ohio State University 

 Awarded in a university-wide search to attract high-caliber graduate students 

RESEARCH 

EXPERIENCE

Research Appointments 

Research Scientist 10/2008 to present 

Ohio State University Center for Automotive Research, Columbus, OH 

 Conduct research in the area of clean and efficient transportation, including emissions reduction, Diesel 

engines, alternative combustion, hydrogen generation, heavy fuel atomization, and advanced powertrains 

 Directed and advised graduate students in this area of research 

Senior Research Associate 11/2005 to 9/2008

Ohio State University Center for Automotive Research, Columbus, OH 

 Conducted research in the area of clean and efficient transportation 

 Directed and advised graduate students in this area of research 

Research Associate II 2/2004 to 10/2005

Ohio State University Center for Automotive Research, Columbus, 

 Conducted research in the area of clean and efficient transportation 
S. Midlam-Mohler C.V. Page | 2 

Research Intern 6/2003 to 9/2003

Ford Scientific Research Labs, Dearborn, MI 

 Conducted research on emissions reductions for gasoline hybrid-electric vehicles 

 Three-month assignment resulted in three Ford invention disclosures and two U.S. patents 

Research Funding 

Projects as PI / Co-PI:

$1,800,000/3 years Title: Systems Level Development for Engine Thermal Management Start: 10/2010 

 Source: DOE via Chrysler subcontract Role: co-PI

$50,000/1 years Title: Analysis of Secondary Powertrain Systems in HEVs Start: 10/2010

 Source: CAR Industrial Consortium Role: PI 

 

$40,000/0.5 years Title: Life Cycle Analysis of Landfill Derived Natural Gas Start: 4/2009

 Source: FirmGreen Role: PI 

$144,500/3 year Title: Fleet Studies of Plug-In Electric Hybrid Vehicles Start: 1/2009 

Source: SMART@CAR Consortium Role: PI 

 

$2,000,000/3 years1 Title: EcoCAR Challenge Hybrid Electric Vehicle Project Start: 6/2008

 Source: US Department of Energy and numerous other sponsors Role: Co-PI 

$943,108/4 years Title: Coordinated Diesel Engine and Aftertreatment Control Start: 4/2008

 Source: Cummins Role: PI 

$724,531/3 years Title: Hierarchical Approach to Engine Modeling Start: 4/2007

 Source: General Motors Role: Co-PI

$234,760/2 years Title: Soot Filter Regeneration though External Heat Addition Start: 11/2005

 Source: Tenneco Automotive Role: Co-PI

$673,550/3 years Title: On-Board Fuel Reformation for Diesel Aftertreatment Start: 11/2005

 Source: Tenneco Automotive Role: Co-PI

Projects with Major Research Role (not co-PI):

$940,863/4 years Title: Next Generation Charge Estimation for IC Engines Start: 7/2004

 Source: General Motors Role: Researcher 

$1,327,954/5 years Title: Next Generation AFR Control for IC Engines Start: 7/2004

 Source: General Motors Role: Researcher 

 

1

 This is the estimated cost of the research conducted under this problem if funded from an external sponsor. This 

project is heavily leveraged by the Department of Energy, General Motors, Ohio State University, and a number of 

other sponsors through in-kind contributions as well as direct funding and fellowships. 
S. Midlam-Mohler C.V. Page | 3 

TEACHING

EXPERIENCE

Instructional Appointments 

Adjunct Assistant Professor 7/2009 to present 

Ohio State University Department of Mechanical Engineering, Columbus, OH 

 Granted in recognition of significant educational service to the Mechanical Engineering Department

 Service includes one-on-one student advising, student project advising, and supervision of undergraduate 

research

Instructor 4/2007 to present 

Ohio State University Department of Mechanical Engineering, Columbus, OH 

 Sole instructor of record for two applied thermal and fluids courses on internal combustion engines

Course Development 

ME 631 - Powertrain Laboratory (3 CR) 1/2009 

Ohio State University Department of Mechanical Engineering, Columbus, OH 

 Developed course material for two quarter hours of classroom lecture which reinforced lab work

 Developed eight new lab experiments based on in-depth knowledge of the automotive industry

 Facilitated donation of a gasoline engine from General Motors and a Diesel engine from Cummins, both 

with a calibration system to provide students access to cutting-edge equipment

ME 730 - Internal Combustion Engine Modeling (3 CR) 4/2007 

Ohio State University Department of Mechanical Engineering, Columbus, OH 

 Developed all new lecture material to bring in personnel research experience

 Developed new homework assignments to better engage students by building a fully functioning engine 

model in stages of greater fidelity and complexity

 Facilitated the donation of industry-standard engine simulation software for use by students 

 Developed capstone project which allowed students to become engaged in a topic of interest

Seminar - Alternative Fuels Short Course 1/2007 

Ohio State University Center for Automotive Research Distance Education Program 

 Developed 10 hours of lecture and lecture notes for industrial distance education program

 Provided case studies of alternative-fueled vehicles to reinforce concepts for the industry audience 

Teaching Experience 

ME 631 – Powertrain Laboratory (3 CR) Sole Instructor of Record 1/2011 

Overall Teaching Rating: 5.0/5.0 Class Size: 16 

ME 631 – Powertrain Laboratory (3 CR) Sole Instructor of Record 1/2010 

Overall Teaching Rating: 5.0/5.0 Class Size: 15 

ME 730 - Internal Combustion Engine Modeling (3 CR) Sole Instructor of Record 4/2009

Overall Teaching Rating: 4.4/5.0 Class Size: 7 

 

ME 631 – Powertrain Laboratory (3 CR) Sole Instructor of Record 1/2009 

Overall Teaching Rating: 4.8/5.0 Class Size: 12 

ME 730 - Internal Combustion Engine Modeling (3 CR) Sole Instructor of Record 4/2007

Overall Teaching Rating: 4.5/5.0 Class Size: 8 
S. Midlam-Mohler C.V. Page | 4 

Academic Advising 

Since 2005, Dr. Midlam-Mohler has become increasingly involved in student advising. He has served in an 

advisory or supervisory capacity to the following students at the M.S. and Ph.D. level: 

 

Degree Student Role

Graduation Date or

Expected Graduation Date

Ph.D. Quiming Gong Research Supervisor 2012

Vis. Scholar Bernhard Grimm Research Supervisor 2010

M.S. John Davis Co-Advisor 2011

Ph.D. Jason Meyer Research Supervisor 2011

Honors B.S. Katherine Bovee Acting Advisor 2010

Honors B.S. John Davis Acting Advisor 2010

Honors B.S. Ryan Everett Acting Advisor 2010

Ph.D. Kenny Follen Research Supervisor 2010

M.S. Beth Bezaire Acting Advisor 2010

M.S. Brad Cooley Acting Advisor 2010

M.S. Chris Hoops Acting Advisor 2010

M.S. Ming Fang Acting Advisor 2009

Honors B.S. Chris Hoops Acting Advisor 2009

M.S. Rajaram Maringanti Acting Advisor 2009

M.S. Joshua Supplee Acting Advisor 2009

Vis. Scholar Adalbert Wolany Supervisor 2009

Ph.D. Sai Rajagopalan Committee Member 2009

Ph.D. Sergio Hernandez Acting co-advisor 2008

Vis. Scholar Andrea Pezzini Supervisor 2008

Vis. Scholar Patrick Rebechi Supervisor 2008

Honors B.S. Rhisee Bhatt Acting co-advisor 2007

Vis. Scholar Simone Bernasconi Supervisor 2007

M.S. Josh Cowgill Acting co-advisor 2007

M.S. Kenny Follen Acting co-advisor 2007

M.S. Courtney Coburn Acting Advisor 2006

M.S. Adam Vosz Acting Advisor 2006

M.S. Eric Snyder Acting co-advisor 2005

Undergraduate Student Research Assistants: 

 

Dr. Midlam-Mohler has supervised the following students on research outside of a formal degree program: 

Degree Student Role Year

B.S. Abbey Underwood Supervisor 2010

B.S. Sarah Jadwin Supervisor 2010

B.S. Andrew Arnold Supervisor 2009-2010

B.S. John Macauley Supervisor 2009-10

B.S. Alixandra Keil Supervisor 2009-10

B.S. Jennifer Loy Supervisor 2009-10
S. Midlam-Mohler C.V. Page | 5 

B.S. Sean Ewing Supervisor 2009

B.S. David Griffin Supervisor 2009

B.S. Ross Wang Supervisor 2009

B.S. Orlando Inoa Supervisor 2008-09

B.S. Al Godfrey Supervisor 2008-09

B.S. John Lutz Supervisor 2008

B.S. Konrad Svzed Supervisor 2008

B.S. Joshua Supplee Supervisor 2007

Mentor for Local High School Students 

Dr. Midlam-Mohler has mentored seven local high school students for ~30 hours of activity per student since 2007. 

Student Organization Advising 

EcoCAR Challenge Hybrid Electric Vehicle Team 6/2008 - present

Ohio State University 

 Co-advise 40 member (~80% undergraduate) student design project team competing in U.S. Department 

of Energy sponsored vehicle competition

 Oversee day-to-day operation of team as they model, design, build, and test a hybrid electric SUV

 Team won 1st place in first year, 4th place in second year

 Nominated by team for “NSF Advisor of the Year Award”

Challenge-X Hybrid Electric Vehicle Team 8/2006 – 6/2008

Ohio State University 

 Co-advised primarily undergraduate team competing in Department of Energy Sponsored advanced 

technology vehicle completion

 Over the course of the four year competition from 2004 – 2008, OSU placed 3rd, 4th, 4th, and 3rd

respectively in the premier advanced technology vehicle competition

PROFESSIONAL

SERVICE

Professional Service

EPA GEM Model Reviewer, Columbus, OH

Peer Reviewer 12/2011

 Conducted peer review of a heavy-duty truck model developed by the U.S. EPA used for predicting fuel 

economy and green house gas emissions.

Clean Fuels Ohio, Columbus, OH 9/2009 to present

Member of the Board of Directors

 Elected to Board of Directors of Clean Fuels Ohio, a non-profit committed to cleaner transportation fuels

State of Indiana 4/2009

Proposal Reviewer

 Reviewed multi-million dollar proposal for Indiana grant program in area of internal combustion engines

Natural Gas Fleet Stakeholders Meeting, Grove City, OH 11/2008

Panel Member 

 Served as panel technical expert on alternative vehicular fuels

 Meeting attended by designees’ from the Governor’s office and from both of Ohio’s U.S. Senators’ staff
S. Midlam-Mohler C.V. Page | 6 

McMaster Fuel Ltd., Perrysburg, OH 9/2006 to 1/2007

Independent Consultant 

 Provided analysis of a hydrogen production technique against other methods of hydrogen production 

 Provided analysis of these techniques for emissions reduction 

 Assisted McMaster Fuel Ltd. in making strategic decisions regarding their technology 

Publication Reviewer Continuous

 Review numerous publications for conferences and journal submission of ASME, SAE, IEEE, etc.

 

PUBLICATIONS

Scholarly Publications 

Journal Articles:

1. Gong, Q. (supervised by SMM); Midlam-Mohler, S.; Marano, V. ; Rizzoni, G. ; “Statistical Analysis for PHEV 

Virtual Fleet Study”, International Journal of Vehicle Design (IJVD). Accepted but undergoing revisions.

2. Meyer, J. (supervised by SMM); Midlam-Mohler, S.; Yurkoich, S. (colleague); “In-cylinder Oxygen 

Concentration Estimation for Diesel Engines Via Transport Delay”, American Control Conference 2011; 

Accepted but undergoing revisions.

3. M. Canova, S. Midlam-Mohler, P. Pisu, A. Soliman, “Model-Based Fault Detection and Isolation for a Diesel 

Lean NOx Trap Aftertreatment System,” Control Engineering Practice, November 2009.

4. M. Canova, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, "Mean Value Modeling and Analysis of HCCI 

Diesel Engines with External Mixture Formation,” ASME Journal of Dynamic Systems, Measurement and 

Control, Vol. 131, No. 11, 2009. 

5. M. Canova, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, “Theoretical and Experimental Investigation on 

Diesel HCCI Combustion with External Mixture Preparation,” International Journal of Vehicle Dynamics, 

Volume 44, Nos 1-2, 2007. 

6. N. Szabo, C. Lee, J. Trimboli1, O. Figueroa, R. Ramamoorthy, S. Midlam-Mohler, A. Soliman, H. Verweij, P. 

Dutta and S. Akbar, “Ceramic-Based Chemical Sensors, Probes and Field-Tests in Automobile Engines,”

Journal of Materials Science, November, 2003. 

Conference Papers:

1. Gong, Q.; Tulpule, P.,Midlam-Mohler, S.; Marano, V.; Rizzoni, G.; “The Role of ITS in PHEV Performance 

Improvement”, American Control Conference (ACC) 2011. Accepted but undergoing revisions.

2. Gong, Q. ; Midlam-Mohler, S.; Marano, V.; Rizzoni, G.; “An Iterative Markov Chain Approach for Generating 

Vehicle Drive Cycles”, Accepted by SAE World Congress 2011. Out for final review.

3. Cooley, B; Vezza, D.; Midlam-Mohler, S.; Rizzoni, G.; “Model Based Engine Control Development and 

Hardware-in-the-Loop Testing for the EcoCAR Advanced Vehicle Competition”, Accepted by SAE World 

Congress 2011. Out for final review.

4. K. Follen, M. Canova, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, B. Lee, G. Matthews, "A High Fidelity 

Lumped-Parameter Engine Model for Powertrain Control Design and Validation." In: ASME Dynamic Systems 

and Control Conference. Cambridge, MA, United States. 

5. Qi. Gong, S. Midlam-Mohler, V. Marano, G. Rizzoni, Y. Guezennec, “Statistical analysis based PHEV fleet 

data study”, 2010 IEEE Vehicle Power and Propulsion Conference, September, 2010. 

6. Kerem Bayar, Beth Bezaire, Brad Cooley, John Kruckenberg, Eric Schact, Shawn Midlam-Mohler, Giorgio 

Rizzoni, “Design of an Extended-Range Electric Vehicle for the EcoCAR Challenge”, ASME 2010 

International Design Engineering Technical Conference, August, 2010. 

7. J. Meyer, S. Yurkovich, S. Midlam-Mohler, “Architectures for Phase Variation Compensation in AFR Control,” 

2010 American Controls Conference, June, 2010. 
S. Midlam-Mohler C.V. Page | 7 

8. R. Maringanti, S. Midlam-Mohler, M. Fang, F. Chiara, M. Canova, “Set-Point Generation using Kernel-Based 

Methods for Closed-Loop Combustion Control of a CIDI Engine,” ASME DSCC2009, September, 2009. 

9. J. Meyer, S. Rajagopalan, S. Midlam-Mohler, Y. Guezennec, S. Yurkovich, “Application of an Exhaust 

Geometry Based Delay Prediction Modal to an Internal Combustion Engine,” ASME DSCC2009, September, 

2009.

10. M. Fang, S. Midlam-Mohler, R. Maringanti, F. Chiara, M. Canova, “Optimal Performance of Cylinder-byCylinder and Fuel Bank Controllers for a CIDI Engine,” ASME DSCC2009, September, 2009. 

11. S. Midlam-Mohler, E. Marano, S. Ewing, D. Ortiz, G. Rizzoni, “PHEV Fleet Data Collection and Analysis,”

IEEE VPPC09, September 2009. 

12. L. Headings, G. Washington, S. Midlam-Mohler, J. Heremans, “Thermoelectric Power Generation for HybridElectric Vehicle Auxiliary Power,” Proc. SPIE Int. Conference on Smart Structures and Materials, 2009, Vol. 

7290, No. 13. 

13. M. Canova, S. Midlam-Mohler, G. Rizzoni, F. Steimle, D. Boland, M. Bargende, “A Simulation Study of an 

E85 Engine APU for a Series Hybrid Electric Vehicle,” 9th Stuttgart International Symposium on Automotive 

and Engine Technology, Stuttgart, Germany, 2009. 

14. S. Rajagopalan, S. Midlam-Mohler, S. Yurkovich, Y. Guezennec, K. Dudek, “Control Oriented Modeling of a 

Three Way Catalyst Coupled with Oxygen Sensors,” ASME Dynamic System and Controls Conference, Ann 

Arbor, MI, 2008. 

15. L. Headings, S. Midlam-Mohler, G. Washington, and J. P. Heremans, “High Temperature Thermoelectric 

Auxiliary Power Unit for Automotive Applications,” ASME Conference on Smart Materials, Adaptive 

Structures and Intelligent Systems, 2008, Paper #610. 

16. K. Koprubasi, A. Pezzini, B. Bezaire, R. Cooley, P. Tulpule, G. Rizzoni, Y. Guezennec, S. Midlam-Mohler, 

“Application of Model-Based Design Techniques for the Control Development and Optimization of a HybridElectric Vehicle”, SAE World Congress 2009, Detroit, MI. 

17. K. Sevel, M. Arnett, K. Koprubasi, C. Coburn, M. Shakiba-Heref, K. Bayar, G. Rizzoni, Y. Guezennec, S. 

Midlam-Mohler, “Cleaner Diesel Using Model-Based Design and Advanced Aftertreatment,” SAE 2008-01-

0868, 2008 International Congress, Detroit, MI, April 2008. 

18. K. Dudek, B. Montello, J. Meyer, S. Midlam-Mohler, Y. Guezennec, and S. Yurkovich, “Rapid Engine 

Calibration for Volumetric Efficiency and Residuals by Virtual Engine Mapping,” International Congress on 

Virtual Power Train Creation 2007, Munich, Germany, October 24-25, 2007. 

19. M. Canova, S. Midlam-Mohler, Y. Guezennec, A. Soliman, and G. Rizzoni, “Control-Oriented Modeling of 

NOx Aftertreatment Systems,” SAE ICE’07 Conference, Capri, Italy, September 2007. 

20. M. Canova, F. Chiara, J. Cowgill, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, “Experimental 

Characterization of Mixed-Mode HCCI/DI Combustion on a Common Rail Diesel Engine,” 8th International 

Conference on Engines for Automobile (ICE2007), Capri, Italy. 

21. M. Canova, F. Chiara, M. Flory, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, “Experimental Characterization 

of Mixed Mode HCCI/DI Combustion on a Common Rail Diesel Engine,” submitted to SAE ICE’07 

Conference, Capri, Italy, September 2007. 

22. M. Canova, M. Flory, Y. Guezennec, S. Midlam-Mohler, G. Rizzoni, and F. Chiara, “Dynamics and Control of 

DI and HCCI Combustion in a multi-cylinder Diesel engine,” Paper 44, submitted to 5th IFAC Symposium on 

Advances in Automotive Control, Pajaro Dunes/Seascape, CA, August 2007. 

23. A. Vosz, S. Midlam-Mohler, and Y. Guezennec, “Experimental Investigation of Switching Oxygen Sensor 

Behavior Due to Exhaust Gas Effects,” Proc. of IMECE ’06, Paper IMECE 2006-14915, Chicago, IL, 

November 2006. 

24. S. Midlam-Mohler and Y. Guezennec, “A Temperature-Based Technique for Temporally and Spatially 

Resolved Lean NOx Trap Catalyst NOx Measurements,” Proc. of IMECE ’06, Paper IMECE 2006-14887, 

Chicago, IL, November 2006.

25. M. Canova, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, L. Garzarella, M. Ghisolfi, and F. Chiara, 

“Experimental Validation for Control-Oriented Modeling of Multi-Cylinder HCCI Diesel Engines,” Proc. of 

IMECE ’06, Paper IMECE 2006-14110, Chicago, IL, November 2006. 

26. A. Soliman, S. Midlam-Mohler, Z. Zou, Y. Guezennec, and G. Rizzoni, “Modeling and Diagnostics of NOx 

Aftertreatment Systems,” Proc. FISITA ’06, Yokohama, Japan, October 2006. 

27. Z. Zou, S. Midlam-Mohler, R. Annamalai, Y. Guezennec, V. Subramaniam, "Literature Survey of On-Board 

Hydrogen Generation Methods for Diesel Powertrains,” Global Powertrain Conference, Novi, MI, Not Peer 

Reviewed, September 2006. 
S. Midlam-Mohler C.V. Page | 8 

28. K. Follen, S. Midlam-Mohler, Y. Guezennec, “Diesel Particulate Filter Regeneration with an External Burner,”

Global Powertrain Conference, Novi, MI, Not Peer Reviewed, September 2006. 

29. S. Midlam-Mohler and Y. Guezennec, “Regeneration Control for a Bypass-Regeneration Lean NOx Trap 

System,” American Control Conference ’06, Minneapolis, MN, Invited paper, June 2006. 

30. A. Soliman, I. Choi, S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, “Modeling and Diagnostics Of NOx AfterTreatment Systems,” SAE Paper 2006-05-0208, 2006 International Congress, Detroit, MI, April 2006. 

31. S. Midlam-Mohler and Y. Guezennec, “Design, Modeling and Validation of a Flame Reformer for LNT 

External By-Pass Regeneration,” SAE Paper 2006-01-1367, 2006 SAE International Congress, Detroit, MI, 

April 2006. 

32. S. Midlam-Mohler, and Y. Guezennec, “Modeling of a Partial Flow Diesel, Lean NOx Trap System,” Proc. of 

IMECE ’05, Paper IMECE 2005-80834, Orlando, FL, November 2005. 

33. M. Canova, L. Garzarella, M. Ghisolfi, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “A Control-Oriented 

Mean-Value Model of HCCI Diesel Engines with External Mixture Formation,” Proc. of IMECE ’05, Paper 

IMECE 2005-79571, Orlando, FL, November 2005. 

34. A. Soliman, P. Jackson, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “Diagnosis of a NOx 

Aftertreatment System,” ICE 2005 7th International Conference on Engines for Automobiles, Capri, Italy, 

September 2005. 

35. M. Canova, L. Garzarella, M. Ghisolfi, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “A Mean-Value 

Model of a Turbo-Charged HCCI Diesel Engine with External Mixture Formation,” ICE 2005 7th International 

Conference on Engines for Automobiles, Capri, Italy, September 2005. 

36. M. Canova, R. Garcin, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “A Control-Oriented Model of 

Combustion Process in HCCI Diesel Engines,” American Control Conference ’05, Portland, OR, June 2005. 

37. C. Musardo, B. Staccia, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “Supervisory Control for NOX 

Reduction of an HEV with a Mixed-Mode HCCI/CIDI Engine,” American Control Conference ’05, Portland, 

OR, June 2005. 

38. M. Canova, A. Vosz, D. Dumbauld, R. Garcin, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “Model and 

Experiments of Diesel Fuel HCCI Combustion with External Mixture Formation,” 6th Stuttgart International 

Symposium on Motor Vehicles and Combustion Engines, Stuttgart, Germany, Not peer reviewed, February 

2005.

39. S. Midlam-Mohler, S. Haas, Y. Guezennec, M. Bargende, G. Rizzoni, S. Haas, and H. Berner, “Mixed-Mode 

Diesel HCCI/DI with External Mixture Preparation,” Paper F2004V258, Proc. FISITA ’04 World Congress, 

Barcelona, Spain, May 2004. 

40. Y. Guezennec, C. Musardo, B. Staccia, S. Midlam-Mohler, E. Calo, P. Pisu, and G. Rizzoni, “Supervisory 

Control for NOx Reduction of an HEV with a Mixed-Mode HCCI/DI Engine,” Paper F2004F233, Proc. FISITA 

’04 World Congress, Barcelona, Spain, May 2004. 

41. M. Gilstrap, G. Anceau, C. Hubert, M. Keener, S. Midlam-Mohler, K. Stockmeier, J-M Vespasien, Y. 

Guezennec, F. Ohlemacher, and G. Rizzoni, “The 2002 Ohio State University FutureTruck – the 

BuckHybrid002,” 2003 SAE International Congress and Exposition, Detroit, MI, March 2003. 

42. Y. Guezennec, S. Midlam-Mohler, M. Tateno, and M, Hopka, “A 2-Stage Approach to Diesel Emission 

Management in Diesel Hybrid Electric Vehicles,” Proc. 2002 IFAC Meeting, Barcelona, Spain, July 2002. 

43. M. Hopka, A. Brahma, Q. Ma, S. Midlam-Mohler, G. Paganelli, Y. Guezennec, and G. Rizzoni, “Design, 

Development and Performance of Buckeyebrid: The Ohio State Hybrid Electric FutureTruck 2001,” SAE SP1701, Not peer reviewed, March 2002. 

Scholarly Presentations Independent of Paper Publications:

1. S. Midlam-Mohler and Y. Guezennec, “Lean NOx Trap Modeling Based on Novel Measurement Techniques,”

CLEERS Conference Workshop 3, Not peer reviewed, May 4, 2006. 

2. S. Midlam-Mohler, and Y. Guezennec, “Design, Modeling and Validation of a Flame Reformer for LNT 

External By-Pass Regeneration,” 2005 DEER Conference, Chicago, IL, Not peer reviewed, August 2005. 

3. M. Canova, S. Midlam-Mohler, Y. Guezennec, and G. Rizzoni, “Control-Oriented Modeling of HCCI 

Combustion,” 2005 DEER Conference, Chicago, IL, Not peer reviewed, August 2005. 

4. S. Midlam-Mohler and Y. Guezennec, 2004 DEER Conference, San Diego, CA, Not peer reviewed, August 

2004.

5. S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, M. Bargende, and S. Haas, “Mixed-Mode Diesel HCCI with 

External Mixture Preparation,” 2003 DEER Conference, Newport, R. I., Not peer reviewed, August 2003. 
S. Midlam-Mohler C.V. Page | 9 

6. S. Midlam-Mohler, Y. Guezennec, “An Active, Thermo-Chemically Managed Diesel NOx After-Treatment 

System,” CLEERS Conference Workshop 2, Not peer reviewed, October 11, 2001. 

Intellectual Property Activity 

Issued Patents:

1. S. Midlam-Mohler, B. Masterson, "System System for Controlling NOx Emissions During Restarts of Hybrid 

and Conventional Vehicles,” U.S. Patent 7,257,493, awarded 3/21/07. 

2. S. Midlam-Mohler, "System and Method for Reducing NOx Emissions after Fuel Cut-Off Events,” U.S. Patent 

7,051,514, awarded 5/30/06. 

Patent Applications:

1. S. Liu, K. Dudek, S. Rajagopalan, S. Yurkovich, Y. Hu, Y. Guezennec, S. Midlam-Mohler, “Off-Line 

Calibration of Universal Tracking Air Fuel Ratio Regulators,” U.S. Patent Application 20090271093, 

10/29/2009. 

2. S. Rajagopalan, K. Dudek, S. Liu, S. Yurkovich, S. Midlam-Mohler, Y. Guezennec, Y. Hu, “Universal 

Tracking Air-Fuel Regulator for Internal Combustion Engines, U.S. Patent Application 20090266052, 

10/29/2009. 

3. K. Dudek, S. Rajagopalan, S. Yurkovich, Y. Guezennec, S. Midlam-Mohler, L. Avallone, I. Anilovich, “Air 

Fuel Ratio Control System for Internal Combustion Engines,” U.S. Patent Application 20090048766, 

2/19/2009. 

4. Y. Guezennec and S. Midlam-Mohler, Shawn, “Fuel Preparation System for Combustion Engines, Fuel 

Reformers and Engine Aftertreatment,” U. S. Patent Application 20040124259, 7/1/04 

5. S. Midlam-Mohler and B. Masterson, "System and Methods for the Reduction of NOx Emissions after Fuel 

Cut-Off Events,” U.S. Patent application 20060021326, filed 2/2/03. 

6. S. Midlam-Mohler and B. Masterson, "Strategy for Controlling NOx Emissions During Hot Restarts for Hybrid 

and Conventional Vehicles,” U.S. Patent Application 20060021330, filed 2/2/03. 

Patent Applications in Preparation:

1. J. Meyer, S. Midlam-Mohler, K. Dudek, S. Yurkovich, Y. Guezennec, Topic: Engine emissions control, Status: 

submitted to patent office 9/09. 

2. J. Meyer, S. Midlam-Mohler, K. Dudek, S. Yurkovich, Y. Guezennec, Topic: Engine emissions control, Status: 

submitted to patent office 9/09. 

3. S. Midlam-Mohler, S. Rajagopalan, K. Dudek, S. Yurkovich, Y. Guezennec, Topic: Catalyst modeling for 

improved emissions control, Status: Patent application being prepared by outside counsel. 
ROBERT F. SAWYER BS, MS, MA, PhD, PE, NAE

Dr. Sawyer studied at Stanford University in the Department of Mechanical Engineering (B.S. 1957, 

M.S. 1958). He served as a Rocket Test Engineer, Rocket Propulsion Research Engineer, and Chief of the 

Liquid Systems Analysis Section at the Air Force Rocket Propulsion Laboratory, Edwards AFB, California 

(1958-1961). His later graduate and doctoral degree work was at the Guggenheim Aerospace Propulsion 

Laboratories of the Department of Aerospace Sciences at Princeton University (M.A. 1963, Ph.D. 1966). 

 He joined the faculty of the Mechanical Engineering Department of the University of California at 

Berkeley as an assistant professor in 1966 and served through the rank of full professor (1991). He held a joint 

appointment as a Senior Faculty Scientist at the Lawrence Berkeley Laboratory. At Berkeley he was Vice 

Chairperson for Graduate Studies of the Department of Mechanical Engineering (1980-1983) and Chairperson 

of the Energy and Resources Group (1984-1988), an interdisciplinary graduate department treating energy, 

resource, and environmental policy. He was selected the first Class of 1935 Professor of Energy (1988). 

Visiting appointments included: Visiting Research Scientist at the Johns Hopkins University Applied Physics 

Laboratory (1971), Visiting Researcher at Imperial College (1978-1979), Visiting Professor at Hokkaido 

University (1984), Visiting Professor at the Toyohashi University of Technology (1984), Visiting Scientist at 

the Sandia National Laboratory Combustion Research Facility (1988-1989), and Honorary Research Fellow at 

University College London (1991). 

 Dr. Sawyer served on the President's Council on Environmental Quality Advisory Committee on 

Alternative Automotive Power Systems (1971-1976), headed the Technology Panel of the National Academy 

of Sciences Committee on Motor Vehicle Emissions (1973-1974), chaired the State of California ad hoc 

Committee on Atmospheric Carcinogens (1978-1979), chaired the National Academy of Sciences Committee 

on Diesel Engine Technology (1979-1982), served as a member of the National Research Council Committee 

on Army Basic Research (1987-1988), a member of the California Air Resources Board (1975-1976), a 

director of KVB, Inc. (1975-1978), a director of the Center for Emissions Research and Analysis (1991-1994), 

a member of the External Advisory Panel to the World Bank Mexico City Transport Air Quality Management 

Program (1992-1996), a Senior Policy Advisor to the Office of Air and Radiation of the U.S. Environmental 

Protection Agency (1994-1995), a member of the Distinguished Advisory Panel to the Joint Auto/Oil Air 

Quality Improvement Research Program (1988-1996), a member of the U.S. EPA Blue Ribbon Panel on 

MTBE, and a member of the National Research Council Committee to Review the MOBILE Model, the 

Committee on Congestion Mitigation and Air Quality (CMAQ), and the Committee on Light Duty Vehicle 

Fuel Economy. He chaired the Health Effects Institute Special Committee on Emerging Technologies. He 

chaired the Bay Area Air Quality Management District Advisory Council (2003) and was co-chair of the 

USEPA Mobile Sources Technical Advisory Sub-committee (1996-2003). 

 In 2005 Dr. Sawyer accepted the appointment by Governor Schwarzenegger to chair the California 

Air Resources Board, a position he held until 2007. This agency with 1200 employees and a budget of more 

than 750 million dollars oversees California’s air quality and global warming programs. He was a member of 

the United Nations International Civil Aviation Organization Independent Experts Panel on Fuel Burn 

Reduction Technology (2009-2010). He is a member of the Advisory Committee to the College of Engineering 

Center for Environmental Research and Technology at the University of California at Riverside and of the 

Board of Advisors of the Institute of Transportation Studies at the University of California at Davis, and the 

International Advisory Board of the Center for Combustion Energy, Tsinghua University. He serves on the 

National Research Council Board on Environmental Science and Toxicology, the National Academy of 

Engineering/National Research Council Committee on Analysis of Causes of the Deepwater Horizon 

Explosion, Fire, and Oil Spill to Identify Measures to Prevent Similar Accidents in the Future, and the National 

Research Council Committee on Transition to Alternative Vehicles and Fuels. He serves on the USEPA 

Mobile Sources Technical Review Sub-committee and is a member of the International Council for Clean 

Transportation. He serves on the board of directors of the American Lung Association in California. 
 Dr. Sawyer served as President of the International Combustion Institute (1992-1996), is a Fellow of 

the Society of Automotive Engineers, Associate Fellow of the American Institute of Aeronautics and 

Astronautics, and a member of American Society of Mechanical Engineers and the American Association of 

University Professors. He is a Registered Professional Engineer (Mechanical Engineering and Fire Protection 

Engineering) in the State of California. He is a recipient of the Berkeley Citation and the Sechiro Honda Medal 

of the Society of Mechanical Engineers. He is listed in Who’s Who in America, American Men and Women of 

Science, Who’s Who in Technology, Who's Who in Engineering, Who’s Who in Science and Engineering, and 

Who's Who in the West. Dr. Sawyer is a member of the National Academy of Engineering. He is a partner of 

Sawyer Associates, an engineering consulting business. 

 At Antelope Valley College (Lancaster, California) Dr. Sawyer was a part-time instructor of physics 

and mathematics (1959-1961). At the University of California at Berkeley, he taught undergraduate and 

graduate courses in combustion, propulsion, thermodynamics, energy conversion, engines, air pollution, and 

fire safety (1966-1991). As Professor of the Graduate School, the Class of 1935 Professor of Energy Emeritus, 

and Senior Research Engineer at the Lawrence Berkeley Laboratory (1991-2005) he conducted research and 

advised undergraduate and graduate research students in motor vehicle emissions and control, toxic waste 

incineration, and regulatory policy. He continued some teaching at Berkeley during this period including the 

undergraduate courses, “Energy and Society” and “The Automobile, Energy, and The Environment” and the 

graduate courses, “Interdisciplinary Energy Analysis” and “Critical Issues in Air Pollution for the 1990s.” He 

is a Visiting Professor of Energy and Environment at University College London (1995-). He directed the 

University of California Study Abroad Center in London, England (2003-2005). Following his service in the 

California state government, he resumed his work at Berkeley where teaches the freshman seminar “The 

Science, Technology, Policy, and Politics of California Air Pollution.” He is the author or co-author of more 

than 350 publications and the co-author of two books, The Chemistry of Propellants and Combustion Sources 

of Air Pollution and Their Control. 

Dr. Sawyer was born in Santa Barbara, California in 1935. He served in the U.S. Air Force (active duty, 

1958-1961), reaching the rank of captain (USAFRes). He lives in Oakland, California with his wife, Barbara 

Sawyer, who is a faculty member and past Chair of the Academic Senate at Diablo Valley College. Their 

daughters, Allison Shaffer, a finance analyst, and Lisa Sawyer, an architect, live in Davis, California. 

University of California 

Department of Mechanical Engineering 

61 Hesse Hall 

Berkeley CA 94720-1740 USA 

Cell phone: 510-305-6602 

fax: 510-642-1850 

lab administrator: 510-642-0215 

email: sawyer@berkeley.edu

6337 Valley View Road (home) 

Oakland CA 94611-1226 

phone: 510-339-9857 

Sawyer Associates 

PO Box 6256 

Incline Village, NV 89450-6256 

email: rsawyer@sawyerassociates.us

February 2011 
WRW Curriculum Vitae 031211.wrw 1 3/12/2011

Wallace R. Wade, P.E.

50786 Drakes Bay Dr.

Novi, MI 48374

Phone: 248-449-4549 

Email: wrwade1@gmail.com

1. Academic Background

MSME University of Michigan, Ann Arbor Mechanical Engineering

BME Rensselaer Polytechnic Institute Mechanical Engineering

2. Professional Licenses/Certification

Registered Professional Engineer, State of Michigan

3. Relevant Professional Experience

Areas of Expertise:

- Engine research and development

- Emission control systems

- Powertrain electronic control systems

- Powertrain calibration

- Systems engineering

1994 – 2004 Chief Engineer and Technical Fellow 

(Retired Oct 2004) Powertrain Systems Technology and Processes

(32+ years service) Ford Motor Company, Dearborn, MI

Responsible for development, application and certification of emission and powertrain 

control system technologies for all Ford Motor Company’s North American vehicles.

- Developed technologies for emission control systems, powertrain control 

systems, OBD II (On-Board Diagnostic) systems and powertrain calibration 

procedures. Achieved U.S. EPA (Environmental Protection Agency) and CARB 

(California Air Resources Board) certifications for all 1993-2005 model year North 

American vehicles.

- Developed and implemented, in production, new technology catalyst systems for 

increasingly stringent emission standards with significant reductions in precious 

metal usage. 

- Developed technologies for California LEV II (Low Emission Vehicle – 2nd

Generation) and EPA SFTP (Supplemental Federal Test Procedure) regulations.

- Developed key low emission technologies for the engine, powertrain control 

system, exhaust emission and vapor emission control systems in the 2003 

California SULEV (Super Ultra Low Emission Vehicle) Ford Focus, which was the 

first domestic production vehicle complying with the most stringent emission 

levels required by the California Air Resources Board.
WRW Curriculum Vitae 031211.wrw 2 3/12/2011

- Developed the first analytical and laboratory based (engine and vehicle) 

automated powertrain calibration process with objective measures of driveability 

to replace the traditional on-the-road calibration process resulting in significant 

reductions in test vehicles and significant improvements in efficiency.

- Initiated production implementation of the first domestic application of a diesel 

particulate filter (DPF) with active regeneration.

Co-Chairman of the Ford Corporate Technical Specialist Committee which provided 

corporate overview in promoting deep technical expertise through the selection and 

appointment of technical specialists.

1992-1994 Assistant Chief Engineer

 Powertrain Systems Engineering

 Ford Motor Company, Dearborn, MI

Responsible for the development and certification of emission and powertrain control 

systems for all Ford Motor Company’s North American vehicles.

- Developed and implemented, in production, the California LEV (Low Emission 

Vehicle) requirements featuring palladium-only catalysts and coordinated 

strategy for starting with reduced emissions (CSSRE). 

- Developed and implemented OBD II, which was phased-in on all North American 

vehicles over the 1994-1996 model years.

- Developed and phased in the advanced EEC V electronic engine control system 

on all production vehicles over the 1994-1996 model years. 

- Led the development and implementation of enhanced evaporative emission and 

running loss controls that were phased-in over the 1995-1999 model years.

- Led the establishment of systems engineering in the development of powertrain 

systems. Design specifications were developed for all powertrain sub-systems. 

1990-1992 Executive Engineer/Manager

 Powertrain Electronics (Containing 4 Departments)

 Ford Motor Company, Dearborn, MI

Responsible for the development and production implementation of powertrain 

electronic control systems (hardware and software) for all of Ford Motor Company’s 

North American vehicles.

- Developed production powertrain electronic control systems for all North 

American vehicles. 

- Developed the technology for OBD II and the advanced EEC V electronic engine 

control system. 

- Led the Powertrain Electronics Control Cooperation (PECC) program resulting in 

the application of Ford EEC V systems on 30% of Mazda vehicle lines by the 

2000 model year.

- Initiated the development of Ford’s next generation 32-bit powertrain electronic 

control system (PTEC) (implemented in the 1999 model year).
WRW Curriculum Vitae 031211.wrw 3 3/12/2011

1987-1990 Manager

 Advanced Powertrain Control Systems Department

 Ford Motor Company, Dearborn, MI

Responsible for the development of powertrain control system technology for future 

applications. 

- Developed the first Ford California ULEV (Ultra Low Emission Vehicle) emission 

control system. Major improvements in air/fuel ratio control were achieved using 

a UEGO (universal exhaust gas oxygen) sensor and a proportional control 

algorithm.

- Developed enhanced evaporative and running loss emission control concepts. 

- Developed the first Ford traction control system using engine torque modulation 

combined with brake modulation.

- Developed the first Ford electronic throttle control (drive-by-wire) system for 

improved driveability (implemented in production for the 2003 model year). 

- Developed engine torque modulation during shifting for imperceptible automatic 

transmission shifts.

- Initiated the requirements specification for a new 32-bit powertrain electronic 

control system (PTEC). 

1978-1987 Manager

 Engine Research Department

 Research Staff

 Ford Motor Company, Dearborn, MI

Responsible for the creation, identification and feasibility prove-out of advanced engine 

concepts for next generation vehicle applications.

- Developed the first Ford passenger car, direct-injection diesel that met current 

emission requirements and provided 10-15% fuel economy improvement vs. 

indirect injection diesel.

- Developed light-duty diesel electronic control systems that achieved significant 

reductions in emissions.

- Developed the first Ford adiabatic diesel engine with a ringless ceramic piston 

operating in a ceramic cylinder.

- Developed the concept and demonstrated the first Ford diesel particulate filter 

(DPF) with active regeneration that provided over 90% reduction in particulate 

emissions (scheduled for production in a Ford vehicle in 2007).

1974-1978 Supervisor, Development Section

 Diesel Engine and Stratified Charge Engine Department

 Ford Motor Company

Responsible for the research and development of low emission, fuel-efficient stratified 

charge engines (PROCO stratified charge, 3 valve CVCC (Compound Vortex Controlled 

Combustion), spark ignited-direct injection) and diesel engines. 
WRW Curriculum Vitae 031211.wrw 4 3/12/2011

1972-1974 Supervisor/Senior Research Engineer

 Turbine Controls and Combustion Section

 Ford Motor Company

Responsible for the research and development of low emission combustion systems for 

a high temperature, ceramic gas turbine engine.

- Developed the first successful premixed, pre-vaporized, variable geometry gas 

turbine combustion system that met the most stringent emission standards in the 

1970’s.

1967-1972 Research Engineer

 General Motors Research Laboratory, Warren, MI

Responsible for the research and development of low emission combustion systems for 

gas turbine, Stirling and steam engines for potential automotive applications.

4. Consulting

2007-2008 Expert Witness for Orrick, Herrington and Sutcliffe, LLP

Expert witness for the plaintiff in a trade secret case involving diesel emission control 

systems (represented by Orrick, Herrington and Sutcliffe, LLP). Case was successfully 

settled after expert testimony. (May 2007 – December 2008) 

2009 U.S. Environmental Protection Agency/ICF Consulting Group, Inc.

 

Evaluated the U.S. EPA’s methodology for analyzing the manufacturing costs of vehicle 

powertrain and propulsion system technologies with low greenhouse gas emissions.

2009-Present Technical Advisory Board, Achates Power, Inc.

Technical advisor to Achates Power, Inc. for the development of unique technologies for 

new, fuel efficient, high power density engines.

2010 Expert Witness for Scott L. Baker, A Professional Law Corp.

Expert witness for the plaintiff in a case involving retrofit emission control systems 

(represented by Scott L. Baker). Case was successfully settled after expert testimony. 

(October – November 2010) 

5. Associated Experience

1965-1966 1st and 2nd Lieutenant

 U.S. Army

- 1965 Frankford Arsenal – Responsible for developing improvements in the save 

capability of high-speed aircraft emergency ejection seats using propellant 

actuated devices.

- 1966 Cam Ranh Bay, Vietnam – Assistant Adjutant, U.S. Army Depot
WRW Curriculum Vitae 031211.wrw 5 3/12/2011

1967-1991 Lt. Col. and prior ranks

 U.S. Army Reserve

Annual Training (Mobilization Designation Training)– Deputy Chief of Staff for 

Research, Development and Acquisition (DCSRDA), Department of the Army, 

Washington, DC

- Responsible for technical analysis of critical powerplant programs for the Army’s 

mobility equipment

6. Professional Affiliations

Society of Automotive Engineering (SAE) – Fellow Member

American Society of Mechanical Engineers (ASME)– Fellow Member

Engineering Society of Detroit (ESD) – Member

7. Patents

Issued 29 U.S. patents and numerous foreign patents in the following areas:

- Low emission combustion systems

- Diesel particulate filters

- Adiabatic engine design

- Engine control systems

- OBD II monitor systems

- Traction control

8. Publications

Published 25 technical papers on powertrain research and development in SAE, 

IMechE, FISITA, ASME, API, NPRA (National Petroleum Refiners Association) and 

CRC.

9. Significant Awards

- Elected a member of the National Academy of Engineering (NAE), which is 

among the highest professional distinctions accorded to an engineer – For 

outstanding contributions in the implementation of low-emission technologies in 

the automotive industry (2011).

- Recognized as an innovator in the automotive industry by being appointed as 

one of the first Henry Ford Technical Fellows (1994) (technical ladder position 

equivalent to Engineering Director in Ford Motor Company).

- ASME Soichiro Honda Medal for technical achievements and leadership in every 

phase of automotive engineering, including 26 patents related to both gasoline 

and diesel engines (2007).
WRW Curriculum Vitae 031211.wrw 6 3/12/2011

- SAE Edward N. Cole Award for Automotive Engineering Innovation – For 

outstanding creativity and achievement in the field of automotive engineering 

(2006).

- Honored by being invited to present the 2003 Soichiro Honda Lecture at the 

ASME Internal Combustion Engine Division Meeting (September, 2003). The 

lecture provided a comprehensive description of the technology incorporated in 

the first domestic SULEV vehicle.

- Honored by the Inventors Hall of Fame as a Distinguished Corporate Inventor 

(1997).

- Elected by ASME to Fellow Member Grade in recognition of outstanding 

accomplishments in engine combustion, efficiency and emissions research and 

development (2010).

- Elected by SAE to Fellow Member Grade in recognition of major technical 

contributions in the area of diesel engine research (1985).

- Honored with 5 SAE Arch T. Colwell Merit Awards for SAE technical publications.

- Selected as SAE Teetor Industrial Lecturer (1985-86 and 1986-87) and invited to 

present lecture at multiple universities.

- Received the prestigious Henry Ford Technology Award for development of 

regenerative diesel particulate filter systems (1986).

- Honored with the SAE Vincent Bendix Automotive Electronics Engineering Award 

(1983).

10. Professional Service

- Chair, ASME Soichiro Honda Medal Committee (2008-Present)

- Member of the 21st Century Truck Partnership-Phase 2 Study Committee of the 

National Research Council (2010 – Present)

- Past member of the 21st Century Truck Partnership Study Committee of the 

National Research Council (2007-2008)

- Past member of the Low Heat Rejection Engines Study Committee of the 

National Research Council (1985-1986)

- Past participant in Workshop for the National Research Council’s Study on 

“Automotive Fuel Economy – How Far Should We Go?” (1991)

- Past member of the SAE Forum on Sustainable Development in Transportation 

to provide a technical response to President Clinton’s initiative on future 

technology and the environment.

- Past member and chairman of the SAE Teetor Educational Awards Committee 

- Past member of SAE ABET Relations Committee

- Past member of SAE Transaction Selection Committee for Advanced 

Powerplants and Emissions 

- Past member of SAE Gas Turbine Committee (early 1970’s)
C-1

Appendix C. Peer Reviewer Comments as Submitted, Round 1
Report Review on 

“Computer Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas 

Emission Reduction in the 2020-2025 Timeframe”

Ricardo, Inc. 

SUMMARY COMMENTS 

The objective of this reported study is to identify the relative impact of novel and advanced lightduty vehicle technologies on fuel economy and greenhouse gases in the 2020-2025 timeframe. 

The objective is pursued by comparing different “packages” of advanced powertrain technology 

through the application of a model-based vehicle simulation software in conjunction with 

experimental data and empirical rules. Vehicles comprising seven different platforms are 

considered. Representative vehicles from each platform are identified for relevance and for 

limited validation of the simulation predictions against measured acceleration and fuel 

consumption for a 2010 baseline case. In the spirit of improving the quality of the study and the 

report, the reviewer provides several general and detailed comments for consideration by the 

contracting agency and the authors of the report. 

The report is intended to provide administrators, product planners and legislators a practical tool 

for assessing what is achievable, as well as insight into the complexity of the path forward to 

reach those advances that will be useful for productive discussions between EPA and the 

manufacturers. This path forward involves trade-offs among many design choices involving 

available, and soon-to-be-available advances in engine technologies, hybridization, transmissions 

and accessories. The current version of the simulation effort seems reasonably balanced in the 

attention paid to each of these areas. The range of improvements shown in the technologies 

considered and examples is encouraging. 

Overall, the project attempts to undertake an analytical technology assessment study of 

significant scope. It does a fairly competent job at analyzing a select number of technologies and 

packages, mostly aimed at improving the gasoline IC engine, and to a less extent the diesel 

engine. It complements improvements on the engine side with synergistic developments on the 

transmissions, hybrids and accessories. The main shortcoming of the study is that the 

methodology relies extensively on proprietary and undisclosed data, as well as empirical rules, 

correlations and modifiers without citing published reference sources. Beyond the perceived 

lack of transparency, keeping up with new technologies or approaches will necessarily involve 

new versions of the program since the actual models of the technologies used are proprietary and 

the choice and range of parameters available to users is fixed and to some extent hidden. Due to 

these constraints, the simulation tool is limited in its ability to provide fundamental insight; this 

will require a more basic thermodynamic approach, perhaps best carried out by universities. 

For the most part, the right technologies are being considered. However, certain promising 

technologies and fuel options for IC engine technologies (other than gasoline and diesel) that can 

make a significant contribution to the improvement of mpg and reduction of CO2 emissions have 

not been considered, or even mentioned at all. Primary examples are advanced combustion 
technologies, such as high pressure, dilute burn, low temperature combustion (e.g., Homogeneous 

Charge Compression Ignition, Partially Premixed Compression Ignition, Spark-Assisted 

Compression Ignition), and closed-loop, in-cylinder pressure feedback. Some of these combustion 

technologies have the potential to improve fuel economy by up to 25%. Another significant 

assumption is that fuels used are equivalent to either 87 octane pump gasoline or 40 cetane pump 

diesel. However, advanced biofuels, particularly from cellulosic or lingo-cellulosic bio-refinery 

processes, which from the standpoint of a life cycle analysis have strong potential for reduction of 

CO2 emissions, can have significantly different properties (including octane and cetane numbers) 

and combustion characteristics than the current fuels. Note that over 13 billion gallons of 

renewables were used in 2010, primarily from corn-ethanol and some biodiesel. According to the 

Renewable Fuel Standard, 36 billion gallons of renewables need to be used by 2022. Also, a joint 

study carried-out by Sandia and General Motors has shown that ninety billion gallons of ethanol 

(the energy equivalent of approximately 60 billion gallons of gasoline) can be produced in the US 

by year 2030 under an aggressive biofuels deployment schedule. 

The report is lengthy at places, for instance in the description of technologies which users of the 

simulation software are likely to be already familiar with, while too laconic at other places, e.g. 

how the selected technologies were modeled in some detail. The draft can benefit from better 

balancing of its sections. There should also be more words summarizing the illustrative results 

(e.g., provide ranges of benefits), and assessing them critically (e.g., which technologies seem to 

incrementally or additively contribute the most), rather than just stating that the results are in 

Table 7.1 or in Appendix 3. A discussion of uncertainties present in the analysis should be 

presented so as to enable the reader to place the findings into proper perspective. 

The characterization of the modeling methodology as objective and “scientific” suggests that the 

simulation is composed of rigorous, first-principle expressions for the various phenomena 

without using “correlations”, “empirical formulas”, and “phenomenological models”. Are these 

conditions truly met? For instance, in many cases, steady-state dyno test data are the basis of an 

engine map featuring a certain technology. In other cases, available data were scaled based on 

empirical/proprietary factors and modifiers. The report should not characterize the study as 

“scientific” unless data uncertainty is discussed and shown in appropriate situations. For 

example, Table 7.1 presents comparisons between simulated and actual vehicle fuel economy 

performance. Given the various subjective assumptions involved in the analysis, the authors 

should comment whether the noticeable differences in certain cases are significant. 

TECHNICAL COMMENTS 

(1)$ Inputs' and' Parameters.$ $ Please$ comment$ on$ the$ adequacy$ of$ numerical$ inputs$ to$ the$ model$ as$

represented$by$default$values,$ fixed$values,$and$user<specifiable$parameters.$$Examples$might$include:$

engine$ technology$selection,$battery$SOC$swing,$accessory$load$assumptions,$etc.$$Please$comment$on$

any$caveats$or$limitations$that$these$inputs$and$parameters$would$affect$the$final$results.

• The report describes a comprehensive set of engine and vehicle technologies for the prediction 

of GHG emissions and performance. However, the full range of inputs and parameters is not 

explicitly presented. It requires the reader to refer to the Data Visualization Tool figures to 

understand what exactly can be varied when querying the RSM. Even within the actual tool 
simulation environment, it is impossible to extract details on, or judge the basis for a number of 

critical inputs. In some occasions, the report mentions that published data have been used, but 

there are no references to the source. Baseline engine maps, torque converter maps and 

shifting maps, electric machine efficiency maps, and control strategies for hybrids, which have 

very direct effects on vehicle performance and emissions, should be presented in the report, at 

least in a limited format. Below are some examples of the types of inputs and parameters that 

would be helpful to include the following in the report: 

(i) Any published fuel economy maps, or other related data, with actual numbers. For 

proprietary maps and data, a normalized representation would be useful, as well, 

without the actual bsfc values shown on the map. 

(ii) Baseline maps used to represent turbomachinery, in actual or normalized form 

(iii) The baseline vehicle cooling system and accessory schematic vs. cooling system and 

accessory load schematics of the future engines considered in the simulation 

(iv) Details of EGR modeling parameters, such as maps showing percentage of EGR being 

used at various loads. 

(v) Details of warm-up model parameters, such as ambient temperature; warm up friction 

correction; cold start fuel consumption correction factor; generation of heat rejection 

maps for various combinations in the simulation matrix 

• The engine technology selection appears somewhat limited in terms of the selected 

combinations. For example, why is the Atkinson engine not boosted as well? Moreover, a 

variable valve actuation technology, as common and important as variable cam phasing, is not 

included. As already stated in the introductory comments, advanced combustion technologies, 

such as HCCI, are worth considering. More flexibility in the engine and vehicle parameters 

would also allow better understanding of the improvements obtained for individual technologies 

and possibly even show some potential synergies not currently identified. 

• Alternative fuels are currently a key research topic and very important for future energy 

independence. Because usage of these fuels can have an impact on efficiency and emissions, the 

study would be enhanced if engine performance maps with various fuels were included. 

(2)$ Simulation'methodology.$ $ Please$ comment$ on$ the$ validity$ and$ applicability$ of$ the$methodologies$

used$ in$ simulating$ these$ technologies$ with$ respect$ to$ the$ entire$ vehicle.$ $ Please$ comment$ on$ any$

apparent$unstated$or$implicit$assumptions$and$ related$caveats$or$limitations.$$Does$ the model$handle$

synergistic$affects$of$applying$various$technologies$together?

• The RSM approach is certainly a good way to provide quick access to wide range of results, 

but it has the limitation that a large number of assumptions have to be made ahead of time in 

order to determine the design space. Also, creating these encompassing RSM’s requires a 

significant amount of simulations, and all the results will not necessarily be of interest. If a 

more flexible model/simulation was created and coupled to a user-friendly interface, users 

might be able to obtain and analyze the desired results instead of being constrained by the 

design space previously determined. 
• Even though the authors attempt to describe the simulation methodology and assumptions in 

the report, it lacks details of the models employed, which makes it hard to determine if 

refinements need to be made, or even if more appropriate models/methods should be used. It is 

understandable that, due to the proprietary data, it is not possible to present everything. 

However, without any of this information, the RSM results are more difficult to interpret. 

Specific suggestions regarding models that need more detailed coverage are given below: 

Engines and Engine Models (Sections 4.1 and 6.3) 

It is not clear whether the engine maps in the simulation tool were generated based on 

simulations or existing experimental data, somehow fitted and scaled to the various 

configurations. In general, the explanation on how maps were obtained is vague for such an 

important component. In one section, the report states that the fueling maps and other engine 

model parameters used in the study were based on published data. If so, it would be nice to 

have a list of the published materials that have been used as the resource. In Section 4.2, the 

report states that the performance of the engines in 2020-25 were developed by taking the 

current research engines and assuming the performance of the 2020 production engines will 

match that of the research engine under consideration. Does this assumption take into account 

the emission standards in 2020, and do the current research engines match those emission 

standards? What is the systematic methodology that has been adopted to scale the performance 

and fuel economy of current baseline engines to engine models for 2020-25? Also, the report 

lacks detail concerning the methodology of extrapolating from available maps to maps 

reflecting the effects on overall engine performance of the combination of the future 

technologies considered. 

The report lacks detail on the specifics on the different engine design and operating choices. 

For instance, what was the compression ratio (and limit) that was used? What is the 

equivalence ratio, or range considered, for the lean burn engine? How much EGR has been 

used across the speed and load range? What constraints, if any, were applied to the simulations 

to account for combustions limitations such as knock and flammability limits? The NOx 

aftertreatment/constraints section could also be expanded. 

In cases where engine models have been used to generated maps, how was combustion 

modeled? For instance, discussion is made as to the heat transfer effect resulting from surface 

to volume changes connected to downsizing. More detail on the heat transfer assumptions that 

go into the applied heat transfer factor would be helpful. Was heat transfer modeled based on 

Woschni’s correlation? What about friction scaling with piston speed? This would change 

with stroke at a constant RPM. Also friction would change with the number of bearings and 

cylinders. 

Turbocharger systems (Section 4.1.3) 

There is no discussion of turbocharger efficiencies and their range. Did the simulations assume 

current boosting technologies? Were maps used for this simulation or some other 

representation? Was scaling used? What were the allowed boost levels? 
Intelligent Cooling Systems (Section 4.3.1) 

The report describes intelligent cooling systems, but does not provide any estimates of the 

anticipated reductions in fuel consumption over the FTP cycle, though related papers have 

been published in the open literature. 

Sizing of various cooling components plays a very crucial role in fuel economy predictions. 

The report does not provide any detail on how the optimum cooling flow required for a given 

engine- transmission combination was determined. This would significantly affect the oil, 

coolant and transmission oil pump RPMs, which would in turn significantly change the 

accessory loads. 

In addition, the report does not have any discussion on how modified cooling components 

(radiator, condenser, etc.) would be sized for more efficient powertrains. For instance, a more 

efficient engine that would reject less heat would likely need a smaller radiator and lesser 

airflow through the radiator; hence, the grill opening could be reduced to cut down on aero 

drag. A high efficiency transmission will not reject a lot of heat to the transmission oil; thus, a 

smaller transmission oil cooler could be used. 

Warm-up methodology (Section 6.3.1) 

This section talks about using engine warm-up profile during the cold start portion to ascertain 

additional fueling requirements. It talks about a correction factor to account for this additional 

fuel. How was this factor determined? Has a different correction factor been used for various 

engines? For instance, for a lean-burn engine that reject less heat, the oil warm-up is slower 

compared to a baseline engine. Was a new heat rejection map generated to account for start-up 

enrichment while modeling the warm-up? What is the ambient temperature that has been 

considered while performing the FTP 75 fuel economy test? Have the viscous effects of 

engine oil considered in the warm up simulation? How have the friction losses for various 

valvetrain engine combinations been modeled? 

Accessories Models (Section 6.3.2) 

Alternator efficiency has been assumed to be constant around 55% for baseline. In the current 

baseline vehicles the alternator efficiencies do vary with the temperature and load. 

Has AC compressor load been considered in any of the simulations? In some of the new cycles 

being proposed by EPA, it is required that AC remains ON throughout the cycle. Hence, 

management of the AC load is very critical. 

Transmission Models (Section 6.4) 

The transmission efficiencies vary by almost 10-15% based on the transmission oil 

temperature. How have these effects been modeled? 

Constraints 

There is no discussion in the report that discusses the constraints on the combinations that can 

be implemented in real life. For example, would a multi-air system that is currently designed 

for small size engines work for a full size car? 
(3)$Results.$$Please$comment$on$the$validity$and$applicability$of$the$results$to$the$light<duty$vehicle$fleet$

in$ the$ 2020<2025$ timeframe.$ Please$ comment$ on$ any$ apparent$ unstated$ or$ implicit$ assumptions$ that$

may$affect$the$results,$and$on$any$related$caveats$or$limitations.

• For the vehicle performance simulation results shown in Table 7.1, were there any significant 

adjustable parameters used to fit these vehicles? 

• Even though it appears that the validation results from the simulation have “acceptably” close 

agreement with the test data, there are up to 15% off. Even for the small car where all data is 

available, the error is on the order of 5%. These discrepancies are usually not negligible and 

should be taken into account when conclusions are drawn from the results, especially if 

regulation is to be proposed based on these. 

• There is also no baseline hybrid configuration and no validation of the hybrid model. Due to 

the increased complexity of these vehicle systems, it is important to ensure the parameters and 

assumptions are valid. 

• It would be desirable to include a complete test case with the appropriate inputs, analysis and 

outputs as part of the report. The sample results presented in figures seem to have been 

included to indicate the RSM and Data Visualization Tool’s capabilities, but they do not 

provide a complete picture from which to draw solid conclusions. 

• The plots showing simulation results in blue, red, etc. could be better labeled (i.e. legends 

could be inserted in the plots) and possibly presented in a relative format indicating percent 

improvements over the baseline engine rather than absolute numbers. This is more of a 

personal choice for a more clear representation of the predicted improvement, rather than 

stating that there is anything wrong with the current representation. 

(4)$ Completeness.$ $ Please$ comment$ on$ whether$ the$ report$ adequately$ describes$ the$ entire$ process$

used$in$the$modeling$work$from$input$selection$to$results.

• Some of the aspects lacking form the report have already been mentioned and discussed in the 

relevant sections. 

• In general, the report provides a fair description of the modeling process. Unfortunately, there 

are no equations, plots or maps showing any specific modeling item, thus making this part of the 

report vague. 

• It might be possible to shorten the descriptions related to the individual technologies 

implemented and their improvements and add more details on how they have been modeled. 

People using this tool will most likely not use the brief descriptions of the various technologies 

to draw conclusions and make decisions. 

• The “Conclusions” section of the report should be renamed “Summary” since it does not present 

any actual conclusions based on the results, but it does provide a summary of the project. 
(5)$ Recommendations. Please$ comment$ on$ the$ overall$ adequacy$ of$ the$ report$ for$ predicting$ the$

effectiveness$of$these$technologies,$and$on$any$improvements$that$might$reasonably$be$adopted$by$the$

authors$for$improvement.$$Please$note$that$the$authors$intend$the$report$to$be$open$to$the$community$

and$transparent$in$the$assumptions$made$and$the$methods$of$simulation.$$Therefore$recommendations$

for$ clearly$ defined$improvements$ that$would$ utilize$ publicly$available$information$would$ be$ preferred$

over$those$that$would$make$use$of$proprietary$information.

• Various suggestions have already been included in the relevant sections. 

• The authors should expand the modeling sections. In particular, they should cite literature 

references (where possible) and provide more detail when empirical data, modifiers, or scaling 

laws are used. 

• Flexibility should be added to the models. Some engine technologies, such as variable cam 

phasing, HCCI and alternative fuels should be considered. 

• A self-contained study should be presented as a test case for the results so that specific 

conclusions can be drawn and the utility of the approach more easily understood.

(6)$Other'comments.$$Please$provide$your$comments$on$ report$ topics$not$otherwise$captured$by$ the$

aforementioned$charge$questions.

It would be desirable to show the analysis used to convert fuel consumption savings to vehicle 

greenhouse gas (GHG) emissions equivalent output. Ultimately, what matters is the GHG 

savings resulting from the combined production and use cycle of alternative fuel options for 

combustion engines. 

Some additional detailed comments on specific sections are given below. 

Advanced Valvetrains (Section 4.1.1) 

The report states that advanced valvetrain systems improve fuel consumption and GHG 

emissions mainly by improving engine breathing. Other benefits cited are in supporting engine 

downsizing and faster aftertreatment warm-up. Beyond improving volumetric efficiency and 

reducing pumping losses, advanced valvetrains can enable compression ratio variation to 

increase fuel economy and avoid knock, alter the combustion process by modulating trapped 

residual, and enable cylinder deactivation to reduce pumping losses. From the report, it is not 

clear which of the possible benefits of the advanced valvetrain packages have been harnessed 

in each case. A more systematic analysis of technology package combinations is warranted as 

several are synergistic but not additive. 

Boosting System (4.1.3 and 6.3) 

A two-stage system is indeed promising for advanced turbocharging concepts. A distinction 

should be made between series and sequential configurations. Air flow manipulation can make it 

a series system (two-stage expansion and compression) or a sequential system (turbos activated 

at different rpm). Variable geometry or twin-scroll turbines can be good options for the low or 

high pressure stages, respectively. A two-stage turbocharging system like this would take 
advantage of the lean SI exhaust enthalpy, reduce pumping work (or even aid pumping), avoid 

mechanical work penalties, improve engine transient response, enable high dilution levels (if 

desired) and probably help keep in-cylinder compression ratio below 12:1, since significant 

compression would be done before the cylinder. EGR flow could be driven through a low 

pressure loop (after the turbines) or an intermediate pressure loop (between the turbines). The 

resulting turbo lag will depend on the details of the configuration and the control logic used. 

Note that the assumption of a time constant of 1.5 seconds (as stated in the report) to represent 

the expected delay may not hold true in all cases. 

Lean-Stoichiometric Switching (Section 4.2.2) 

The mixed-mode operation considered in the report seems to switch between stoichiometric and 

lean SI direct injection operation. There are several multi-mode combustion efforts under 

development that encompass several more combustion modes, including HCCI and Sparkassisted compression ignition with amounts of EGR dilution. 

P2 Parallel Hybrid (Section 4.3.2) 

P2 refers to pre-transmission parallel hybrid, where an electric machine is placed in between the 

engine and the transmission. While the report does not discuss details, there are two possible 

configurations: (i) a single clutch, located in between the engine and the electric machine, such 

as in the Hyundai Sonata, and (ii) two clutches, one in between the engine and the motor, and the 

other one in between the motor and the transmission, such as in the Infiniti M35 HEV. The P2 

system looks promising to achieve good efficiency, but remaining barriers include cost, drive 

quality, durability and to a lesser extend packaging. Careful consideration of details is needed to 

properly assess benefits compared to a single mode power split. Early reports have indicated that 

Nissan got 38% mpg increase out of their P2 and Hyundai got 42%, both with higher 

horsepower, as well. However, the P2 Touareg doesn't seem to meet EPA 2012 CAFE 

standards. 

 

Transmission Technologies (Section 4.4) 

What about automatic transmissions with automated clutch replacing the torque convertor and 

lock-up clutch? This is also a possibility. 

Efficient Components (Section 4.4.9) 

Efficient components should also include gears since rotating gears are also a major source of 

drag. Designing a better profile for gear teeth can reduce drag losses. 

Transmission Models (Section 6.4) 

It is claimed that gear selection will be optimized for fuel economy for a given driver input and 

road load. Can this also be adaptive? Engine performance degrades with age. This strategy 

could also lead to more gear shifts; the latter would increase hydraulic loads and frictional power 

losses in the clutch, thus eroding some of the possible fuel economy gains. 
Peer review of the report, “Computer Simulation of Light-Duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe” 

Report by: Scott McBroom

Date of Report: May 15, 2011

Charge to Peer Reviewers:

As EPA and NHTSA develop programs to reduce greenhouse gas (GHG) emissions and 

increase fuel economy of light-duty highway vehicles, there is a need to evaluate the 

effectiveness of technologies necessary to bring about such improvements. Some potential 

technology paths that manufacturers might pursue to meet future standards may include 

advanced engines, hybrid electric systems, mass reduction, along with additional road load 

reductions and accessory improvements. 

 Ricardo Inc. has developed simulation models including many of these technologies with 

the inputs, modeling techniques, and results described in the Ricardo Inc. document that you 

have been provided dated March 10, 2011. 

 EPA is seeking the reviewers' expert opinion on the inputs, methodologies, and results 

described in this document and their applicability in the 2020-2025 timeframe. The Ricardo Inc. 

report is provided for review. We ask that each reviewer comment on all aspects of the Ricardo 

Inc. report. Findings of this peer review may be used toward validation and improvement of the 

report and to inform EPA and NHTSA staff on potential use of the report for predicting the 

effectiveness of these technologies. No independent data analysis will be required for this 

review. 

Reviewers are asked to orient their comments toward the five (5) general areas listed 

below. Reviewers are expected to identify additional topics or depart from these general areas as 

necessary to best apply their particular set of expertise toward review of the report.

Comments should be sufficiently clear and detailed to allow readers familiar with the 

report to thoroughly understand their relevance to the material provided for review. EPA 

requests that the reviewers not release the peer review materials or their comments until Ricardo 

Inc. makes its report and supporting documentation public. EPA will notify the reviewers when 

this occurs. 

Below you will find a template for your comments. You are encouraged to use this 

template to facilitate the compilation of the peer review comments, but do not feel constrained by 

the format. You are free to revise as needed; this is just a starting point. 

 If a reviewer has questions about what is required in order to complete this review or 

needs additional background material, please contact Susan Blaine at ICF International 

(SBlaine@icfi.com or 703-225-2471). If a reviewer has any questions about the EPA peer review 

process itself, please contact Ms. Ruth Schenk in EPA’s Quality Office, National Vehicle and 

Fuel Emissions Laboratory by phone (734-214-4017) or through e-mail (schenk.ruth@epa.gov).
Scott McBroom 

2 

Charge Questions: 

(1) Inputs and Parameters. Please comment on the adequacy of numerical inputs to the model 

as represented by default values, fixed values, and user-specifiable parameters. Examples might 

include: engine technology selection, battery SOC swing, accessory load assumptions, etc.) 

Please comment on any caveats or limitations that these inputs and parameters would affect the 

final results.

(Section 3.2 Ground Rules for Study) The vehicle and technology selection process needs 

further discussion. My experience in these large simulation studies is that the vast majority of 

the time needs to be spent on the selection and once selected agreeing upon the model/data. 

(Section 4) There was no model data provided. Engine maps, transmission efficiency maps, 

battery efficiency maps etc need to be in the Appendices. The black box nature of the inputs is 

disconcerting. 

(Section 4.1.1.1 CPS) How were the profiles selected? Was there an optimization process for 

each engine size of a given engine type?

(Section 4.1.1.2 DVA) Was the actuation power requirement accounted for? What were the 

timing/lift profiles and what control strategy was used to select the timing/lift profile? Was this 

an active model or was the timing/lift profile preset and then unchangeable. I would expect that 

as the engine size changes and the boost changes the timing/lift profile will have to change with 

it.

(Section 4.1.3 Boosting Systems) What about superchargers? Eaton’s AMS supercharger 

systems offer high efficiency supercharges that are comparable to turbo’s and don’t have the lag 

problem. 

(Section 4.1.4 Other Engine Technologies) regarding global engine friction reduction, what 

value(s) was assigned to that? Was it the same across all engines? If so, why?

How was the FEAD electrification energy balance accomplished? Was additional load placed on 

the alternator?

No mention or consideration of cylinder deactivation technologies. This seems like pretty low 

hanging fruit, even on downsized boosted engines, especially if you deploy DVA. 

(Section 4.2 Engine Configurations) Quantification needed ...“The combinations of technologies 

encompassed in each advanced engine concept provide benefits to the fueling map....” 

How were baseline BFSC maps modified? Was it across the board improvement or were 

improvements only attributed to certain parts of the map?

(6.3 Accessories) I think the assumption that LDT cooling fans will be engine driven is incorrect. 

The new F150’s have electric fans.
Scott McBroom 

3 

Limiting the alternator to 200A is very conservative, particularly if the system voltage stays at 

14V. 

Is there any accounting for the energy conversion on hybrids from the high voltage bus to the 

low voltage? 

(6.4 Transmission Models) no efficiency maps, no description of the efficiency maps. What was 

efficiency a function of? Typically it’s gear ratio, torque and speed. 
Scott McBroom 

4 

(2) Simulation methodology. Please comment on the validity and applicability of the 

methodologies used in simulating these technologies with respect to the entire vehicle. Please 

comment on any apparent unstated or implicit assumptions and related caveats or limitations. 

Does the model handle synergistic affects of applying various technologies together?

(Section 3.4 CSM Approach) Is the CSM approach used in other applications? If so it would be 

helpful to give citations. If it was developed by Ricardo, that should be stated. The discussion 

refers to physics based models, but other than that very little about the type of modeling is 

discussed. I recall on the phone call that lumped parameter models were mentioned. There is no 

discussion of that. 

Some assessment of the model uncertainty would be helpful. This could be a qualitative rating 

assigned by the advisory committee or a more rigorous method could be used. 

More detail on the types of models is required. Do some models use first principals of physics 

and others lumped parameter? 

ANOVA or some other analytical approach to consider technology interactions needs to be 

deployed. 

It says a statistical analysis was used to correlate variations in the input factors to variations in 

the output factors. This is ambiguous. What analysis method was used? Where is it reported? I 

didn’t see anything in the results about this. It was used to generate the RSM, but what was the 

measure of fitment? How did the RSM fit compare from vehicle config to vehicle config

(Section 4.1.1 Advanced Valvetrains) There is no explanation of how CPS and DVA systems 

were modeled. There was only a description of what CPS and DVA is. 

(Section 4.2.1 Stoich DI Turbo) Quantify how did the cooled exhaust manifold/lower turbine 

inlet temp improved the BSFC map. This is a good example of technology interaction...how did 

the radiator size grow to accommodate the additional heat rejection; how did the frontal area of 

the vehicle change to accommodate the larger radiator?

(Section 4.2.2 Lean Stoich Switching) This type of tech points to one of the dangers of 

optimizing configuration/technology/control strategy to the drive cycles; that is that it has the 

potential to over constrain the design and effect the “real world” performance/fuel economy. 

(Section 4.2.4 Atkinson Cycle) How do the 2020-2025 maps differ from the 2010 maps? 

(Section 4.2.5 Advanced Diesel) Why were only the benefits of improved pumping losses or 

friction considered? What improvements were assigned to these benefits? Was it across the 

board or regional? What about advanced boosting technology for these engines? 

Ricardo’s expectation for pace and direction: I thought there was an advisory committee making 

these decisions. I’m surprised that they think boost will be limited to 17-23bar. 

(4.4 Transmission Technologies) How were the gear ratios selected? What about shift logic?
Scott McBroom 

5 

(Section 6 Vehicle Models) No discussion of how driveline inertia is handled. This is important 

in forward-looking models. 

There are several types of rolling resistance models, what type was used?

Was coast-down data from the baseline vehicles obtained or where the coefficients of rolling 

resistance and Cd modified to get the data to match? 

(6.3 Engine Models) two methods to develop engine models were discussed. It is not disclosed 

which approach was used for which engine. I recommend that one approach be developed for all 

engines or both approaches be applied to each engine to converge to a solution. 

Regarding engine downsizing, I’m not sure that the scaling approach applies to boosted engines, 

especially engine with multiple compressors as well as DVT and CPS technology.

Turbo lag applied as a first order transfer function with a time constant. How was the time 

constant selected? Was it validated? How was the improvement attributed to turbo compounding 

modeled? 

(6.3.1 Warm-up Methodology) How was the engine warmup modeled? Is it a first order transfer 

function with a time constant? It said proprietary data was used, but how? Does the method 

allow for different warmup depending on size and engine technology? 

(6.3.2 Accessories) Constant alternator efficiency and load is not a very good assumption. New 

alternator technologies and higher alternator loads due to electrification and increased electrical 

demands. Will the future still continue to use 14V or will higher voltages be used? 

(6.8 Hybrids) Were separate optimization runs to determine the best control strategy done? How 

are we assured the best control strategy is implemented?

(7.2 Nominal Runs) Was a separate matrix of simulations run to obtain the nominal sizes for the 

advanced engine or was it merely a matter of matching the peak torque.

How was a 20% reduction in engine size for the nominal hybrid engine arrived at? Even for the 

micro-hybrid (engine start/stop)?

“These summary results....used to assess the quality of the simulation....” Where is the data for 

this assessment published? What were the criteria that said pass or fail?
Scott McBroom 

6 

(3) Results. Please comment on the validity and applicability of the results to the light-duty 

vehicle fleet in the 2020-2025 timeframe. Please comment on any apparent unstated or implicit 

assumptions that may affect the results, and on any related caveats or limitations.

(Section 4.4.6 Shifting Clutch Technology) “The technology will be best suited to smaller 

vehicle segments because of reduced drivability expectations” – not in the US market.

(Section 4.4.7 Improved Kinematic Design) Assumes a sweeping improvement without 

identifying a clear rationale...doesn’t appear to describe a scientific or objective approach.

(Section 4.4.11 Lubrication) Assumes a sweeping improvement without identifying a clear 

rationale...doesn’t appear to describe a scientific or objective approach.

(Section 4.5.1 Intelligent Cooling System) The system as described seems more appropriate for 

regulated emissions reduction opportunity rather than fuel economy or GHG. I think these 

systems enable engine control strategies that aren’t part of this study that would have a greater 

impact on fuel economy than warming up the engine faster.

(5.2 Vehicle Configuration and technology combinations) Also there is no scientific or objective 

reason given for the DoE ranges. It appears that I can make any vehicle 60% less mass, 70% less 

rolling resistance etc....This will skew the results towards that end of the DoE, when they may 

not be practically achievable.

(6.1 Baseline Conventional Vehicle Model) Results were compared to the EPA Vehicle 

Certification Database. These results often include correction factors and allowances that aren’t 

documented on the sticker. Recommend that actual testing be run to perform the benchmark. 

(6.3.1 Engine Warmup Methodology) Were there hot and cold engine maps? No mention. 

(6.4 Transmission Models) Fig 6.1 appears to be a comparison of desired cvt ratio vs desired 

6spd gear ratio. Should be stated as such. The shift logic controller should take into account the 

time to shift and whether or not the desired shift is achievable. 

What are the shift optimizer inputs? What are it’s basic decision criteria?

There is no discussion of engine downspeeding. 

There is no discussion of gear ratio selection. 

(6.5 torque Converter models) The lockup strategy seems very conservative. Large gains are 

achievable with more sophisticated control and are in use today. 

What was the basis for the minimum rpm’s for lockup sited? Should be based on lugging the 

engine. The controller should recognize when it needs to unlock the TC based on the engines 

ability to keep up. 

(6.6 Final Drive Model) Only discussed the baseline, what improvements for 2020 and what final 

drive selection criteria for the future vehicles was used?
Scott McBroom 

7 

(6.7 Driver Model) How was the soak modeled? Were there hot engine maps and cold engine 

maps?

(7.1 Baseline Conventional Vehicle Models) 

Better definition of what “acceptably close” means. This doesn’t meet the criteria for 

objectivity. Something like, “the advisory committee determined that the baseline models had to 

predict within x% to be usable for this study.” 

On the performance runs, a few tenths of a second represent measurable difference in engine 

torque for example. 

(8.1 Evaluation of Design Space) Why was Latin hypercube sampling methodology picked over 

other sampling methods? While it’s attributes are mentioned, what other methods were 

considered? 

(8.2 RSM) A description of how the neural network is deployed is needed, only the why it was 

used is discussed in this section. What were the best fit criteria? What types of equations did the 

neural net have to play with? Where are the fit’s published? How was it determined that the “one 

fit per transmission” was the best way to go?

(9.1 Basic Results) Why 10Hz sampling rate? By what criteria was a run considered good vs 

bad? 

(9.3 Exploration of the Design Space) If boundaries of acceptable performance were applied, a 

considerable number of simulation runs could be eliminated.
Scott McBroom 

8 

(4) Completeness. Please comment on whether the report adequately describes the entire 

process used in the modeling work from input selection to results.

(Section 2 Objectives) A discussion of appropriate/anticipated use of the results is required. 

(Section 3.3 Ground Rules) How did the group arrive at the seven vehicles? While it show 

comprehensiveness, it’s possible to see that there could be some overlap. If one looks at the 

engine and transmissions packages available in these vehicles already you can see the overlap. 

Reducing the number of vehicles might save on the number of runs you’ll need to make. 

(Section 3.3 Technology Selection Process) Who is on the Advisory Committee? Is it 

independent? How did the program team come up with the comprehensive list of potential 

technologies? (From the phone call it sounded like it was based on what models Ricardo had in 

their library. This is concerning.) 

It said there was a comprehensive list of technologies that the group started with, that list should 

be shown and a comment on why it wasn’t included. 

Why wasn’t HCCI technology considered? From the publications this seems to be a candidate 

for production in the next 10 yrs. 

(Section 4. Technology Review and Selection) Regarding qualitative evaluation of technology 

“Potential of the technology to improve GHG emissions on a tank to wheels basis”, since this 

was a qualitative assessment I think it would be better to include well to wheels.

Regarding “Current (2010) maturity of the technology”, how was maturity ranked? 

Citations required for statement “ SI engine efficiency to approach CI efficiency in the time 

frame considered” This represents relatively large gains in SI technology compared to CI, 

however EU and Japanese engine companies are making big improvements on CI as well.

(Sections 4.1 and 4.2) There’s no descriptions of the models. There are only descriptions of the 

technologies and their perceived benefits. The reader has to assume that the same modeling 

approach was used to model each technology, but I know from personal experience this is very 

difficult and most likely not the case.

(Section 4.1.2 DI Fuel Systems) No discussion of DI control strategy. How was it selected? Was 

there a separate optimization of DI control or was it one size fits all?

(Section 4.1.3 Boosting Systems) It says that other boosting systems were included in the study, 

but only turbocharging is discussed. 

(Section 4.3 Hybrids) Don’t see any data on the battery technology, battery management, SOC 

control strategies. No discussion of regen braking strategies. 

(Section 4.3.1 Micro Hybrids) It is implied that electrified accessories aren’t used in this 

configuration. I don’t see that as the case. 
Scott McBroom 

9 

(Section 4.3.2 P2 Hybrid) No discussion of why DCT was only transmission used for P2 hybrids 

instead of CVT and AMT. 

(4.4 Transmission Technologies) What types of CVT’s were in the original mix? Toroidals, 

push-belts, Miller?

No transmission data was shown. No mass, no inertia to efficiency maps, no gear ratios. 

(4.4.1 Automatic Transmission) No logical explanation for the 20-33% improvement...how was 

this number arrived at? 

(4.4.3 Wet clutch) It said these were expected to be heavier, cost more and be less efficient than 

DCT’s so why where they included? 

(4.4.10 Super Finishing) How much improvement is attributed to super finishing? 

(4.5 Vehicle Technologies) No values for mass, rolling resistance or drag given. No discussion 

of the improvement possibilities. This would be a good place to use historical trends for vehicle 

mass reduction, aero improvements and parasitic loss improvement. 

(5.2 Vehicle Configuration and technology combinations) While the tables show the vehicle 

configurations, more discussion regarding the selection criteria for each vehicle is warranted. In 

some cases this discussion was attempted in the technology sections, but I don’t think it should 

go there. 

(Section 6 Vehicle Models) No discussion of how driveline inertia is handled. This is important 

in forward-looking models. 

There are several types of rolling resistance models, what type was used?

(6.8 Hybrid Models) Too much data is missing. What were the pack voltages? What were the 

battery technologies? Was there only one or more? Other than improved resistance, what other 

future improvements were included, like improved power density, improved usable SOC range? 

What was the control strategy for each type? 

Load leveling the engine by charging the batteries has been shown to not be a very good idea 

because the round trip efficiency hit is a killer. Should only be used when SOC falls below a 

certain level.

We’re left to assume that SOC leveling is accomplished, but there is no description of how? Was 

an EPA/SAE method used. 

When it comes to GHG reductions why weren’t plug-in hybrids considered? 
Scott McBroom 

10 

(5) Recommendations. Please comment on the overall adequacy of the report for predicting the 

effectiveness of these technologies, and on any improvements that might reasonably be adopted 

by the authors for improvement. Please note that the authors intend the report to be open to the 

community and transparent in the assumptions made and the methods of simulation. Therefore 

recommendations for clearly defined improvements that would utilize publicly available 

information would be preferred over those that would make use of proprietary information. 

1) Instead of using proprietary Ricardo data/models/control algorithms citable data should 

be used. 

2) Without stating how this model is going to be used in the regulatory decision making 

process, it is very difficult to assess its appropriateness. 

3) Considerably more time in this effort is required up front in the report, to discuss the 

process of building consensus on data and models. Because this is not really discussed, it 

gives the impression that not much was done. 

4) Guidelines for appropriate use should be given. 

5) An uncertainty rating for each model/data set should be published to highlight the relative 

differences in the assumptions/extrapolation of future technologies. 

6) Should use coast down data for baseline vehicles to model parasitic losses. 

7) In terms of acceptable use: rather that trying to use the model to assess the boundaries of 

the envelope (or which technology is better), the tool could be used to find the areas of 

maximum overlap. In other words, knowing that the same performance and fuel economy 

is achievable using different technologies lends more confidence that the result is 

achievable. Theoretically this number could be a calculated value generated from the 

RSM’s.

8) Recommend allowing “real world” drive cycles to assess the robustness of the results. 

Could be a user generated result from a composite of the data sets already generated.

9) Should define the process for data selection....eventually you’ll be asked by a 

manufacturer, ‘how do we get ‘x’ technology included for consideration in the study. 

10) Where lumped improvements are made, I recommend using historical results to publish 

technology improvement curves. For example, the parasitic losses (Cd, Crr) should be 

quantifiable. Vehicle mass reductions as well.

(6) Other comments. Please provide your comments on report topics not otherwise captured by

the aforementioned charge questions. 
Scott McBroom 

11 

Having conducted a similar effort for USCAR on the PNGV program, I understand that 

considerable effort is required to develop such a model. I don’t want to diminish all the hard 

work that was done, by only offering criticism in the above sections. It appears that the intent of 

the approach to this activity is in the right place, just better documentation is needed and 

appropriate use guidelines. 
PEER REVIEW:

Computer Simulation of Light-Duty Vehicle 

Technology for Greenhouse Gas Emission Reduction in 

the 2020-2025 Timeframe

Review Conducted for: 

U.S. EPA

Review Conducted By: 

Shawn Midlam-Mohler

Review Period:

4/28/2011 – 5/16/2011 
Shawn Midlam-Mohler - Peer Review Page 2 

Contents

Executive Summary .................................................................................................................. 3

Simulation Methodology .......................................................................................................... 4

Vehicle Model ....................................................................................................................... 5

Engine Models ...................................................................................................................... 5

Aftertreatment/Emissions Solutions ..................................................................................... 7

Advanced Valvetrains ....................................................................................................... 7

Direct Injection Fuel Systems ........................................................................................... 8

Boosting Systems .............................................................................................................. 8

Engine Downsizing ........................................................................................................... 8

Warm-Up Methodology .................................................................................................... 9

Accessory Models ............................................................................................................. 9

Engine Technology “Stack-Up” ..................................................................................... 10

Baseline Hybrid Models ..................................................................................................... 11

Hybrid Control Strategy .................................................................................................. 11

Electric Traction Components ........................................................................................ 12

HEV Battery Model ........................................................................................................ 12

Transmissions ..................................................................................................................... 13

Data Analysis Tool ................................................................................................................. 13

Conclusions ............................................................................................................................. 14
Shawn Midlam-Mohler - Peer Review Page 3 

Executive Summary

For the purpose of describing the modeling approach used in the forecasting of the 

performance of future technologies, the report reviewed is inadequate. In virtually every area, 

the report lacks sufficient information to answer the charge questions provided for the reviewer. 

It is entirely possible that the approach used is satisfactory for the intended purpose. However, 

given the information provided for the review, it is not possible for this reviewer to make any 

statement regarding the suitability of this approach. Some brief comments on each of the five 

charge questions are provided below: 

Inputs and Parameters – From a high level, it is clear what the inputs to the design space 

tool are, which are listed in tables 8.1 and 8.2. At the next level down (i.e. the vehicle and 

subsystem models) there is no comprehensive handling of inputs in parameters in the report. 

Some models are partially fleshed out in this area but most are lacking. By way of example, the 

engine models are described as maps which are “defined by their torque curve, fueling map, and 

other input parameters” where “other input parameters” are never defined. 

Simulation Methodology – The vehicle model is reported as “a complete, physics-based 

vehicle and powertrain system model” - which it is not. The modeling approach used relies 

heavily on maps and empirically determined data which is decidedly not physics-based. This 

nomenclature issue aside, the model is not described in sufficient detail in the report to make an 

assessment in this area. An excellent example of this is the electric traction drives and HEV 

energy storage system for which the report mentions no details, even qualitative ones, on the 

structure of the models. 

Results - The third charge questions deals with the validity and the applicability of the 

resulting prediction. The difficulty in this task is that it is an extrapolation from present 

technology that uses an extrapolation method (i.e. the model) and a set of inputs to the model 

(i.e. future powertrain data.) Since it is not possible to validate the results against vehicles and 

technology that do not exist, one can only ensure that the model and the model inputs are 

appropriate for the task. Because of the lack of transparency in the model and inputs it is 

difficult to make any claims regarding the results. In trying to validate results, one example is 
Shawn Midlam-Mohler - Peer Review Page 4 

cited in the body of the report that shows the baseline engine getting superior HWFET and US06 

fuel economy than all of the other non-HEV powertrains with other factors being the same – this 

leaves some skepticism regarding the results.

Completeness – Based on the above, it is clear that this reviewer feels the report is 

inadequate at describing the entire process of modeling work from input selection to results. 

There was not a single subsystem that was documented at the level desired. It is understood that, 

in some cases, there are things of a proprietary nature that must be concealed. As a trivial 

example, the frontal area of the vehicle classes does not seem to be anywhere in the report or 

data analysis tool. This is one parameter amongst hundreds excluding the real details of the 

models (i.e. equations or block diagrams), methods used to generate engine maps, details on 

control laws, etc. On the topic of proprietary data, there are many ways of obscuring data 

sufficiently that can demonstrate a key point (i.e. simulation accuracy) without compromising 

confidentiality of data – this should not be a major barrier to providing some insight into the 

inner working of the simulator. 

Recommendations – Given the low level of detail given in the report, it does seem that the 

strategy used is consistent with the goal of the work and what others in the field are doing. That 

being said, the report is inadequate in nearly every respect at documenting model inputs, model 

parameters, modeling methodology, and the sources and techniques used to develop the 

technology performance data. Given the need for transparency in this effort, this reviewer feels 

that the detail in the report is wholly inadequate to document the process used. The organization 

responsible for the modeling has expertise in this area it is certainly possible that the 

methodology is sound, however, given just the information in the report there is simply no way 

for an external reviewer to make this conclusion. 

Because of the lack of hard information to answer the charge questions, this peer review 

evolved mainly into a suggested list of details that should be brought forward in order to allow 

the charge questions to be answered properly. With this information, it is hoped that a person 

with expertise in the appropriate areas will be able comment on the work more fully. 

Simulation Methodology
Shawn Midlam-Mohler - Peer Review Page 5 

The simulation methodology is generally not described in the report in sufficient detail to 

assess the validity and accuracy of the approach. The models and approach are described 

qualitatively; however, this is insufficient to truly evaluate the ability of the modeling approach 

to perform the desired function. The following subsections address specific issues with the 

models, inputs, and parameters and suggest possible corrective actions to address these issues.

Vehicle Model

The vehicle model is described as “a complete, physics-based vehicle and powertrain system 

model” developed in the MSC.Easy5TM simulation environment. This description is not 

particularly helpful in defining the type of model as portions of the model are clearly not physics 

based, such as the various empirical maps used or sub-models like the warm-up model which is 

by necessity an empirical model due to the complexity of the warm-up process compared to the 

expected level of fidelity of the model. It is assumed that a standard longitudinal model accounts 

for rolling losses, aero losses, and grade is used to model the forces acting on the vehicle. Input 

parameters for the vehicle model are not described. The baseline vehicle platforms are listed, 

however, the relevant loss coefficients are not provided (rolling resistance, drag coefficient, 

inertia.)

Suggested Corrective Action: 

1. List the dynamic equation describing the longitudinal motion of the vehicle

2. List all parameters used for each vehicle class for simulation

Engine Models

The engine model is the most important element in successfully modeling the capability 

of future vehicles, since it is the responsible for the largest loss of energy. It is also one of the 

most difficult aspect to predict since it involves many complicated processes (i.e. combustion, 

compressible flow) which must be considered in parallel with emissions compliance (i.e. incylinder formation, catalytic reduction.) Because of this, this sub-model must be viewed with 

extreme scrutiny in order to ensure quality outputs from the model. 

The engine models are “defined by their torque curve, fueling map, and other input 

parameters.” This implies that the maps are static representations of fuel consumption versus 

torque, engine speed, and other unknown input parameters. Generally speaking, representing 

engine performance in this fashion is consistent with typical practice for this class of modeling. 
Shawn Midlam-Mohler - Peer Review Page 6 

This comment deals only with the representation of the engine performance in simulation, the 

generation of the data contained within the map is much more challenging.

The report outlines two methods were used to produce engine models. The first method 

was used for boosted engines and relied upon published data on advanced concept engines which 

would represent production engines in the 2020-2025 timeframe. The second method was used 

with Atkinson and diesel engines and somehow extrapolated from current production engines to 

the 2020-2025 time frame. The description of both of these methods in the report is 

unsatisfactory. It also fails to address how the various technologies are used to build up to a 

single engine map for a specific powertrain. Validation, to the extent possible with future 

technologies, is also lacking in this area.

This reviewer took some time to look at the data via the tool provided. One table is 

shown in Figure 1 which shows some unexpected results. The results are for a small car with the 

dry clutch transmission and it shows the baseline engine having superior fuel economy over all 

other non-hybrid powertrain options. This is unexpected behavior and, since there is minimal 

transparency in the model, it cannot be investigated any further. 

Engines FTP HWFET US06

Baseline 42.1 62.5 37.0

Stoich_DI_Turbo 46.3 55.3 33.7

Lean_DI_Turbo 48.3 56.4 33.9

EGR_DI_Turbo 48.2 57.6 35.2

Atkinson_CPS 44.5 59.0 35.4

Atkinson_DVA 45.5 57.1 34.5

Figure 1: Simulation Results Different Engines for Small Car with 8Dry_DCT and all other Parameters Constant

Suggested Corrective Action:

1. Provide fuel and efficiency map data for all engines used in simulation

2. Describe what the “other inputs” are to the engine maps

3. Provide specific references of which published data was used to predict performance of 

the future engines. Some references are given, however, it is not clear how exactly these 

references are used.

4. Wherever possible, provide validation against data on similar technologies

5. Describe in detail the approach used to “stack up” technologies for a given powertrain 

recipe
Shawn Midlam-Mohler - Peer Review Page 7 

Aftertreatment/Emissions Solutions

Based on the report, it seems that emissions solutions are assumed to be available for all 

powertrain technology packages selected. The report discusses this in some qualitative detail in 

section 4.2.2 with respect to lean-stoichiometric switching. This discussion is somewhat 

incomplete, in that the way it is written it assumes operating at stoichiometry lowers exhaust gas 

temperature. In reality, switching from lean to stoichiometric operation at constant load results 

in higher exhaust gas temperatures. Despite this factual inconsistency, it is indeed generally 

better to operate a temperature sensitive catalyst hot and stoichiometric or rich rather than hot 

and lean – so the concept of lean-stoich switching is valid even if the explanation provided is not. 

Even without this factual inconsistency, some additional discussion of aftertreatment systems 

would be of benefit given that lean-burn gasoline engines are at present a well-known technology 

for many years that is still problematic with respect to emissions control. A separate issue is the 

topic of fuel enrichment for exhaust temperature management which will have an important 

impact on emissions and, if emissions are excessive, reduce the peak torque available from an 

engine.

Suggested Corrective Action:

1. Provide better evidence that powertrain packages have credible paths to meet emissions 

standards 

2. Provide evidence that fuel enrichment strategies are consistent with emissions regulations

Advanced Valvetrains

Two types of advanced valvetrains were included in the study, cam-profile switching and 

digital valve actuation. Both of these technologies are aimed at reducing pumping losses at partload. The impact of these technologies is difficult to predict using simplified modeling 

techniques and typically require consideration of compressible flow and a 1-D analysis at a 

minimum. Even with an appropriate fidelity model, these systems require significant amounts of 

optimization in order to determine the best possible performance across the torque-speed plane 

of the engine. It is unclear how these systems were used to generate accurate engine maps given 

the level of detail provided in the report. 

Suggested Corrective Action:
Shawn Midlam-Mohler - Peer Review Page 8 

1. Describe how variable valve timing technologies were applied to the base engine maps

2. Describe the process of determining the extent of the efficiency improvement

3. Describe how optimal valve timing was determined across the variety of engines 

simulated

Direct Injection Fuel Systems

Because of the availability of research and production data in this area, it is expected that 

performance from this technology was used to predict performance rather than any type of 

modeling approach. That being said, the report does not describe where or how this data might 

have been used to develop the fuel consumption map of the engines simulated nor what data 

sources were used.

Suggested Corrective Action:

1. Cite sources of data used to predict DI performance 

2. Describe how this data was used to develop the future engine performance maps

3. Provide validation of modeling techniques used 

Boosting Systems

 Boosting was applied to many of the different powertrain packages simulated. Beyond 

stating what maximum BMEP that was achievable, very little is mentioned in how the efficiency 

of the boosted engines were determined. Among other factors, boosting often creates a need for 

spark retard which costs efficiency if compression ratio is fixed. These complex issues are tied 

to combustion which is inherently difficulty to model. This aspect of the engine model is not 

well documented in the report. 

Suggested Corrective Action:

1. Describe the process of arriving at the boosted engine maps 

2. Describe how factors like knock are addressed in the creation of these maps

Engine Downsizing

Engine scaling is used extensively in the report. Basic scaling based on brake mean 

effective pressure is common in modeling at this level of fidelity, thus, this does not need any 

special description. However, the report mentions some means of modeling the increased 
Shawn Midlam-Mohler - Peer Review Page 9 

relative heat loss with small displacement engines which is not a standard technique. The model 

or process used to account for this effect should be explicitly described given that engine size is 

one of the key parameters in the design space.

Suggested Corrective Action:

1. Properly document the process of scaling engines 

2. Validate the process used to scale engines

Warm-Up Methodology

The report describes a 20% factor applied to bag 1 of the FTP-75 for baseline vehicles and a 

10% factor applied to the advanced vehicles. The motivation for these factors is described 

qualitatively and is valid, as many organizations are currently investigating strategies to 

selectively heat powertrain components to combat friction effects. However, the values for these

factors that were selected are not backed up with any data or citation. It is suspicious that the 

two values cited are such round numbers - the data from which these numbers are derived should 

be cited. Because of the complexity of this phenomenon, some type of empirical model is 

justified. The model described in the report is not sufficiently validated to judge its suitability.

Suggested Corrective Action:

1. Cite sources of data for 10% and 20% factors applied to the cold bag fuel economy 

data

2. Cite and/or validate the modeling approach used 

Accessory Models

The accessory model is divided into electrical and mechanical loads. The electrical submodel assumes alternator efficiency’s of 55% and 70% for the baseline and advanced vehicles 

respectively. Given the required simplicity of the model, a simple model like this is likely 

acceptable, however, there is no source described for the alternator efficiencies. The base 

electrical load of the vehicle is mentioned briefly, however, no numerical values are given for 

each vehicle class or any type of model described.

The electrical system also includes an advanced alternator control which allows for 

increased alternator usage during decelerations for kinetic energy recovery. The control 

description given is valid but simplistic, but seems to fit the expected level of accuracy required 

for the purpose. There is an issue regarding with the approach for modeling the battery during 
Shawn Midlam-Mohler - Peer Review Page 10

this process. When charging the battery at the stated level of 200 amps, the charging efficiency 

of the battery will be relatively poor. During removal of the energy later, there will once again 

be an efficiency penalty. There is no description of a low-voltage battery model in the report nor 

any explicit reference to such charge/discharge efficiencies. Additionally, an arbitrary limit of a 

200 amp alternator is defined for all vehicle classes – it is unlikely that a future small car and a 

future light heavy duty truck will have an alternator with the same rating.

On the mechanical side, it is assumed that “required accessories” (e.g. engine water 

pump, engine oil pump) are included in the engine maps. The mechanical loading of a 

mechanical fan is mentioned but no description of the model which, at a minimum, should be 

adjusted based on engine speed and engine power.

Suggested Corrective Action:

1. Cite and/or validate the alternator efficiency values of 55% and 70% 

2. Account for charge/discharge losses in the advanced alternator control and/or 

describe the 12V battery model used for the simulation 

3. Describe, cite, and validate the accessory fan model used in the simulation

4. Justify the use of a 200 Amp advanced alternator across all of the vehicle platforms.

Engine Technology “Stack-Up”

There are a host of different technologies superimposed to create the future powertrain 

technologies. There is not a clear process described on how this technology “stack-up” is 

achieved. For instance, an advanced engine technology may allow for greatly improved BMEP. 

Greatly improved BMEP often comes at the expense of knock limits which are difficult to model 

even with sophisticated modeling techniques. In this simulation, many layers of powertrain 

technology are being compounded upon each other which will not simply sum up to the best 

benefits of all of the technologies – there are simply too many interactions. At the level of 

modeling described, which are maps which are altered in various unspecified ways; it is not clear 

how the technology stack-up is captured. 

Suggested Corrective Action:

1. Describe in greater detail the approach used to model technology stack-up on the 

advanced vehicles

2. Provide some form of validation that this approach is justified
Shawn Midlam-Mohler - Peer Review Page 11

Baseline Hybrid Models

The following subsections deal with issues related to the hybrid component models. 

Hybrid Control Strategy

Hybrid vehicles are particularly challenging to model because of the extra components 

which allow multiple torque sources, and thus, require some form of torque management strategy 

(i.e. a supervisory control.) The report briefly describes a proprietary supervisory control 

strategy that is used to optimize the control strategy for the FTP, HWFET, and US06 drive cycle. 

The strategy claims to provide the “lowest possible fuel consumption” which seems to be 

somewhat of an exaggeration – this implies optimality which is quite a burden to achieve and 

verify for such a complicated problem. The strategy also is reported to be “SOC neutral over a 

drive cycle” which is also difficult to achieve in practice in a forward looking model. Once can 

get SOC with a certain window, however, short of knowing the future or simply not using the 

battery - it is impossible to develop a totally SOC neutral control strategy. 

Another factor that must be considered is that a hybrid strategy that achieves maximum 

fuel efficiency on FTP, HWFET, and US06 does not consider many other relevant factors. 

Performance metrics like 0-60 time and drivability metrics often suffer in practice. In today’s 

hybrids, the number of stop-start events is sometimes limited from the optimum number for

efficiency because of the emissions concerns. Because of these factors and others, a strategy 

achieving optimal efficiency may be higher than what can be achieved in practice.

Without even basic details on the hybrid control strategy, it is simply not possible to 

evaluate this aspect of the work. Because of the batch simulations with varying component sizes 

and characteristics, this problem is not trivial. Supervisory control strategies used in practice and 

in the literature require intimate knowledge of the efficiency characteristics and performance 

characteristics of all of the components (engine, electric motors/inverters, hydraulic braking 

system, and energy storage system) to develop control algorithms. This concern is amplified by 

the lack of validation of the hybrid vehicle model against a known production vehicle. It is 

unclear how a “one-size fits all” control strategy can be truly be perform near optimal over such 

widely varying vehicle platforms.
Shawn Midlam-Mohler - Peer Review Page 12

A last comment is that there is no validation of the HEV model against current 

production vehicles. At a minimum, the Toyota Prius has been dissected sufficiently in the 

public domain to conduct a validation of this class of hybrid electric vehicle.

Suggested Corrective Action: 

1. Better describe the hybrid control strategy and validate against a current production 

baseline vehicle

2. Validate that the HEV control algorithm performs equally well on all vehicle classes

3. Validate that other vehicle performance metrics, like emissions and acceleration, are not 

adversely impacted by an algorithm that focuses solely on fuel economy. The emission 

side of things will challenge to validate with this level of model, however, some kind of 

assurance should be made to these factors which are currently not addressed at all. 

Electric Traction Components

The model of electric traction components is not discussed in any detail, as the only 

mention in the report is that current technology systems were altered by “decreasing losses in the 

electric machine and power electronics.” Given the importance of the electric motor and inverter 

system in hybrids this is not acceptable.

Suggested Corrective Action: 

1. Describe the method used to model electric traction components 

2. Provide validation/basis for the process used to generate future technology versions of 

these components 

3. Describe the technique used to scale these components 

HEV Battery Model

Battery models for HEVs are necessary to adequately model the performance of an HEV. 

The report provides no substantive description of the battery pack model, other than that the 

model was developed by “lowering internal resistance in the battery pack to represent 2010 

chemistries under development.” Battery pack size is also not a currently a factor in the model – 

this has a impact of charge and discharge efficiency of the battery pack.

Suggested Corrective Action: 

1. Describe the method used to model the HEV battery 
Shawn Midlam-Mohler - Peer Review Page 13

2. Provide validation/basis for the process used to generate future technology versions of the 

battery

3. Describe the technique used to scale the HEV battery 

Transmissions

This peer reviewer is not as well-practiced in transmissions as in other areas in this 

review. Because of this, a more limited review was conducted of this aspect of the report. As 

with the other areas of the report, the general concern in this area is the inadequacy of 

documentation of the modeling approach and validation. Generically, the same issues noted 

above are applicable here:

1. Cite data sources used in modeling 

2. Validate models wherever possible 

3. Fully describe transmission models/maps and processes used to generate them

4. Fully describe clutch/torque converter models/maps and processes used to generate them

5. Fully describe the process used to generate shift maps and the operation of the shift 

controller 

6. Fully describe the lockup controller (i.e. how soon can it enter lockup after shifting?) 

7. Fully describe the process for modeling torque holes during shifting 

8. Fully describe the model used for the final drive (i.e. inputs/structure/outputs) 

Data Analysis Tool

The vehicle simulator is used to generate several thousand simulations using a DOE 

technique. This data is then fit with a neural-network-based response surface model in which the 

“goal was to achieve low residuals while not over-fitting the data.” This response surface model 

then becomes the method from which vehicle design performance is estimated in the data 

analysis tool. In this case, the response surface model is nothing more than a multi-dimensional 

black-box curve fit. There was no error analysis given in the report regarding this crucial step. 

By way of example, the vehicle simulator could provide near perfect predictions of future 

vehicle performance; however, a bad response surface fit could corrupt all of the results. 
Shawn Midlam-Mohler - Peer Review Page 14

Suggested Corrective Action:

1. Provide error metrics for the neural network RSMs (i.e. R2, min absolute error, max 

absolute error, error histograms, error standard deviation, etc.) before combining the fit 

and validation data sets 

2. Provide the error metrics described above for the RSMs after combining the fit and 

validation data sets

3. Provide validation that the data analysis tool correctly uses the RSM to predict results 

very close to the source data (i.e. demonstrate the GUI software behaves as expected)

Conclusions

As outlined in the executive summary, it was not possible to answer the charge questions 

provided for this peer review due to lack of completeness in the report. Thus, this report was 

aimed at providing feedback on what information would be helpful to allow a reviewer to truly 

evaluate the spirit of the charge questions. With the above in mind, the following conclusions are 

made.

The modeling approach describe in the report could be appropriate for the simulation task 

required and is generally consistent with approaches used by other groups in this field. The 

conclusions from the report could very well be sound; however, there is insufficient information 

and validation provided in the report to determine if this is the case. The technique used to 

analyze the mass simulation runs could also be sound, although the accuracy of the response 

surface model is not cited in the report. 

These issues are summarized in the following key areas: 

1. The process of arriving at the performance of the future technologies is not well 

described

2. The majority of models are only described qualitatively making it hard or impossible 

to judge the soundness of the model 

3. Some of the qualitative descriptions of the models indicate that models do not 

consider some important factors 

4. Because of the qualitative nature of the model descriptions, there is a major lack of 

transparency in the inputs and parameters in the models 
Shawn Midlam-Mohler - Peer Review Page 15

5. Where precise value(s) are given for parameters in the model, the report generally 

does not cite the source of the value(s) or provide validation of the particular value 

6. Validation of the model and sub-models is not satisfactory (It is acknowledged that 

many of these technologies do not exist, but the parameters and structure of the model 

have to be based on something.) 
Review of the report 

COMPUTER SIMULATION OF LIGHT-DUTY VEHICLE TECHNOLOGIES 

FOR GREENHOUSE GAS EMISSION REDUCTION

IN THE 2020-2025 TIMEFRAME

17 May 2011

Prepared for 

ICF International 

Environmental Science & Policy Division 

Contracts Management Group

9300 Lee Highway, Fairfax, VA 22031-1207 USA 

Robert F. Sawyer, PhD

Partner

SAWYER ASSOCIATES

PO Box 6256

Incline Village, NV 89450-6256 USA

Phone 1-510-305-6602

email: rsawyer@sawyerassociates.us 

 
2 

OVERVIEW

This is a review of the report, Computer simulation of light-duty vehicle technologies for 

greenhouse gas emission reduction in the 2020-2025 timeframe, 6 April 2011, prepared by 

Ricardo, Inc. Additionally the “Complex System Tool,” which uses the results of about 500,000 

computer simulations to generate fuel economy and CO2 emissions for combinations of vehicle 

architectures, engines, and transmissions was examined. Up to 11 parameters may be varied 

within constrained limits to explore the sensitivity of fuel economy and CO2 emissions. Jeff 

Cherry of USEPA/OTAQ assisted in the installation and running of the tool. Examination of the 

tool provided additional perspective on how the computational results are to be used and the 

nature of some of the hidden assumptions. This review does not include the Complex System 

Tool, except as it may reveal the nature of the computer simulation. 

Computer simulation of light-duty vehicle technologies for greenhouse gas emission reduction in 

the 2020-2025 timeframe describes engine and vehicle technologies that are or could be available 

to improve light-duty vehicle efficiency and thereby reduce carbon dioxide emissions. It does not 

treat other greenhouse gas emissions or alternative fuels. The Federal Test Procedure (FTP) 

framework for vehicle certification constrains the analysis, thereby excluding technologies 

related to vehicle downsizing, reduced performance, and “real world” operation such as driver 

behavior compensation, air conditioning and heating load management, and loads as affected by 

speed, acceleration, turning, hills, and wind, all of which are outside of the certification tests. 

The work includes the integration of selected technologies through a “data visualization tool” 

(The Complex System Tool) for assessment of user-elected technologies. The technologies 

include both drive-train technologies and technologies to reduce vehicle load, such as drag 

reduction, rolling resistance reduction, light weighting, and improved accessories efficiency (but 

limited to intelligent cooling systems and electric power steering). Seven light-duty vehicle types 

represent the 2010 baseline and future 2020-2025 fleets. Battery electric vehicles (BEVs), plugin hybrid electric vehicles (PHEVs), and fuel-cell electric vehicles (FCEVs) are not included. 

The report describes, qualitatively, the technologies considered in a clear, logical fashion. 

Because of its proprietary nature, quantitative performance data, such as engine maps, are 

missing from the report and not accessible for this review. 

REVIEW 

This review follows the structure of the ‘charge questions”.

(1) Inputs and Parameters. Please comment on the adequacy of numerical inputs to the 

model as represented by default values, fixed values, and user-specifiable parameters. 

Examples might include: engine technology selection, battery SOC swing, accessory load 

assumptions, etc.) Please comment on any caveats or limitations that these inputs and 

parameters would affect the final results.

The vehicle classes and baseline exemplars are reasonably chosen, within the constraint that 

vehicle size, footprint, and interior volume for each class be locked to the 2010 base year. It is 
3 

likely that new vehicle classes will emerge by 2025 and/or that these “locking” restraints will be 

relaxed. 

The design of experiment (DoE) ranges, Tables 5.4, 5.5, 8.1, and 8.2, are reasonable and do not 

exclude likely sizings. The assumed alternator baseline and advanced alternator efficiencies are 

reasonable. The assumed reduction in automatic transmission losses is reasonable, but not 

aggressive for 15 development years from the baseline year. Similarly the state-of-charge swing 

for hybrid modeling of 30-70% is reasonable, but does not reflect improved battery technology 

for the 2020-25 period, which should allow a greater swing for reduced battery size, weight, and 

cost. 

(2) Simulation methodology. Please comment on the validity and applicability of the 

methodologies used in simulating these technologies with respect to the entire vehicle. 

Please comment on any apparent unstated or implicit assumptions and related caveats or 

limitations. Does the model handle synergistic affects of applying various technologies 

together? 

Ricardo simulated dynamic vehicle physical behavior using MSC Easy5TM software with 10 

Hz time resolution. This software and the time resolution are appropriate for the 

computations to show the effect of component interactions on vehicle performance. 10 

Hz time resolution is sufficient to capture both driver behavior and vehicle response. 

Should the application of information technology, as is being implemented, as a means of 

vehicle control for reducing fuel consumption become a future strategy, the model should 

be able to provide a suitable simulation.

Drivetrain synergistic effects seem to be predicted reasonably. This was demonstrated by 

calculation of fuel economy of the baseline vehicles and comparison with EPA 

certification test data. The model does not seem to have the capability to capture vehicle

weight-drivetrain synergistic effects. Vehicle weight reductions associated with drivetrain 

efficiency improvements are input rather than modeled internally. This is an important 

deficiency. Similarly, from the Complex System Tool, weight reductions do not seem to 

result in reduction in engine displacement.

(3) Results. Please comment on the validity and applicability of the results to the light-duty 

vehicle fleet in the 2020-2025 timeframe. Please comment on any apparent unstated or 

implicit assumptions that may affect the results, and on any related caveats or 

limitations. 

Performance calculations tied to the FTP, HWFET, and US06 test cycles do not adequately 

capture vehicle behavior under real-world operation. Therefore, technologies that address 

improving fuel economy under real-world operation are either excluded or their contribution not 

included. The application of a 20% reduction in fuel economy to the FTP75 bag 1 portion of the 

drive cycle for 2010 baseline vehicles and 10% for 2020-2025 is crude, arbitrary, and treats only 

one of many problems with the driving simulation in the test cycles. Test cycle difficulties carry 

over into the simulation of hybrid control strategies. 
4 

It is conceivable that BEVs and PHEVs (and less likely FCEVS) will be a significant part of the 

2020-2025 vehicle fleet. That they are excluded from the model is a deficiency. 

(4) Completeness. Please comment on whether the report adequately describes the entire 

process used in the modeling work from input selection to results.

The selection of drivetrain technologies (other than the electric storage technologies) is 

comprehensive. The qualitative description of the drivetrain technologies is complete and clear, 

but quantitative performance data are missing. Transparency in the actual performance data is 

entirely lacking. This includes engine performance maps, shift strategies, battery management in 

hybrids, and more. That much of that data is proprietary to the companies that generated it and/or 

to Ricardo is a problem for what is proposed as a regulatory tool. 

The assumptions are difficult to extract from the text. 

(5) Recommendations. Please comment on the overall adequacy of the report for predicting 

the effectiveness of these technologies, and on any improvements that might reasonably 

be adopted by the authors for improvement. Please note that the authors intend the 

report to be open to the community and transparent in the assumptions made and the 

methods of simulation. Therefore recommendations for clearly defined improvements 

that would utilize publicly available information would be preferred over those that 

would make use of proprietary information.

The failure to model the drivetrain-weight interactions is a major shortcoming. Appendix 2 

should clearly state that vehicle weights are held constant (assuming that I am correct in that 

assumption). 

There should be a table describing the baseline vehicles. 

Summarizing assumptions in tabular form would be a great assistance to the reader. 

The design space should be expanded to include performance parameters, such as power/weight 

or 0-60 times. 

(6) Other comments. Please provide your comments on report topics not otherwise captured 

by the aforementioned charge questions.

The conclusions, Section 11, are a reasonable summary of the work conducted. 

Including the membership of the advisory committee would be appropriate. 
W. R. Wade

5/15/2011 

1

Peer review of the report, “Computer Simulation of Light-Duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe” 

Report by: Wallace R. Wade 

Date of Report: May 15, 2011

Charge to Peer Reviewers:

As EPA and NHTSA develop programs to reduce greenhouse gas (GHG) emissions and 

increase fuel economy of light-duty highway vehicles, there is a need to evaluate the 

effectiveness of technologies necessary to bring about such improvements. Some potential 

technology paths that manufacturers might pursue to meet future standards may include 

advanced engines, hybrid electric systems, mass reduction, along with additional road load 

reductions and accessory improvements. 

 Ricardo Inc. has developed simulation models including many of these technologies with 

the inputs, modeling techniques, and results described in the Ricardo Inc. document that you 

have been provided dated March 10, 2011 (version received was dated April 6, 2011). 

 EPA is seeking the reviewers' expert opinion on the inputs, methodologies, and results 

described in this document and their applicability in the 2020-2025 timeframe. The Ricardo Inc. 

report is provided for review. We ask that each reviewer comment on all aspects of the Ricardo 

Inc. report. Findings of this peer review may be used toward validation and improvement of the 

report and to inform EPA and NHTSA staff on potential use of the report for predicting the 

effectiveness of these technologies. No independent data analysis will be required for this 

review. 

Reviewers are asked to orient their comments toward the five (5) general areas listed 

below. Reviewers are expected to identify additional topics or depart from these general areas as 

necessary to best apply their particular set of expertise toward review of the report.

Comments should be sufficiently clear and detailed to allow readers familiar with the 

report to thoroughly understand their relevance to the material provided for review. EPA 

requests that the reviewers not release the peer review materials or their comments until Ricardo 

Inc. makes its report and supporting documentation public. EPA will notify the reviewers when 

this occurs. 

Below you will find a template for your comments. You are encouraged to use this 

template to facilitate the compilation of the peer review comments, but do not feel constrained by 

the format. You are free to revise as needed; this is just a starting point. 

 If a reviewer has questions about what is required in order to complete this review or 

needs additional background material, please contact Susan Blaine at ICF International 

(SBlaine@icfi.com or 703-225-2471). If a reviewer has any questions about the EPA peer review 

process itself, please contact Ms. Ruth Schenk in EPA’s Quality Office, National Vehicle and 

Fuel Emissions Laboratory by phone (734-214-4017) or through e-mail (schenk.ruth@epa.gov).
W. R. Wade

5/15/2011 

2

Charge Questions: 

(1) Inputs and Parameters. Please comment on the adequacy of numerical inputs to the model 

as represented by default values, fixed values, and user-specifiable parameters. Examples might 

include: engine technology selection, battery SOC swing, accessory load assumptions, etc.) 

Please comment on any caveats or limitations that these inputs and parameters would affect the 

final results.

A. Baseline vehicle subsystem models/maps

- The development of baseline vehicle models with comparison of the model 

results to available 2010 EPA fuel economy test data was appropriate.

- The models/maps for the subsystems used in these vehicle models 

were not provided in the report so that their adequacy could not be 

assessed. 

- Including these baseline models in the report would assist in assessing 

the development process as well as the adequacy of the new technology 

subsystem models/maps, which was not possible in this peer review.

Recommendation: Since the baseline vehicles modeled were 2010 

production vehicles, the models/maps for the subsystems used in 

these vehicle models should be included in the report before it is 

released.

- A major omission was that a baseline model of a hybrid vehicle, which is 

significantly more complex than the baseline vehicle, was not developed and 

compared to available EPA fuel economy test data for production hybrid vehicles. 

Recommendation: A baseline model of a hybrid vehicle should be 

developed and compared to 2010 EPA fuel economy test data for 

production hybrid vehicles.

B. Engine technology selection

- The engine technologies selected for this study, listed in Table 5.1 (page 22), 

are appropriate, but are not all-inclusive of possible future engine technologies.

- Setting the minimum per-cylinder volume at 0.225L and the minimum 

number of cylinders at 3 is appropriate. However, achieving customer 

acceptable NVH with 3 cylinder engines continues to be problematic.

Issue: The description of the derivation of all of the engine 

models/maps was insufficient.
W. R. Wade

5/15/2011 

3

Issue: The technology “package definitions” precluded an 

examination of the individual effects of a variety of technologies 

such as a single stage turbocharger vs. series-sequential 

turbochargers. 

Issue: There are many engine technologies that have potential for 

reduced GHG emissions that were not included in this study, such 

as:

 Single stage turbocharged engines 

 Diesel hybrids

 Biofueled spark ignition and diesel engines

 Natural gas fueled engines

 Other alternative fuel engines

 Charge depleting PHEV and EV 

- The feasibility of the following assumptions for the engines modeled should be 

re-examined as indicated below.

- None of the Stoichiometric DI Turbo engines listed as references by 

Ricardo limited the turbine inlet temperature to a value as low as the 950C 

limit in the Ricardo model (Ref 1, 2, 3). Reducing the turbine inlet 

temperature to reach this limit is expected to result in BMEP levels below 

the assumed 25-30 bar level in the model (which were obtained in the 

referenced engine with a turbine inlet temperature of 1025C).

- Turbocharger delays of the magnitude assumed in the model will result 

in significant driveability issues for engines that are downsized 

approximately 50%. Although Ricardo assumed a turbocharger delay of 

approximately 1.5 seconds, the comparable delay published for a 

research engine was significantly longer at 2.5 seconds (Ref 3). 

Transmission technology selection

- The transmission technologies selected for this study, listed in Table 5.3 (page 

23) are appropriate.

- The forecast that current 4-6 speed automatic transmissions will have 7-

8 speeds by 2020-2025 is appropriate for all except the smallest and/or 

low cost vehicles (page 19).

- The report mentions that the transmissions include dry sump, improved 

component efficiency, improved kinematic design, super finish, and 

advanced driveline lubricants (page 22).

Recommendation: The detailed assumptions showing how the 

benefits of dry sump, improved component efficiency, improved 
W. R. Wade

5/15/2011 

4

kinematic design, super finish, and advanced driveline lubricants 

were added to the transmission maps should be added to the report 

before it is released.

C. Hybrid technology selection

- The hybrid technologies selected for this study, listed in Table 5.2 (page 22) 

are appropriate.

Issue: The adequacy of the P2 Parallel and PS Power Split Hybrid 

systems cannot be determined without having, at a minimum, 

schematics and operational characteristics of the each system 

together with comparisons with today’s hybrid systems.

- Although not contained in the report, the teleconference call with Jeff Cherry 

(EPA) on May 5, 2011 revealed that 90% of the deceleration kinetic energy 

would be recovered.

Kinetic energy recovery is limited by the following:

 Maintaining high generator efficiency over the range of speeds and 

resistive torques encountered during deceleration

 Limitations on the rate at which energy can be stored in the battery

 Losses in the power electronics

 Some energy is lost when energy is withdrawn from the battery for 

delivery to the motor.

 Inefficiencies in the motor at the speeds and torques required.

The inefficiencies of each of these four subsystems are in series and are 

compounded. If each subsystem had 90% efficiency, the kinetic energy 

recovery efficiency would be only 66%. 

Issue: Capturing 90% of the deceleration kinetic energy is a 

significantly goal. The technology to be used to achieve this goal 

needs to be explained and appropriate references added to the 

report. 

D. Actual models/maps for subsystems (engine, transmission, hybrid system, 

accessories, final drive, tires and vehicle)

- None of the subsystem models/maps were provided for review so comments 

on their adequacy are not possible.

Issue: Insufficient reasons are presented to justify why the 

models/maps for subsystems are not provided in the report, 

especially when one of the goals of the report was to provide 

transparency (per Jeff Cherry, May 5, 2011 teleconference and Item 5, 

below).
W. R. Wade

5/15/2011 

5

Recommendation: Subsystem models/map should be added to this 

report and another peer review conducted to assess their adequacy 

before this report is released.

Recommendation: To establish the adequacy of the subsystem 

models/maps, derivation details should be provided.

E. Accessory load assumptions

- The accessory selections listed in Table 5-2 (page 22) appear to be adequate 

except for the following issue:

Issue: Belt driven air conditioning for the stop-start powertrain 

configuration is not acceptable for driver comfort. Electrically driven 

air conditioning is required for the stop-start powertrain 

configuration to provide driver comfort for extended idle periods.

– Input values 

- Alternator efficiency was increased from the current level of 55% to 70% 

to reflect “an improved efficiency design” (page 26 and 27). 

Comment: Justification for the increase in alternator efficiency from 

55% to 70% should be added to the report with references provided. 

Alternator efficiency as a function of speed and load may be more 

appropriate than a constant value.

 

- Accessory power requirements were not provided, such as shown in Figure 3-3 

of Reference 4, for example.

Recommendation: Both mechanically driven and electrically driven 

accessory power requirements should be clearly provided in the 

report. 

F. Battery SOC swing and SOC

- Although not contained in the report, an email from Jeff Cherry (EPA) on May 

5, 2011 revealed that the SOC swing was 30% SOC to 70% SOC or 40% total, 

which appears to be appropriate.

- Achieving neutral SOC (neither net accumulation or depletion) for hybrid 

vehicle simulations is appropriate (page 30).
W. R. Wade

5/15/2011 

6

G. DOE ranges

- The following DOE ranges for Baseline and Conventional Stop-Start (page 23) 

appear to be appropriate, with the exception of Engine Displacement, as 

discussed below.

Parameter

Engine Displacement 50 125

Final Drive Ratio 75 125

Rolling Resistance 70 100

Aerodynamic Drag 70 100

Mass 60 120

DoE Range (%)

Since the default for the Stoichiometric DI Turbo engine appears to be greater 

than 50% reduction in displacement (Standard Car baseline of 2.4L is reduced to 

1.04L for the Stoichiometric DI Turbo (page 46)), the opportunity should be 

provided to start with a displacment near the baseline engine (2.4L) and 

progressively decrease it to approximatly 50% (1.04L). This would require an 

Engine Displacement upper range of over 200%. The model should also have 

the capabilty of increasing the boost pressure as the displacement is reduced. 

- The following DOE ranges for P2 and PS hybrid vehicles (page 24) appear to 

be appropriate:

Engine Displacement 50 150 50 125

Final Drive Ratio 75 125 75 125

Rolling Resistance 70 100 70 100

Aerodynamic Drag 70 100 70 100

Mass 60 120 60 120

Electric Machine Size 50 300 50 150

P2 Hybrid Powersplit

DoE Range (%)

Parameter

H. Other inputs

- The Design Space Query within the Data Visualization Tool allows the user to 

set a continuous range of variables within the design space range. Although this 

capability is useful for parametric studies, the following risks are incurred with 

some of the variables.

- The sliders for “Eng. Eff” and “Driveline Eff.” would allow the user to 

arbitrarily change engine efficiency or driveline efficiency uniformly over 

the map without having a technical basis for such changes.

- The slider for weight would allow the user to add hybrid or diesel 

engines with signficant weight increases without incurring any vehicle 

weight increase. 
W. R. Wade

5/15/2011 

7

Recommendation: A default weight increase/decrease should be 

added for each technology. If weight reductions are to be studied, 

then the user should have to input a specific design change, with the 

appropriate weight reduction built into the model, rather that having 

an arbitrary slider for weight.
W. R. Wade

5/15/2011 

8

(2) Simulation methodology. Please comment on the validity and applicability of the 

methodologies used in simulating these technologies with respect to the entire vehicle. Please 

comment on any apparent unstated or implicit assumptions and related caveats or limitations. 

Does the model handle synergistic affects of applying various technologies together? 

Concern: Methodologies used in simulating the subsystems and the overall 

vehicles were not provided, so that the validity and applicability of these 

methodologies cannot be assessed.

A. Major deficiencies in the report 

- An overall schematic and description of the powertrain and vehicle models and 

the associated subsystem models/maps were not provided. Only vague 

descriptions were included in the text of the report.

- Technical descriptions of how the subsystems and vehicle models/maps for the 

baseline vehicles were developed were not provided.

- Most importantly, only non-technical descriptions of how each of the advanced 

technology subsystem models/maps was developed were provided.

- Descriptions of the algorithms used for engine control, transmission control, 

hybrid system control, and accessory control were not provided. 

- Descriptions of how synergistic effects were handled were not provided.

B. Baseline vehicle model validation results

Ricardo developed baseline vehicle simulations for 2010 vehicles for which EPA fuel 

economy data were available (page 30). “For the 2010 baseline vehicles, the engine 

fueling maps and related parameters were developed for each specific baseline 

exemplar vehicle.” (page 25). Even though these are production vehicles, the models 

and maps used were not described (including whether they were derived from actual 

measurements or models) and they were not provided in the report so that their 

appropriateness could not be assessed.

Table 7.1 shows the calculated vs. EPA test data for the baseline vehicle fuel economy 

performance. This table should include percentage variation of the model calculations 

vs. the test data. The agreement of the model with the test data is within 11%, but this 

is a larger error than some of the incremental changes shown in Appendix 3. A closer 

agreement would have been expected.

Recommendation: A closer examination of the reasons for the up to 11% 

discrepancies between the models and baseline vehicles’ EPA fuel 

economy test data should be undertaken so that the models could be 

refined to provide better agreement.
W. R. Wade

5/15/2011 

9

C. Transmission optimization

A transmission shift optimization strategy is presented in the report and the results are 

shown in Figure 6.1 (page 28). This figure shows very frequent shifting, especially for 

4th, 5th and 6th gears. 

Issue: Optimized shift strategies of the type used by Ricardo have been 

previously evaluated and found to provide customer complaints of “shift 

busyness”. Customers are likely to reject such a shift strategy. 

D. Vehicle model issues

Although the report described the major powertrain subsystems included in the vehicle 

models (page 24), a description of the vehicle model was not provided. 

Issue: A description of how aerodynamic losses, tire rolling losses and 

weight are handled in the model was not provided.

E. Additional discussion of deficiencies is contained in Section 6, Other Comments.
W. R. Wade

5/15/2011 

10

(3) Results. Please comment on the validity and applicability of the results to the light-duty 

vehicle fleet in the 2020-2025 timeframe. Please comment on any apparent unstated or implicit 

assumptions that may affect the results, and on any related caveats or limitations.

A. Overview of results

The results from this work could be useful in evaluating possible GHG emission 

reductions in the 2020-2025 timeframe if the issues throughout this peer review were 

addressed and the recommendations in Item 5 (below) were implemented. However, 

even if the foregoing deficiencies were resolved, the foregoing caveat that there are 

numerous technologies that have potential for reduced GHG emissions that were not 

included in this study must be recognized (see Item 1B, above).

B. Sample runs of CSM

In the review process, several sample runs of the Complex Systems Model (CSM) for 

the Standard Car (Toyota Camry) were made and the results are shown in the attached 

chart (at the end of this peer review) and summarized below.

- Baseline engine with AT6-2010 to Stoichiometric DI Turbo, Stop-Start, AT8-2020

 38.7% improvement in M-H mpg

 Reference 3 identified a 25-30% improvement in mpg for a 50% downsized, 

DI, Turbo engine.

 The remaining 9-14% potentially could be explained by stop-start and the 

change from AT6-2010 to AT8-2020 (although the details of the systems and 

the models used would be needed to make this assessment).

- AT8-2020 to DCT

 3.3% improvement in M-H mpg

 This improvement appears reasonable.

- Stoichiometric DI Turbo with Stop-Start to P2 Hybrid

 18.2% improvement in M-H mpg

 This improvement appears reasonable.

- Stoichiometric DI Turbo with Stop-Start to PS Hybrid

 11.1% improvement in M-H mpg

 A detailed explanation of the differences in the improvements between the P2 

and PS hybrids should be provided in the report, especially considering that 

the P2 hybrid has better fuel economy and uses a 70% smaller electric motor 

(24 vs. 80 kW).

- Stoichiometric DI Turbo PS Hybrid to Naturally Aspirated Atkinson CPS Hybrid

 Loss of 2.3% M-H mpg (From Stoichiometric DI Turbo PS Hybrid)
W. R. Wade

5/15/2011 

11

 The details of the Naturally Aspirated Atkinson CPS Hybrid should be 

provided to explain the nearly equal fuel economy to the Stoichiometric DI 

Turbo PS Hybrid.

- Stoichiometric DI Turbo PS Hybrid to Naturally Aspirated Atkinson DVA Hybrid

 2.1% M-H mpg improvement in M-H mpg (From Stoichiometric DI Turbo PS 

Hybrid)

 The details of the Naturally Aspirated Atkinson DVA Hybrid should be 

provided to explain the nearly equal fuel economy to the Stoichiometric DI 

Turbo PS Hybrid

C. Issue with CSM

Issue: The technology “package definitions” (page 22 and 23) precluded 

an examination of the individual effects of a variety of technologies. 

Some examples where the model did not allow a build up of comparison cases 

are:

 Baseline engine with AT-2010 to AT-2020 to DCT

 Baseline engine without stop-start to with/stop-start

D. Other issues:

 The Advanced Diesel does not appear to be modeled for the Standard Car and 

Small MPV (page 46 and 47), yet no reason was provided.

 The P2 and PS hybrid system was not modeled for the LHDT (page 47), yet no 

reason was provided.

 When the baseline cases were run in the Complex Systems Model, incorrect values 

of displacement and architecture were shown in the output. 

o As an example shown on the attached chart (copied from the output of the 

CSM), the baseline for the Standard Car with a 2.4L engine shows a 

displacement of 1.04L. 

o For the same example, the architecture is shown as “conventional SS”, 

whereas the baseline was understood to not have the stop-start feature (page 

22, Table 5-2).
W. R. Wade

5/15/2011 

12

(4) Completeness. Please comment on whether the report adequately describes the entire 

process used in the modeling work from input selection to results. 

Concern: This report has significant deficiencies in its description of the entire 

process used in the modeling work. Many of these deficiencies have been 

previously discussed, but are listed here for completeness.

 An overall schematic and description of the powertrain and vehicle models and the 

associated subsystem models/maps were not provided. Only vague descriptions 

were included in the text of the report.

 Technical descriptions of how the subsystems and vehicle models/maps for the 

baseline vehicles were developed were not provided. 

 None of the overall or subsystem models/maps were provided for review so 

comments on their adequacy are not possible.

 Most importantly, only minimal descriptions were provided of how each of the 

advanced technology subsystem models/maps was developed.

 Descriptions of the algorithms used for engine control, transmission control, hybrid 

system control, and accessory control were not provided. 

 Descriptions of how synergistic effects were handled were not provided.

There are many engine technologies that have potential for reduced GHG 

emissions that were not included in this study, such as:

 Single stage turbocharged engines 

 Diesel hybrids

 Biofueled spark ignition and diesel engines

 Natural gas fueled engines

 Other alternative fuel engines

 Charge depleting PHEV and EV 

Additional discussion of completeness is contained in Item 6, Other Comments.

 
W. R. Wade

5/15/2011 

13

(5) Recommendations. Please comment on the overall adequacy of the report for predicting the 

effectiveness of these technologies, and on any improvements that might reasonably be adopted 

by the authors for improvement. Please note that the authors intend the report to be open to the 

community and transparent in the assumptions made and the methods of simulation. Therefore 

recommendations for clearly defined improvements that would utilize publicly available 

information would be preferred over those that would make use of proprietary information. 

This report needs major enhancements to reach the stated goal of being open and 

transparent in the assumptions made and the methods of simulation. 

Recommendations to rectify the deficiencies in these areas are provided in the previous 

four items. 

A. Overall recommendations

Overall Recommendation: Provide all vehicle and powertrain models/maps and 

subsystem models/maps used in the analysis in the report so that they can be 

critically reviewed.

Overall Recommendation: Expand the technology “package definitions” to 

enable evaluation of the individual effects of a variety of technologies. 

B. Specific recommendations for improvements

1. Provide an overall schematic and description of the powertrain and vehicle models.

a. Show all of the subsystem models/maps used in the overall model. 

b. Show the format of the information in each of the subsystem models 

(including input, subsystem model, output). 

 

2. Provide technical descriptions of how the subsystems and vehicle models/maps for 

the baseline vehicles were developed.

 

3. Provide overall system and subsystem models/maps in the report.

4. Provide detailed technical descriptions of how each of the advanced technology 

subsystem models/maps was developed.

5. Provide descriptions of the algorithms used for engine control, transmission control, 

hybrid system control, and accessory control. 

6. Provide detailed descriptions of how synergistic effects were handled. 
W. R. Wade

5/15/2011 

14

C. Additional recommendations shown in bold print throughout other sections of this 

report are repeated below for completeness (in the order that they appear in the report).

Recommendation: Since the baseline vehicles modeled were 2010 production 

vehicles, the models/maps for the subsystems used in these vehicle models 

should be included in the report before it is released.

Recommendation: A baseline model of a hybrid vehicle should be developed and 

compared to 2010 EPA fuel economy test data for production hybrid vehicles.

Recommendation: The detailed assumptions showing how the benefits of dry 

sump, improved component efficiency, improved kinematic design, super finish, 

and advanced driveline lubricants were added to the transmission maps should 

be added to the report before it is released.

Recommendation: Subsystem models/map should be added to this report and 

another peer review conducted to assess their adequacy before this report is 

released.

Recommendation: To establish the adequacy of the subsystem models/maps, 

derivation details should be provided.

Recommendation: Both mechanically driven and electrically driven accessory 

power requirements should be clearly provided in the report. 

Recommendation: A default weight increase/decrease should be added for each 

technology. If weight reductions are to be studied, then the user should have to 

input a specific design change, with the appropriate weight reduction built into 

the model, rather that having an arbitrary slider for weight.

Recommendation: A closer examination of the reasons for the up to 11% 

discrepancies between the models and baseline vehicles’ fuel economy test data 

should be undertaken so that the models could be refined to provide better 

agreement.

D. There are numerous “Issues” identified throughout this peer review that need to be 

addressed with specific resolution actions. 
W. R. Wade

5/15/2011 

15

(6) Other comments. Please provide your comments on report topics not otherwise captured by 

the aforementioned charge questions. 

Overview

The vehicle model and powertrain model were developed and implemented by Ricardo 

in the MSC.Easy5 software package. The model reacts to driver input to provide the 

torque levels and wheel speeds required to drive a specified vehicle over specified 

driving cycles. The overall model consists of subsystem models that determine key 

component outputs such as torque, speeds, heat rejection, and efficiencies. Subsystem 

models are expected to be required for the engine, accessories, transmission, hybrid 

system (if included), final drive, tires and vehicle, although the report did not clearly 

specify the individual subsystem models used.

A design of experiments (DOE) matrix was constructed and the vehicle models were 

used to generate selected performance, fuel economy and GHG emission results over 

the design space of the DOE matrix. Response surface modeling (RSM) was 

generated in the form of neural networks. The output from each model simulation run 

was used to develop the main output factors used in the fit of the RSM. The resulting 

Complex Systems Model (CSM) provides a useful tool for viewing the results from this 

analysis that included over 350,000 individual vehicle simulation cases.

Overall Issue:

The vehicle and powertrain models/maps and subsystem models/maps used in 

the analysis were not provided in the report and could not be reviewed. In most 

cases, the report stated that the models/maps were either proprietary to Ricardo 

or at least elements were proprietary so that they could not be provided for 

review. Without having these models/maps and subsystem models/maps, their 

adequacy and suitability cannot be assessed.

Overall Recommendation: Provide all vehicle and powertrain models/maps and 

subsystem models/maps used in the analysis in the report so that they can be 

critically reviewed.

Overall Issue:

The technology “package definitions” preclude an examination of the individual 

effects of a variety of technologies. For example, for the Stoichiometric DI Turbo 

engine, only the version with a series-sequential turbocharger could be evaluated 

whereas a lower cost alternative with a single turbocharger could not be 

evaluated. Likewise, only the AT8-2020 transmission could be evaluated with the 

Stoichiometric DI Turbo engine, while the substitution of the AT6-2010, as a lower 

cost alternative, could not be evaluated. 
W. R. Wade

5/15/2011 

16

Overall Recommendation: Expand the technology “package definitions” to 

enable evaluation of the individual effects of a variety of technologies. 

This section provides additional details regarding the overall issues and comments 

made in the foregoing five items.

Engine Models

Engine models provided the torque curve, fueling map and other input parameters 

(which were not specified in the report) (page 25). Since the report stated that “The 

fueling maps and other engine model parameters used in the study were based on 

published data and Ricardo proprietary data” (page 26), their adequacy and suitability 

could not be assessed.

The report states that engines used in the model were developed using two main 

methods (page 14). 

1. The first method assumed that “reported performance of current research 

engines” would closely resemble production engines of the 2020-2025 

timeframe. 

2. The second method began with current production engines and then a “pathway 

of technology improvements over the new 10-15 years that would lead to an 

appropriate engine configuration for the 2020-2025 timeframe” was applied.

Both of these approaches are reasonable if: 

1. appropriate references are provided, 

2. the reported performances for the research engines used are documented in the 

report, 

3. the technology improvements are documented in the report, and 

4. the methodology of incorporating the improvements is fully documented. 

The description of the derivation of the engine models in the report was, at best, vague, 

as illustrated by the two examples below:

Example 1: Stoichiometric DI Turbo

The current research engines of this configuration were reported to be the Sabre engine 

developed by Lotus and the downsized concept engine developed by Mahle. Since the 

engine modeled in the Ricardo report had a peak BMEP of 25-30 bar and used seriessequential turbochargers, the Sabre engine is not applicable since it only had a peak 

BMEP of 20 bar and used a single stage turbocharger (Refs 1 and 2). 

On the other hand, the Mahle engine appeared to be directly applicable, since it had a 

peak BMEP of 30 bar and used series-sequential turbocharging (Ref 3). Since 

Reference 3 provided the BSFC map for this engine, shown below, it is not clear why 

the Ricardo report could not have shown this map, or a map derived from this one, and 

then described how it was derived and/or combined with other maps to provide the 

model used in the report.
W. R. Wade

5/15/2011 

17

Example 2: Advanced Diesel

For the advanced diesel, the report provided the following description: “...the LHDT 

engine torque curve and fueling maps were generated by starting with a 6.6L diesel 

engine typical for this class and applying the benefits of improvements in pumping 

losses or friction to the fueling map”. No description of the improvements in pumping 

losses or friction reduction was provided and the variation of these improvements over 

the speed and load map were not provided. In addition, the baseline 6.6L engine map 

was not provided, the 6.6L friction map was not provided and the methodology for 

applying the improvements to the 6.6L engine map was not provided.

The report should explain whether the engine model is only a map of BSFC vs. speed 

and load, or if the engine model includes details of the turbocharger, valve timing, and 

control algorithms for parameters such as air/fuel ratio, spark/injection timing, EGR rate, 

boost pressure, and valve timing.

Advanced valvetrains were included in many of the advanced engines (page 12). 

However, the method for applying these advanced valvetrains to the engine maps was 

not provided. Also, no description of the control strategy for these valvetrains was 

provided. The report did not provide a description of how the reduction of pumping 

losses with an advanced valvetrain was applied to a downsized engine that already had 

reduced pumping losses. Therefore, no assessment of how the model handled 

synergies could be made.

In summary, the Ricardo report provided insufficient descriptions of the derivation of the 

maps used for all of the engines in this study, which included:

 Baseline

 Stoichiometric DI Turbo

 Lean-Stoichiometric Switching

 EGR DI Turbo

 Atkinson Cycle

 Advanced Diesel
W. R. Wade

5/15/2011 

18

Transmission Models

Similar to engine models, the description of the derivation of transmission models was 

also vague. Using the automatic transmission model as an example, “For the 2020-

2025 timeframe, losses in automatic transmissions are expected to be about 20-33% 

lower than in current automatic transmissions from the specific technologies described 

below.” The specific technologies that could provide these reductions appeared to 

include:

 Shift clutch technology - to improve thermal capacity of the shifting clutch to 

reduce plate count and lower clutch losses during shifting.

 Improved kinematic design – no description of these improvements was 

provided.

 Dry sump – to reduce windage and churning losses.

 Efficient components – improvements in seals, bearings and clutches to reduce 

drag.

 Super finishing - improvements expected were not specified.

 Lubrication- new developments in base oils and additive packages, but 

improvements were not specified.

In addition to not specifying the improvements expected from these technologies, no 

indication was provided of how these technologies were applied to the transmission 

models. For example, 

 The report stated that losses in automatic transmissions are expected to be 

about 20-33% lower than in current automatic transmissions (page 19). 

However, the baseline losses were not provided for reference and the means to 

achieve these reductions were not described.

 The report stated that energy losses in DCTs are expected to be 40-50% lower 

than in current automatic transmissions (page 19). The details of this reduction 

were not provided and references describing these reductions were not provided.

 Bearing and seal losses have a greater effect on efficiency at light loads than at 

heavy loads. The report did not describe how these losses were incorporated in 

the model. In contrast to the lack of descriptions of details in the report, 

Reference 4, as an example, provided the following map of bearing losses in a 

transmission as a function of shaft diameter and speed. Similar details for the 

relevant aspects of the transmission models in this report would have been 

expected.

 
W. R. Wade

5/15/2011 

19

In summary, the Ricardo report provided insufficient descriptions of the derivation of the 

maps for the following transmissions:

 Advanced automatic

 Dry clutch DCT

 Wet clutch DCT

 P2 Parallel hybrid transmission

 PS Power Split hybrid transmission

In addition, the models for the automatic transmissions of the baseline vehicles were not 

provided, so that their adequacy could not be assessed.

Hybrid Technologies Models

Key elements of a hybrid system include: electric machines (motor-generator), power 

electronics, and a high-voltage battery. Only the following vague description of the 

models for these subsystems was provided: “For each of these systems, current state 

of the art technologies were adapted to an advanced 2020-2025 version of the systems, 

such as by lowering internal resistance in the battery pack to represent 2010 

chemistries under development and decreasing losses in the electric machine and 

power electronics to represent continued improvements in technology and 

implementation” (page 29). This vague description did not provide adequate details to

assess the adequacy of these models. For example, specific values for internal 

resistance with references should be provided together with an illustration of how this 

was incorporated in the model of the battery.

In contrast, as an example, Reference 6 provided a detailed motor efficiency map, 

shown below, as well as efficiency maps of other key components of the Prius hybrid 

vehicle. Similar maps for all hybrid subsystems would be expected in this report.

In addition, “a Ricardo proprietary methodology was used to identify the best possible 

fuel consumption for a given hybrid powertrain configuration over the drive cycles of 

interest.” (page 29), which precluded an assessment of its suitability.

No mention was provided of how the cooling system for the hybrid system was 

modeled.
W. R. Wade

5/15/2011 

20

Accessory Models

None of the accessory models were not provided for review, so their adequacy and 

suitability cannot be assessed. 

The accessory loads vs. engine speed for the conventional belt driven accessories were 

apparently removed from the engine when electric accessories were applied. However, 

the conventional accessory loads as well as the alternator loads/battery loads for the 

electric accessories were not provided.

In contrast, as an example, Reference 4 provided the following map of an electric water 

pump and AC compressor drive efficiency. Similar maps for all accessory models 

would be expected in this report.

Boosting Systems

The report states that “various boosting approaches are possible, such as 

superchargers, turbochargers, and electric motor-driven compressors and turbines.” 

(page 13). However, elsewhere the report states “series-sequential turbochargers” will 

be used on the Stoichiometric DI Turbo engine (page 15).

It is not clear in the report how the series-sequential turbocharger was selected from the 

variety of boosting devices that were introduced. Models for the turbochargers with 

compressor and turbine efficiency maps were not provided, so the appropriateness of 

these model cannot be assessed. 

Comment: The model should include a single turbocharger system with 

less extreme downsizing as advocated by the Sabre Engine (References 1 

and 2) as a lower cost alternative to series-sequential turbochargers.
W. R. Wade

5/15/2011 

21

Stoichiometric DI Turbo Engine

The table below compares several attributes of the Ricardo Stoichiometric DI Turbo 

Engine with the Mahle Turbocharged, DI Concept Engine.

Feature

Ricardo 

Stoichiometric DI 

Turbo Engine

Mahle 

Turbocharged, DI 

Concept Engine

SAE 2009-01-1503

Downsizing 57% (for Std Car) 50%

BMEP 25-30 bar 30 bar

Turbo Response 1.5 second time 

constant

2.5 second time 

constant

(estimated from 4 

second total 

response time)

Turbine Inlet 

Temperature

950C 1025C

NEDC fuel 

economy

Not available 25 – 30% better 

that NA baseline

Key content of the Mahle Turbocharged, DI Concept Engine:

- Two turbochargers in series

- Charge air cooler

- Dual variable valve timing

- High energy ignition coils

- Fabricated, sodium cooled valves

- EGR cooler

Reference 3 describing the Mahle concept engine stated that lowest fuel consumption 

that usually occurs around 2000 rpm had moved out to 4000 rpm for the seriessequential turbocharged engine. 

Issue: The Ricardo report did not discuss the concern that the lowest fuel 

consumption in a series-sequential turbocharged engine had moved out to 

4000 rpm, rather than the usual 2000 rpm and did not discuss how this 

concern was handled.

The foregoing table indicates several significant issues:

1. The turbine inlet temperature of the Mahle engine is significantly higher than the limit 

assumed for the Ricardo engine (1025C vs. 950C). Reducing the turbine inlet 

temperature is expected to result in lower BMEP levels where the temperature is 

limited.
W. R. Wade

5/15/2011 

22

2. The turbocharger response time for the Mahle engine is 2.5 seconds, whereas 

Ricardo assumed a time constant of 1.5 seconds. Such turbocharger delays are 

expected to result in significant driveability issues for engines that are downsized 

approximately 50%.

The table below compares several attributes of the Ricardo Stoichiometric DI Turbo 

Engine with the Lotus Sabre Engine.

Feature

Ricardo 

Stoichiometric DI 

Turbo Engine

Lotus Sabre Engine

SAE 2008-01-0138

Downsizing 57% (for Std Car) 32%

BMEP 25 - 30 bar 20.1 bar

Turbine Inlet 

Temperature 

950C 980C

1050C (common) 

and desired

Fuel RON 87 PON

(Pump Octane 

Number)

95 RON

Est 91 PON

The paper on the Sabre engine (Reference 2) indicates that operation at lower turbine 

inlet temperatures results in a reduction in BMEP. However, the turbine inlet 

temperature for the Sabre engine is still 40C above Ricardo’s assumption.

Reference 2 indicates that the Sabre engine with a single stage turbocharger provides 

an attractive alternative to extreme downsizing with series-sequential turbochargers.

Cooled Exhaust Manifold

The Ricardo report states, “The future engine configuration was assumed to use a 

cooled exhaust manifold to keep the turbine inlet temperature below 950C...” No 

explanation was provided of how the limit on turbine inlet temperature would affect 

boost pressure and power.

 

Warm-Up Methodology

“Ricardo used company proprietary data to develop an engine warm-up profile” which 

was used to increase the fueling requirements during the cold start portion of the FTP75 

drive cycle (page 26). Since this data was proprietary, no assessment of its 

appropriateness can be made.

 

Elsewhere the report states, “A bag 1 correction factor is applied to the simulated “hot” 

fuel economy result of the vehicles to approximate warm-up conditions...” The 

correction factor reduces the fuel economy results of the FTP75 bag 1 portion of the 

drive cycle by 20% on the current baseline vehicles and 10% on 2020-2025 vehicles 
W. R. Wade

5/15/2011 

23

that take advantage of fast warm-up technologies” (page 29). No references or data are 

cited to support this significant reduction in correction factor.

Issue: No explanation was provided to clarify when the “engine warm-up 

profile” is used and when the “correction factor” is used. Therefore, the 

appropriateness of the warm-up methodology cannot be assessed.

Lean-Stoichiometric Switching Engine

The report states that this engine will use a lean NOx trap or a urea-based SCR system 

(page 15). The use of fuel as a reducing agent was also suggested in the report (page 

16). However, the fuel economy penalty associated with regenerating the NOx trap or 

the reducing agent for the SCR system was not provided.

Engine Scaling

The report states, “The BSFC of the scaled engine map is ...adjusted by a factor that 

accounts for the change in heat loss that comes with decreasing the cylinder volume, 

and thereby increasing the surface to volume ratio for the cylinder” (page 26). This is a 

directionally correct correction. However, specific values for the correction should be 

provided, together with references to the data and methodology used to derive the 

values used.

Issue: The report states, “...downsizing the engine directly scales the 

delivered torque, ...” (page 26). However, since there will be increased heat 

loss from the smaller displacement cylinder, the torque would be expected 

to be less than the directly scaled values for the same fueling rate. 
W. R. Wade

5/15/2011 

24

References (Used for this Review that are also listed in the Report)

References used to establish the basis for the Stoichiometric DI Turbo engine 

assumptions (page 15 of the report):

1. Coltman, et al. (2008), “Project Sabre: A Close-Spaced Direct Injection 3-Cylinder 

Engine with Synergistic Technologies to Achieve Low CO2 Output”, SAE Paper 

2008-01-0138 

2. Turner, et al. (2009), ‘Sabre: A Cost-Effective Engine Technology Combination for 

High Efficiency, High Performance and Low CO2 Emissions”, IMechE conference 

proceedings

3. Lumsden, et al. (2009), “Development of a Turbocharged Direct Injection 

Downsizing Demonstrator Engine”, SAE Paper 2009-01-1503

Reference that summarizes the 2008 study by Perrin Quarles Associates (PQA) that 

provided the 2010 baseline cases for five LDV classes (Page 30 of the report):

4. PQA and Ricardo (2008), "A Study of Potential Effectiveness of Carbon Dioxide 

Reducing Vehicle Technologies"

References containing supporting information for the hybrid powertrains:

5. Hellenbroich, et al. (2009), "FEV's New Parallel Hybrid Transmission with Single Dry 

Clutch and Electric Torque Support"

6. Staunton, et al. (2006), "Evaluation of 2004 Toyota Prius Hybrid Electric Drive 

System", ORNL technical report TM-2006/423
Sample Output From Complex System Model (CSM)

5/4/2011

Relative Percentage Differences Were Added by W. R. Wade

FTP HWFET US06 Combined [0-60 mph Displacement FDR Rolling R. Aero Weight Eng.Eff. Hybrid Class

CONVENTIONAL SS

Base 30.0 43.5 29.1 34.9 8.3 1.04 3.23 0.00822 0.69 3625 1 Standard Car (Toyota Camry)

 (Baseline) - - - - 

Stoich DI Turbo 44.5 54.2 32.5 48.4 8.5 1.04 3.23 0.00822 0.69 3625 1 Standard Car (Toyota Camry)

48.2% 24.6% 11.7% 38.7%

AT8-2020 to DCT 46.3 55.3 33.7 50.0 8.6 1.04 3.23 0.00822 0.69 3625 1 Standard Car (Toyota Camry)

4.21% 1.93% 3.51% 3.28%

HYBRIDS

P2 w/Stoich DI Turbo 61.6 56.3 36.6 59.1 8.6 0.83 3.23 0.00822 0.69 3625 1 24 Standard Car (Toyota Camry)

 (Rel to Conv SS SCT) 32.96% 1.80% 8.89% 18.23%

PS w/Stoich DI Turbo 57.5 53.3 36.4 55.5 9.2 0.83 3.23 0.00822 0.69 3625 1 80 Standard Car (Toyota Camry)

 (Rel to Conv SS DCT) 24.00% -3.50% 8.24% 11.11%

PS w/Atkinson CPS 55.1 53.2 38.1 54.3 8.5 2.4 3.23 0.00822 0.69 3625 1 80 Standard Car (Toyota Camry)

 (Rel to Stoich DI Turbo) -4.08% -0.18% 4.61% -2.29%

PS w/Atkinson DVA 58.3 54.8 38.7 56.7 8.5 2.4 3.23 0.00822 0.69 3625 1 80 Standard Car (Toyota Camry)

 (Rel to Stoich DI Turbo) 1.5% 2.7% 6.1% 2.1%
C-1

Appendix D. Peer Reviewer Comments as Submitted, Round 2
Peer review of the report, “Computer Simulation of Light-Duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe” 

Report by:Scott McBroom

Date of Report: 8/17/1

Charge to Peer Reviewers:

As EPA and NHTSA develop programs to reduce greenhouse gas (GHG) emissions and 

increase fuel economy of light-duty highway vehicles, there is a need to evaluate the 

effectiveness of technologies necessary to bring about such improvements. Some potential 

technology paths that manufacturers might pursue to meet future standards may include 

advanced engines, hybrid electric systems, mass reduction, along with additional road load 

reductions and accessory improvements. 

 Ricardo Inc. has developed simulation models including many of these technologies with 

the inputs, modeling techniques, and results described in the Ricardo Inc. document that you 

have been provided dated March 10, 2011. 

 EPA is seeking the reviewers' expert opinion on the inputs, methodologies, and results 

described in this document and their applicability in the 2020-2025 timeframe. The Ricardo Inc. 

report is provided for review. We ask that each reviewer comment on all aspects of the Ricardo 

Inc. report. Findings of this peer review may be used toward validation and improvement of the 

report and to inform EPA and NHTSA staff on potential use of the report for predicting the 

effectiveness of these technologies. No independent data analysis will be required for this 

review. 

Reviewers are asked to orient their comments toward the five (5) general areas listed 

below. Reviewers are expected to identify additional topics or depart from these general areas as 

necessary to best apply their particular set of expertise toward review of the report.

Comments should be sufficiently clear and detailed to allow readers familiar with the 

report to thoroughly understand their relevance to the material provided for review. EPA 

requests that the reviewers not release the peer review materials or their comments until Ricardo 

Inc. makes its report and supporting documentation public. EPA will notify the reviewers when 

this occurs. 

Below you will find a template for your comments. You are encouraged to use this 

template to facilitate the compilation of the peer review comments, but do not feel constrained by 

the format. You are free to revise as needed; this is just a starting point. 

 If a reviewer has questions about what is required in order to complete this review or 

needs additional background material, please contact Susan Blaine at ICF International 

(SBlaine@icfi.com or 703-225-2471). If a reviewer has any questions about the EPA peer review 

process itself, please contact Ms. Ruth Schenk in EPA’s Quality Office, National Vehicle and 

Fuel Emissions Laboratory by phone (734-214-4017) or through e-mail (schenk.ruth@epa.gov).
Scott McBroom 

2 

Charge Questions: 

(1) Inputs and Parameters. Please comment on the adequacy of numerical inputs to the model 

as represented by default values, fixed values, and user-specifiable parameters. Examples might 

include: engine technology selection, battery SOC swing, accessory load assumptions, etc.) 

Please comment on any caveats or limitations that these inputs and parameters would affect the 

final results.

Battery Model: Overall the battery model is sound; however, I don’t understand why cold 

modeling is included. The FTP testing doesn’t include cold testing therefore only 25C and up 

should be included and the battery is consistent at those temps.

Engine Model: 

I see data on the HEDGE engine technology but no mention of it in the list of engine 

technologies unless it’s the high EGR DI gasoline engine. 

Engine Model: 

The trend in engine technology is forced induction (engine downsizing). I think the selection of 

turbo only is too limiting. I anticipate variable speed supercharging and other combination of 

forced induction. I think the study would do well to include this. 

Rgen Alternator: 

Ricardo states - 70% efficient alternator; however, alternator efficiency is a function of temp, 

speed and load. 70% is probably the best, but it’s highly unlikely that it will operate there for the 

duration of the conditions. 

Diesel Engine Fuel Maps:

The presentation shows the technologies to be deployed, but doesn’t discuss how the 2020 bsfc 

maps were arrived at. It might be helpful to also use the same method for comparison that the 

authors used to show LBDI vs EGR 

Diesel Technology:

Curious about the author’s comment regarding supercharging, “advances to avoid variable 

speed”. Why not variable speed? 

Curious about why no discussion of advanced materials in engines to achieve improvements. 

EBDI Engine:

Couldn’t find fuel economy benefit discussion in presentation. Should be done as gasoline or 

energy equivalent. I know CO2 is proportional, but.... 

Future Developments in Engine Friction – 

I think it would be worthwhile to point out that there are technologies that are more driven by 

increased durability rather than fuel economy but they could play off one another. Engine 

friction reduction is one of those areas.
Scott McBroom 

3 

(2) Simulation methodology. Please comment on the validity and applicability of the 

methodologies used in simulating these technologies with respect to the entire vehicle. Please 

comment on any apparent unstated or implicit assumptions and related caveats or limitations. 

Does the model handle synergistic affects of applying various technologies together?

Transmission Model: 

Ricardo describes an approach that asserts that using an average efficiency value vs a 3D 

efficiency map yields insignificant differences over the CAFÉ drive cycles, but offers no results 

to validate the claim.

Transmission Model: 

Ricardo offers no discussion of how inertial changes are managed during shifts. This may have 

greatest impact on the shift strategies where the transmission shifts to put the engine at the best 

bsfc for the given load. 

Hybrid:

I don’t see any effort to model motor/inverter temperature effects. One would expect significant 

degradation of motor capability as things heat up during normal operation. 

Regen Alternator:

Alternator model is too simplistic. On average the efficiency is too high as identified and it’s 

unrealistic to assume that the battery will be able to accept 100% of the charge.

EHVA:

The paper addresses the potential of the technology nicely. Since it was published in 2003 has 

any more recent work been done to address the durability and issues brought up in the 

conclusions? 

Accessories:

I don’t see any discussion on the treatment of accessories. I believe from my review of the 

previous material, that the authors assume that all accessories will be electric. I think that engine 

driven accessories will play a key role in 2020.
Scott McBroom 

4 

(3) Results. Please comment on the validity and applicability of the results to the light-duty 

vehicle fleet in the 2020-2025 timeframe. Please comment on any apparent unstated or implicit 

assumptions that may affect the results, and on any related caveats or limitations.

Motor Efficiency Maps 

I am having trouble believing that motor efficiency will stay above 90% once temperature effects 

are accounted for. It also seems to me that these numbers don’t include the inverter even though 

the authors say that it does. The UQM maps seem more reasonable. As stated in a previous 

comment, I believe that the cost reductions needed for motors will drop their efficiencies in the 

future.

After reading the papers and presentations I come to the assumption that the papers were used to 

guide the selection of technology, but it’s not clear which maps were generated from model and 

which maps were generated in the test cell. It’s evident that there is a heavy concentration on 

engine technology and the fidelity of the engine models, which is appropriate. I have a slight 

concern about the impression I’m left with; that there is not much attention to the interaction of 

systems effects. This is most likely because of cost and availability of data. I would like to see 

the EPA articulate a process for looking at system interactions, continuous improvement and 

model compatibility. For example if the study were to run over several years the researches 

should feel confident comparing a result generated with the models in 2013 to modeling results 

generated today.
Scott McBroom 

5 

(4) Completeness. Please comment on whether the report adequately describes the entire 

process used in the modeling work from input selection to results. 

Hybrid: 

Ricardo asserts that electric machine design activities of the future will most like concentrate 

around cost reductions; however I see machine efficiency dropping in order to meet cost 

reductions. Therefore I think it premature to assume that efficiency will stay the same and cost 

will drop. 
Scott McBroom 

6 

(5) Recommendations. Please comment on the overall adequacy of the report for predicting the 

effectiveness of these technologies, and on any improvements that might reasonably be adopted 

by the authors for improvement. Please note that the authors intend the report to be open to the 

community and transparent in the assumptions made and the methods of simulation. Therefore 

recommendations for clearly defined improvements that would utilize publicly available 

information would be preferred over those that would make use of proprietary information. 

(6) Other comments. Please provide your comments on report topics not otherwise captured by

the aforementioned charge questions. 
PEER REVIEW:

Computer Simulation of Light-Duty Vehicle 

Technology for Greenhouse Gas Emission Reduction in 

the 2020-2025 Timeframe

Review Conducted for: 

U.S. EPA

Review Conducted By: 

Shawn Midlam-Mohler

Review Period:

4/28/2011 – 5/16/2011 
Shawn Midlam-Mohler - Peer Review Page 2 

Contents

Executive Summary .................................................................................................................. 3

Simulation Methodology .......................................................................................................... 4

Vehicle Model ....................................................................................................................... 5

Engine Models ...................................................................................................................... 5

Aftertreatment/Emissions Solutions ..................................................................................... 7

Advanced Valvetrains ....................................................................................................... 7

Direct Injection Fuel Systems ........................................................................................... 8

Boosting Systems .............................................................................................................. 8

Engine Downsizing ........................................................................................................... 8

Warm-Up Methodology .................................................................................................... 9

Accessory Models ............................................................................................................. 9

Engine Technology “Stack-Up” ..................................................................................... 10

Baseline Hybrid Models ..................................................................................................... 11

Hybrid Control Strategy .................................................................................................. 11

Electric Traction Components ........................................................................................ 12

HEV Battery Model ........................................................................................................ 12

Transmissions ..................................................................................................................... 13

Data Analysis Tool ................................................................................................................. 13

Conclusions ............................................................................................................................. 14
Shawn Midlam-Mohler - Peer Review Page 3 

Executive Summary

For the purpose of describing the modeling approach used in the forecasting of the 

performance of future technologies, the report reviewed is inadequate. In virtually every area, 

the report lacks sufficient information to answer the charge questions provided for the reviewer. 

It is entirely possible that the approach used is satisfactory for the intended purpose. However, 

given the information provided for the review, it is not possible for this reviewer to make any 

statement regarding the suitability of this approach. Some brief comments on each of the five 

charge questions are provided below: 

Inputs and Parameters – From a high level, it is clear what the inputs to the design space 

tool are, which are listed in tables 8.1 and 8.2. At the next level down (i.e. the vehicle and 

subsystem models) there is no comprehensive handling of inputs in parameters in the report. 

Some models are partially fleshed out in this area but most are lacking. By way of example, the 

engine models are described as maps which are “defined by their torque curve, fueling map, and 

other input parameters” where “other input parameters” are never defined.

Simulation Methodology – The vehicle model is reported as “a complete, physics-based 

vehicle and powertrain system model” - which it is not. The modeling approach used relies 

heavily on maps and empirically determined data which is decidedly not physics-based. This 

nomenclature issue aside, the model is not described in sufficient detail in the report to make an 

assessment in this area. An excellent example of this is the electric traction drives and HEV 

energy storage system for which the report mentions no details, even qualitative ones, on the 

structure of the models. 

Results - The third charge questions deals with the validity and the applicability of the 

resulting prediction. The difficulty in this task is that it is an extrapolation from present 

technology that uses an extrapolation method (i.e. the model) and a set of inputs to the model 

(i.e. future powertrain data.) Since it is not possible to validate the results against vehicles and 

technology that do not exist, one can only ensure that the model and the model inputs are 

appropriate for the task. Because of the lack of transparency in the model and inputs it is 

difficult to make any claims regarding the results. In trying to validate results, one example is 

cited in the body of the report that shows the baseline engine getting superior HWFET and US06 
Shawn Midlam-Mohler - Peer Review Page 4 

fuel economy than all of the other non-HEV powertrains with other factors being the same – this 

leaves some skepticism regarding the results.

Completeness – Based on the above, it is clear that this reviewer feels the report is 

inadequate at describing the entire process of modeling work from input selection to results. 

There was not a single subsystem that was documented at the level desired. It is understood that, 

in some cases, there are things of a proprietary nature that must be concealed. As a trivial 

example, the frontal area of the vehicle classes does not seem to be anywhere in the report or 

data analysis tool. This is one parameter amongst hundreds excluding the real details of the 

models (i.e. equations or block diagrams), methods used to generate engine maps, details on 

control laws, etc. On the topic of proprietary data, there are many ways of obscuring data 

sufficiently that can demonstrate a key point (i.e. simulation accuracy) without compromising 

confidentiality of data – this should not be a major barrier to providing some insight into the 

inner working of the simulator. 

Recommendations – Given the low level of detail given in the report, it does seem that the 

strategy used is consistent with the goal of the work and what others in the field are doing. That 

being said, the report is inadequate in nearly every respect at documenting model inputs, model 

parameters, modeling methodology, and the sources and techniques used to develop the 

technology performance data. Given the need for transparency in this effort, this reviewer feels 

that the detail in the report is wholly inadequate to document the process used. The organization 

responsible for the modeling has expertise in this area it is certainly possible that the 

methodology is sound, however, given just the information in the report there is simply no way 

for an external reviewer to make this conclusion. 

Because of the lack of hard information to answer the charge questions, this peer review 

evolved mainly into a suggested list of details that should be brought forward in order to allow 

the charge questions to be answered properly. With this information, it is hoped that a person 

with expertise in the appropriate areas will be able comment on the work more fully. 

Simulation Methodology

The simulation methodology is generally not described in the report in sufficient detail to 

assess the validity and accuracy of the approach. The models and approach are described 
Shawn Midlam-Mohler - Peer Review Page 5 

qualitatively; however, this is insufficient to truly evaluate the ability of the modeling approach 

to perform the desired function. The following subsections address specific issues with the 

models, inputs, and parameters and suggest possible corrective actions to address these issues.

Vehicle Model

The vehicle model is described as “a complete, physics-based vehicle and powertrain system 

model” developed in the MSC.Easy5TM simulation environment. This description is not 

particularly helpful in defining the type of model as portions of the model are clearly not physics 

based, such as the various empirical maps used or sub-models like the warm-up model which is 

by necessity an empirical model due to the complexity of the warm-up process compared to the 

expected level of fidelity of the model. It is assumed that a standard longitudinal model accounts 

for rolling losses, aero losses, and grade is used to model the forces acting on the vehicle. Input 

parameters for the vehicle model are not described. The baseline vehicle platforms are listed, 

however, the relevant loss coefficients are not provided (rolling resistance, drag coefficient, 

inertia.)

Suggested Corrective Action: 

1. List the dynamic equation describing the longitudinal motion of the vehicle

a. NOT ADDRESSED IN SUPPLEMNTAL MATERIAL REVIEWED

2. List all parameters used for each vehicle class for simulation

a. NOT ADDRESSED IN SUPPLEMNTAL MATERIAL REVIEWED

Engine Models

The engine model is the most important element in successfully modeling the capability 

of future vehicles, since it is the responsible for the largest loss of energy. It is also one of the 

most difficult aspect to predict since it involves many complicated processes (i.e. combustion, 

compressible flow) which must be considered in parallel with emissions compliance (i.e. incylinder formation, catalytic reduction.) Because of this, this sub-model must be viewed with 

extreme scrutiny in order to ensure quality outputs from the model. 

The engine models are “defined by their torque curve, fueling map, and other input 

parameters.” This implies that the maps are static representations of fuel consumption versus 

torque, engine speed, and other unknown input parameters. Generally speaking, representing 

engine performance in this fashion is consistent with typical practice for this class of modeling. 
Shawn Midlam-Mohler - Peer Review Page 6 

This comment deals only with the representation of the engine performance in simulation, the 

generation of the data contained within the map is much more challenging.

The report outlines two methods were used to produce engine models. The first method 

was used for boosted engines and relied upon published data on advanced concept engines which 

would represent production engines in the 2020-2025 timeframe. The second method was used 

with Atkinson and diesel engines and somehow extrapolated from current production engines to 

the 2020-2025 time frame. The description of both of these methods in the report is 

unsatisfactory. It also fails to address how the various technologies are used to build up to a 

single engine map for a specific powertrain. Validation, to the extent possible with future 

technologies, is also lacking in this area. 

This reviewer took some time to look at the data via the tool provided. One table is 

shown in Figure 1 which shows some unexpected results. The results are for a small car with the 

dry clutch transmission and it shows the baseline engine having superior fuel economy over all 

other non-hybrid powertrain options. This is unexpected behavior and, since there is minimal 

transparency in the model, it cannot be investigated any further. 

Figure 1: Simulation Results Different Engines for Small Car with 8Dry_DCT and all other Parameters Constant

Suggested Corrective Action:

1. Provide fuel and efficiency map data for all engines used in simulation

2. Describe what the “other inputs” are to the engine maps

3. Provide specific references of which published data was used to predict performance of 

the future engines. Some references are given, however, it is not clear how exactly these 

references are used.

4. Wherever possible, provide validation against data on similar technologies 

5. Describe in detail the approach used to “stack up” technologies for a given powertrain 

recipe

Engines FTP HWFET US06

Baseline 42.1 62.5 37.0

Stoich_DI_Turbo 46.3 55.3 33.7

Lean_DI_Turbo 48.3 56.4 33.9

EGR_DI_Turbo 48.2 57.6 35.2

Atkinson_CPS 44.5 59.0 35.4

Atkinson_DVA 45.5 57.1 34.5
Shawn Midlam-Mohler - Peer Review Page 7 

Aftertreatment/Emissions Solutions

Based on the report, it seems that emissions solutions are assumed to be available for all 

powertrain technology packages selected. The report discusses this in some qualitative detail in 

section 4.2.2 with respect to lean-stoichiometric switching. This discussion is somewhat 

incomplete, in that the way it is written it assumes operating at stoichiometry lowers exhaust gas 

temperature. In reality, switching from lean to stoichiometric operation at constant load results 

in higher exhaust gas temperatures. Despite this factual inconsistency, it is indeed generally 

better to operate a temperature sensitive catalyst hot and stoichiometric or rich rather than hot 

and lean – so the concept of lean-stoich switching is valid even if the explanation provided is not. 

Even without this factual inconsistency, some additional discussion of aftertreatment systems 

would be of benefit given that lean-burn gasoline engines are at present a well-known technology 

for many years that is still problematic with respect to emissions control. A separate issue is the 

topic of fuel enrichment for exhaust temperature management which will have an important 

impact on emissions and, if emissions are excessive, reduce the peak torque available from an 

engine.

Suggested Corrective Action:

1. Provide better evidence that powertrain packages have credible paths to meet emissions 

standards 

2. Provide evidence that fuel enrichment strategies are consistent with emissions regulations

Advanced Valvetrains

Two types of advanced valvetrains were included in the study, cam-profile switching and 

digital valve actuation. Both of these technologies are aimed at reducing pumping losses at partload. The impact of these technologies is difficult to predict using simplified modeling 

techniques and typically require consideration of compressible flow and a 1-D analysis at a 

minimum. Even with an appropriate fidelity model, these systems require significant amounts of 

optimization in order to determine the best possible performance across the torque-speed plane 

of the engine. It is unclear how these systems were used to generate accurate engine maps given 

the level of detail provided in the report. 

Suggested Corrective Action:
Shawn Midlam-Mohler - Peer Review Page 8 

1. Describe how variable valve timing technologies were applied to the base engine maps

2. Describe the process of determining the extent of the efficiency improvement

3. Describe how optimal valve timing was determined across the variety of engines 

simulated

Direct Injection Fuel Systems

Because of the availability of research and production data in this area, it is expected that 

performance from this technology was used to predict performance rather than any type of 

modeling approach. That being said, the report does not describe where or how this data might 

have been used to develop the fuel consumption map of the engines simulated nor what data 

sources were used.

Suggested Corrective Action:

1. Cite sources of data used to predict DI performance 

2. Describe how this data was used to develop the future engine performance maps

3. Provide validation of modeling techniques used 

Boosting Systems

 Boosting was applied to many of the different powertrain packages simulated. Beyond 

stating what maximum BMEP that was achievable, very little is mentioned in how the efficiency 

of the boosted engines were determined. Among other factors, boosting often creates a need for 

spark retard which costs efficiency if compression ratio is fixed. These complex issues are tied 

to combustion which is inherently difficulty to model. This aspect of the engine model is not 

well documented in the report. 

Suggested Corrective Action:

1. Describe the process of arriving at the boosted engine maps 

2. Describe how factors like knock are addressed in the creation of these maps

Engine Downsizing

Engine scaling is used extensively in the report. Basic scaling based on brake mean 

effective pressure is common in modeling at this level of fidelity, thus, this does not need any 

special description. However, the report mentions some means of modeling the increased 
Shawn Midlam-Mohler - Peer Review Page 9 

relative heat loss with small displacement engines which is not a standard technique. The model 

or process used to account for this effect should be explicitly described given that engine size is 

one of the key parameters in the design space.

Suggested Corrective Action:

1. Properly document the process of scaling engines 

2. Validate the process used to scale engines

Warm-Up Methodology

The report describes a 20% factor applied to bag 1 of the FTP-75 for baseline vehicles and a 

10% factor applied to the advanced vehicles. The motivation for these factors is described 

qualitatively and is valid, as many organizations are currently investigating strategies to 

selectively heat powertrain components to combat friction effects. However, the values for these

factors that were selected are not backed up with any data or citation. It is suspicious that the 

two values cited are such round numbers - the data from which these numbers are derived should 

be cited. Because of the complexity of this phenomenon, some type of empirical model is 

justified. The model described in the report is not sufficiently validated to judge its suitability.

Suggested Corrective Action:

1. Cite sources of data for 10% and 20% factors applied to the cold bag fuel economy 

data

2. Cite and/or validate the modeling approach used 

Accessory Models

The accessory model is divided into electrical and mechanical loads. The electrical submodel assumes alternator efficiency’s of 55% and 70% for the baseline and advanced vehicles 

respectively. Given the required simplicity of the model, a simple model like this is likely 

acceptable, however, there is no source described for the alternator efficiencies. The base 

electrical load of the vehicle is mentioned briefly, however, no numerical values are given for 

each vehicle class or any type of model described.

The electrical system also includes an advanced alternator control which allows for 

increased alternator usage during decelerations for kinetic energy recovery. The control 

description given is valid but simplistic, but seems to fit the expected level of accuracy required 

for the purpose. There is an issue regarding with the approach for modeling the battery during 
Shawn Midlam-Mohler - Peer Review Page 10

this process. When charging the battery at the stated level of 200 amps, the charging efficiency 

of the battery will be relatively poor. During removal of the energy later, there will once again 

be an efficiency penalty. There is no description of a low-voltage battery model in the report nor 

any explicit reference to such charge/discharge efficiencies. Additionally, an arbitrary limit of a 

200 amp alternator is defined for all vehicle classes – it is unlikely that a future small car and a 

future light heavy duty truck will have an alternator with the same rating.

On the mechanical side, it is assumed that “required accessories” (e.g. engine water 

pump, engine oil pump) are included in the engine maps. The mechanical loading of a 

mechanical fan is mentioned but no description of the model which, at a minimum, should be 

adjusted based on engine speed and engine power.

Suggested Corrective Action:

1. Cite and/or validate the alternator efficiency values of 55% and 70% 

2. Account for charge/discharge losses in the advanced alternator control and/or 

describe the 12V battery model used for the simulation 

3. Describe, cite, and validate the accessory fan model used in the simulation

4. Justify the use of a 200 Amp advanced alternator across all of the vehicle platforms.

Engine Technology “Stack-Up”

There are a host of different technologies superimposed to create the future powertrain 

technologies. There is not a clear process described on how this technology “stack-up” is 

achieved. For instance, an advanced engine technology may allow for greatly improved BMEP. 

Greatly improved BMEP often comes at the expense of knock limits which are difficult to model 

even with sophisticated modeling techniques. In this simulation, many layers of powertrain 

technology are being compounded upon each other which will not simply sum up to the best 

benefits of all of the technologies – there are simply too many interactions. At the level of 

modeling described, which are maps which are altered in various unspecified ways; it is not clear 

how the technology stack-up is captured. 

Suggested Corrective Action:

1. Describe in greater detail the approach used to model technology stack-up on the 

advanced vehicles

2. Provide some form of validation that this approach is justified
Shawn Midlam-Mohler - Peer Review Page 11

Baseline Hybrid Models

The following subsections deal with issues related to the hybrid component models. 

Hybrid Control Strategy

Hybrid vehicles are particularly challenging to model because of the extra components 

which allow multiple torque sources, and thus, require some form of torque management strategy 

(i.e. a supervisory control.) The report briefly describes a proprietary supervisory control 

strategy that is used to optimize the control strategy for the FTP, HWFET, and US06 drive cycle. 

The strategy claims to provide the “lowest possible fuel consumption” which seems to be 

somewhat of an exaggeration – this implies optimality which is quite a burden to achieve and 

verify for such a complicated problem. The strategy also is reported to be “SOC neutral over a 

drive cycle” which is also difficult to achieve in practice in a forward looking model. Once can 

get SOC with a certain window, however, short of knowing the future or simply not using the 

battery - it is impossible to develop a totally SOC neutral control strategy. 

Another factor that must be considered is that a hybrid strategy that achieves maximum 

fuel efficiency on FTP, HWFET, and US06 does not consider many other relevant factors. 

Performance metrics like 0-60 time and drivability metrics often suffer in practice. In today’s 

hybrids, the number of stop-start events is sometimes limited from the optimum number for

efficiency because of the emissions concerns. Because of these factors and others, a strategy 

achieving optimal efficiency may be higher than what can be achieved in practice.

Without even basic details on the hybrid control strategy, it is simply not possible to 

evaluate this aspect of the work. Because of the batch simulations with varying component sizes

and characteristics, this problem is not trivial. Supervisory control strategies used in practice and 

in the literature require intimate knowledge of the efficiency characteristics and performance 

characteristics of all of the components (engine, electric motors/inverters, hydraulic braking 

system, and energy storage system) to develop control algorithms. This concern is amplified by 

the lack of validation of the hybrid vehicle model against a known production vehicle. It is 

unclear how a “one-size fits all” control strategy can be truly be perform near optimal over such 

widely varying vehicle platforms.
Shawn Midlam-Mohler - Peer Review Page 12

A last comment is that there is no validation of the HEV model against current 

production vehicles. At a minimum, the Toyota Prius has been dissected sufficiently in the 

public domain to conduct a validation of this class of hybrid electric vehicle.

Suggested Corrective Action: 

1. Better describe the hybrid control strategy and validate against a current production 

baseline vehicle

2. Validate that the HEV control algorithm performs equally well on all vehicle classes

3. Validate that other vehicle performance metrics, like emissions and acceleration, are not 

adversely impacted by an algorithm that focuses solely on fuel economy. The emission 

side of things will challenge to validate with this level of model, however, some kind of 

assurance should be made to these factors which are currently not addressed at all. 

Electric Traction Components

The model of electric traction components is not discussed in any detail, as the only 

mention in the report is that current technology systems were altered by “decreasing losses in the 

electric machine and power electronics.” Given the importance of the electric motor and inverter 

system in hybrids this is not acceptable.

Suggested Corrective Action: 

1. Describe the method used to model electric traction components 

2. Provide validation/basis for the process used to generate future technology versions of 

these components 

3. Describe the technique used to scale these components 

HEV Battery Model

Battery models for HEVs are necessary to adequately model the performance of an HEV. 

The report provides no substantive description of the battery pack model, other than that the 

model was developed by “lowering internal resistance in the battery pack to represent 2010 

chemistries under development.” Battery pack size is also not a currently a factor in the model – 

this has a impact of charge and discharge efficiency of the battery pack.

Suggested Corrective Action: 

1. Describe the method used to model the HEV battery 
Shawn Midlam-Mohler - Peer Review Page 13

2. Provide validation/basis for the process used to generate future technology versions of the 

battery

3. Describe the technique used to scale the HEV battery 

Transmissions

This peer reviewer is not as well-practiced in transmissions as in other areas in this 

review. Because of this, a more limited review was conducted of this aspect of the report. As 

with the other areas of the report, the general concern in this area is the inadequacy of 

documentation of the modeling approach and validation. Generically, the same issues noted 

above are applicable here:

1. Cite data sources used in modeling 

2. Validate models wherever possible 

3. Fully describe transmission models/maps and processes used to generate them

4. Fully describe clutch/torque converter models/maps and processes used to generate them

5. Fully describe the process used to generate shift maps and the operation of the shift 

controller 

6. Fully describe the lockup controller (i.e. how soon can it enter lockup after shifting?) 

7. Fully describe the process for modeling torque holes during shifting 

8. Fully describe the model used for the final drive (i.e. inputs/structure/outputs) 

Data Analysis Tool

The vehicle simulator is used to generate several thousand simulations using a DOE 

technique. This data is then fit with a neural-network-based response surface model in which the 

“goal was to achieve low residuals while not over-fitting the data.” This response surface model 

then becomes the method from which vehicle design performance is estimated in the data 

analysis tool. In this case, the response surface model is nothing more than a multi-dimensional 

black-box curve fit. There was no error analysis given in the report regarding this crucial step. 

By way of example, the vehicle simulator could provide near perfect predictions of future 

vehicle performance; however, a bad response surface fit could corrupt all of the results. 
Shawn Midlam-Mohler - Peer Review Page 14

Suggested Corrective Action:

1. Provide error metrics for the neural network RSMs (i.e. R2, min absolute error, max 

absolute error, error histograms, error standard deviation, etc.) before combining the fit 

and validation data sets 

2. Provide the error metrics described above for the RSMs after combining the fit and 

validation data sets

3. Provide validation that the data analysis tool correctly uses the RSM to predict results 

very close to the source data (i.e. demonstrate the GUI software behaves as expected)

Conclusions

As outlined in the executive summary, it was not possible to answer the charge questions 

provided for this peer review due to lack of completeness in the report. Thus, this report was 

aimed at providing feedback on what information would be helpful to allow a reviewer to truly 

evaluate the spirit of the charge questions. With the above in mind, the following conclusions are 

made.

The modeling approach describe in the report could be appropriate for the simulation task 

required and is generally consistent with approaches used by other groups in this field. The 

conclusions from the report could very well be sound; however, there is insufficient information 

and validation provided in the report to determine if this is the case. The technique used to 

analyze the mass simulation runs could also be sound, although the accuracy of the response 

surface model is not cited in the report. 

These issues are summarized in the following key areas: 

1. The process of arriving at the performance of the future technologies is not well 

described

2. The majority of models are only described qualitatively making it hard or impossible 

to judge the soundness of the model 

3. Some of the qualitative descriptions of the models indicate that models do not 

consider some important factors 

4. Because of the qualitative nature of the model descriptions, there is a major lack of 

transparency in the inputs and parameters in the models 
Shawn Midlam-Mohler - Peer Review Page 15

5. Where precise value(s) are given for parameters in the model, the report generally 

does not cite the source of the value(s) or provide validation of the particular value 

6. Validation of the model and sub-models is not satisfactory (It is acknowledged that 

many of these technologies do not exist, but the parameters and structure of the model 

have to be based on something.) 
Shawn Midlam-Mohler - Peer Review Page 16

Supplemental Review

After the main review, some supplemental information was provided for further review. 

Comments on this material are found below and are organized by the title of the file reviewed. 

General Comments

The supplemental review material provided some answers to questions posed above, but 

in general, did not provide the level of detail necessary to ensure a thorough review of the 

process. The conclusion of this reviewer remains similar as on the original review, which is that 

there were no serious flaws found in the work, however, there were enough omissions that it is 

not possible to accurately judge if the predictions made are accurate. The biggest concern in 

this work is the lack of validation and/or citation of where data and models are coming from. 

There are numerous maps that are presented in the follow-up material, however, these maps had 

to have originated from some process (which needs documented) and should be compared 

against some kind of validation. Despite the lack of documentation provided, the work is 

generally that of a project team that is competent in this field of study. 

Cold Start Correction Methodology

The correction used to adjust fuel economy for cold start is described in this presentation. 

The method is based on two pieces of information: 

1. A set of three tests from a single vehicle’s instantaneous fuel multiplication correction 

factor

2. A piece of EPA data which shows a fleet-wide average for 2007 of the instantaneous fuel 

multiplication correction factor

The instantaneous fuel multiplication correction factor is not described in the 

presentation, however, it is assumed to be the sum of the “short term fuel trim” and “long term 

fuel trim.” If this is the case, then this value doesn’t correlate to increased fuel consumption, but 

rather, to errors in the injector characterizations, fuel property assumptions, and air estimation 

algorithm in the engine controller. The engine controller is going to maintain stoichiometry 

based on oxygen sensor measurements, these trim values are the simply the feedback correction

values required to do this based on the feedforward algorithm in the ECU. By way of example, I 

could alter the fuel tables of an ECU by 15% which would cause the feedback control system to 
Shawn Midlam-Mohler - Peer Review Page 17

correct by an opposite 15%. This would not change the fuel consumption of the vehicle once the 

control system has corrected it, which would happen in seconds. 

I don’t disagree necessarily with the magnitude of the outcomes, since they are based 

mostly on EPA bag fuel economy data. If I am correct in my understanding of the correction 

factor then the method is not valid. 

Alternator Regen Shift Optimizer

Alternator Regen

The alternator regeneration strategy is not well documented. The key system 

specifications, such as max alternator output and efficiency, are listed as assumptions without a 

data source for validation. The efficiency of the battery is not mentioned in this nor other 

presentations that this reviewer has read – battery efficiency for a lead acid battery at high 

currents is poor, this would have an impact on the recovery of energy. Strategies like this are 

disruptive to drivability and this issue is not discussed in the presentation. 

Shift Optimizer

Shifting strategy impacts efficiency, performance, and drivability. Manufacturers are 

aware of this and balance all three when calibrating shift maps. Changing baseline shift maps to 

improve efficiency will have an impact on the other metrics which are also important to the 

vehicle. Additionally, it is not clear how the optimized shift strategy was developed, what the 

shift strategy is, or how it will be applied to the range of transmissions in the study. It is stated 

that is optimizes BSFC, however, there are other constraints that must be applied in addition to 

this.

Battery Warm up 1, Battery Warm up 2

The battery model described has the following possible problems: 

1. The model is relatively simple – but could potentially work for the application and 

generally is consistent with the fidelity of the rest of the model.

2. The model references ambient temperature for heat rejection. Most HEVs pull in cabin 

air rather than outside air for cooling, thus, this will cause modeling error. 
Shawn Midlam-Mohler - Peer Review Page 18

3. Adjusting the Mbat x Cpbat term by 200% is a red flag that something might be 

fundamentally wrong with either the model formulation or the data used in the model. 

There should be minimal errors in the mass estimation of the pack and the specific heats 

of battery modules can be found in the literature or through testing. 

4. The method of handling battery packs of different classes of vehicles is not described, nor 

are the actual parameters for these different models disclosed.

Turbo Lag

The data and methods used in modeling turbo lag are appropriate and there is sufficient 

explanation and data to support the model. 

Future Friction Assessment

The provided presentation does not describe how engine friction projections to 2020 are 

made or how they are modeled. It provides some data from 1995 to 2005, however, it does not 

provide any useful insight into how this information is used. 

Scaling Methodology Review

With one exception, the scaling methodology appears to be sound given the information 

provided in the presentation. The curve used to adjust BSFC with displacement ratio is not 

supported with data or any citation of where it originated. The motivation for this correction 

seems valid, however, it needs to be supported with data. 

SI Engine Maps and Diesel Engine Maps

The baseline engine map data is shown in a series of figures and references are provided 

for the specific vehicle that the map is for. It is assumed that this indicates that this data has been 

measured experimentally. If this is the case, then this is well documented.

For the 2020 engine maps, there is insufficient detail in this presentation on how the maps 

were generated. Getting accurate simulation requires careful validation of the model as well as 

the data in the model – these engine maps are not sufficiently well documented for me to make a 

judgment on their suitability for the overall goal of the simulator. I am well aware that these 

future engines do not exist, but there had to be some process of generating these engine maps. 

Without more information on this process it is simply not possible to comment on their accuracy. 
Shawn Midlam-Mohler - Peer Review Page 19

BSFC Map Comparisons

I reviewed this but do not have any substantive comments. All of the figures compare 

pseudo-virtual engines with other pseudo-virtual engines. A comparison back to a known, 

experimentally validated engine current engine would have been more useful for me as it would 

allow one to see the magnitude of improvements that were assumed for the 2020 engines and 

where on the map these improvements were made.

Input Data Review

The documentation on the Diesel engine maps was helpful; however, it did not discuss 

how the 2020 engine maps were developed. This is critical for having confidence in the 

predictions made for the Diesel powertrains in 2020. 

The shift strategy is discussed qualitatively; however, it is not described in enough detail 

to understand exactly how it is accomplished. Shift schedules are shown, however, no validation 

is shown that would indicate that these shift schedules are optimal as claimed.

The torque converter models are standard models, thus, the provided documentation is 

adequate.

Hybrid Controls Presentations

Several hybrid controls presentations were provided, however, it was difficult to piece 

together what information superseded the other since they were provided out of context. There 

were several good slides showing dynamic programming results of different control scenarios, 

however, it is assumed that this was not used for the mass simulation since it would be 

computationally impractical. Thus, I expected to see some results comparing the offline control 

results to the actual control used in the vehicle simulation, however, this was not found. The 

major concern in this area is developing a control strategy that is near optimal for a wide variety 

of hybrid architectures as well as architectures with varying component types and sizes. Without 

further validation in this area it is not clear that the hybrid results are valid since the control has 

such an important role in this. 
Review of supplemental materials 

to the report 

COMPUTER SIMULATION OF LIGHT-DUTY VEHICLE TECHNOLOGIES 

FOR GREENHOUSE GAS EMISSION REDUCTION

IN THE 2020-2025 TIMEFRAME

18 August 2011 

Prepared for 

ICF International 

Environmental Science & Policy Division

Contracts Management Group 

9300 Lee Highway, Fairfax, VA 22031-1207 USA 

Robert F. Sawyer, PhD

Partner

SAWYER ASSOCIATES

PO Box 6256

Incline Village, NV 89450-6256 USA

Phone 1-510-305-6602

email: rsawyer@sawyerassociates.us 

 
2 

OVERVIEW

Reviewers of the report, Computer simulation of light-duty vehicle technologies for greenhouse 

gas emission reduction in the 2020-2025 timeframe, 6 April 2011, prepared by Ricardo, Inc. 

requested documentation of data used in the computer simulation. Of particular interest were the 

engine maps and other performance information incorporated in the model. Ricardo provided 44 

documents that included proprietary engine maps, proprietary Ricardo reports, technical papers 

from the open literature, responses to USEPA questions, and other materials. 

REVIEW 

For each document, its title, a brief description of the nature of the material contained, and 

comments on the nature of the material follows: 

1) Ricardo, Action Item Response, 16 Feb 10, 15 p. (proprietary) 

A response to an EPA inquiry, this document deals with engine maps, engine map comparisons, 

engine map plots, transmissions, batteries, motor and generator efficiency maps. 

Comment: Ricardo responses and data selection seem reasonable. 

2) Ricardo, Baseline Camry with Alternator Regen and Shift Optimizer Development of 

Optimized Shifting Strategy Light Duty Vehicle Complex Systems Simulation EPA Contract 

No. EP-W-07-064, work assignment 2-2, 15 Apr 10, 10 p. (proprietary) 

This document provides data on effectiveness of shift optimizer, including alternator regen, over 

the FTP and HWFET. 

Comment: Seems reasonable, improvements are greater on FTP than HWFET. 

3) Carlson, R., et al., Argonne National Laboratory, On-Road Evaluation of Advanced 

Hybrid Electric Vehicles over a Wide Range of Ambient Temperatures EVS23 – Paper #275, 

15 p. 

Paper reports on-road and dynamometer testing of two hybrid vehicles at cold (-14 degC) and 

hot (33 decC) conditions. Fuel economy increases with temperature (except for highest 

temperatures with the system which does not limit battery temperature). 

Comment: Paper provides data showing importance of temperature on hybrid vehicle fuel 

economy. These data are used by Ricardo to validate their battery warm up model, see next 
3 

document. 

4) Ricardo, Hybrid Battery Warm Up Model Validation – Update, Light Duty Vehicle 

Complex Systems Simulation ,EPA Contract No. EP-W-07-064, work assignment 2-2, 15 

Mar 10, 5 p. (proprietary) 

This report presents a simple battery heat transfer model for battery warm up and compares with 

Argonne National Laboratory of the previous document. 

Comment: Model produces adequate prediction of battery temperature. 

5) Ricardo, BSFC Map Commparisons, LBDI vs EGR Boost & DVA for STDI, OBDI, & EGR 

Boost, Light Duty Vehicle Complex Systems Simulation, EPA Contract No. EP-W=07=064, 

work assignment 2-2, 24 Feb 10, 20 p. (proprietary) 

Comparison of engine technologies in terms of maps of percent difference in bsfc in bmep vs 

rpm space allows visualization 

Comment: Straight forward data analysis, presumably as requested by USEPA. Should aid in 

understanding technology performance differences. 

6) Mischker, K. and Denger, D., Requirements of a Fully Variable Valvetrain and 

implementation using the Electro-Hydraulic Valve Control System EHVS, 24th International 

Vienna Engine Symposium 2003, 17 p. 

This paper describes an electro-hydraulic valve system (EVHS) and limited data on reduction in 

bsfc. 

Comment: This would seem to be of limited quantitative value since technology is well advanced 

beyond 2003. 

7) Ricardo, Engine and Battery Warm-Up Methodology, Light Duty Vehicle Complex Systems 

Simularion, 17 Feb 10, 16 p. (proprietary)

Document reviews engine and battery warm-up strategies and provides a simple model. 

Comment: The approach to battery warm-up is uncertain. Points to importance of test cycle (FTP 

for fuel economy compliance versus test for EPA label versus real-world). 

8) Ricardo, Response to EPA Questions on the Diesel Engine Fuel Maps, Supplemental 

Graphs for Word Document, 16 Feb 10, 11 p. (proprietary) 
4 

Document presents proposed diesel engine maps for MY2020+ vehicles. 

Comment: Anticipated technologies are listed but how the maps were generated is not described. 

Maps seem reasonable. 

9) Ricardo, Assessment of Technology Options, Technologies related to Diesel Engines, 23 

Nov 09, 17 p. 

Overview predicts continuation of low uptake in the U.S. LDA and LDT markets. Review deals 

with various engine technologies to improve efficiency. Individual improvements <1-5%. Most 

promising is electric turbo-compounding (bottoming cycle to recover exhaust thermal energy to 

produce electricity). 

Comment: Individual technology assessments seem reasonable. There is no analysis of 

integrating several technologies. 

10) Ricardo, EBDI Project Overview, Ethanol Boosted Direct Injection, Nov 09, 8 p. 

This study examines ethanol boosted direct injection (EBDI) to optimize engine operation of E85 

fuel. Possibility exists to match or exceed diesel performance and reduce CO2 emissions. 

Comment: It is not clear if comparison of EBDI and diesel is a equal technology level. 

11) Ricardo, Hybrid Controls Follow-up, 10 Sep 11, 3 p. (proprietary) 

Report discussed motor/general efficiency map used for 2020 technology. Projected efficiencies 

peak at 95% but most P2 hybrid application if below 90% efficiency. 

Comment: I am not qualified to assess if the projected motor/generator efficiencies are 

appropriate for 2020-2025 as reported, but they seem low for 15 years in the future. 

12) UOM, HiTor®"for"elecgtric,"hybrid"electric,"and"fuel"cell"powered"vehicles,"18"Aug"

09,"based"on"test"data"map,"5"p."

Describes(power(electronics(for(motor(generator(control,(including(an(efficiency(map(for(

combined(controller(and(motor(based(on(test(data.

Comment:(Efficiency(maps(seem(reasonable.(
5 

13)"Odvarka,"E.,"et"al.,"Electgric"motor:generator"for"a"hybrid"electric"vehicle,"

Engineering"Mechanics,"16,"131:139,"2009,"9"p.

Describes(electrical(machine(options(of(hybrid(electric(vehicles.(Includes(efficiency(maps(

for(four(technologies.

Comment:(Data(are(of(general(interest,(but((date(from(2003.(

14)"UOM,"PowerPhase®75"for"electric,"hybrid"electric,"and"fuel"cell"powered"vehicles,"

not"dated,"6"p.

Described(power(electronics(of(vehicle(electric(power.

Comment:(Similar(to(earlier(brochure(on(power(electronics,(including(efficiency(map.(

15)"Ricardo,""Future"Engine"Friction"Assessment—Response"to"Action"Item"Question"SI"

Engine"#4,"18"Feb"11,"4"p."(proprietary)

Projects(continued(reduction(in(engine(friction,(2010E2020.

Comment:(Data(provide(confirm(projection.(

16)"Ricardo,"Revised"Follow:up"Answers"to"8"April"2010"Meeting"with"EPA"and"Ricardo,"

19"Apr"10,"8"p."(proprietary)

Presents(fueling(maps(for(several(technologies.(

Comment:(Adds(to(documentation(of(engine(map(data.(

17)"Alger,"T.,"Southwest"Research"Institute,"Examples"of"HEDGE"Engines,"2009,"4"p.

Presents(engine(map(for(a(2.4(L(I4(HighEEfficiency(Dilute(Gasoline(Engine((HEDGE)(engine(

and(compares(with(TC(GDI(engine,(diesel(engine.

Comment: Adds to documentation of engine map data. 

18) Ricardo, Hybrid Controls Peer Review, 18 Feb 10, 31 p. (proprietary) 

Review of hybrid control technologies for various architectures. Review of battery operation in 

cold weather. 
6 

Comment: Thorough description of technologies and their operation characteristics. Battery 

discussion covers similar material to an earlier paper. 

19) Ricardo, Hybrids Control Strategy, 6 Aug 10, 41 p. (proprietary) 

Discusses development of control strategies for P2 and Power Split hybrids. 

Comment: includes efficiency maps and substantial technical detail including vehicle mass 

effect.

20) Ricardo, Simulation Input Data Review, 4 Feb 10, 14 p. (proprietary) 

Described hybrid architectures with emphasis on machine-inverter combine efficiencies, 

including efficiency maps. 

Comment: More data, seems reasonable. 

21) Ricardo, Assessment of Technology Options, 18 Nov 09, 14 p. (proprietary) 

Assessment of hybrid technologies using evaluation template. 

Comment: Treats a range of hybrid technologies, including series hydraulic, giving projections 

of CO2 reduction benefits. 

22) Ricardo, Simulation Input Data Review, 2 Feb 10, 30 p. (proprietary) 

Document review modeling parameters for vehicle performance simulations, including engine 

efficiency maps for a range of engine and transmission technologies. 

Comment: This is the kind of data that we requested. Includes shift strategies. Seems reasonable 

and well-documented. 

23) Trapp, C., et al., Lean boost and NOx—strategies to control nitrogen oxide emissions, (no 

date), 23 p. 

Technical paper that describes lean burn direct injection (LBDI) engines, SCR NOx control, and 

more. Includes some emission control cost data. 

Comment: Not clear how this related to Ricardo’s model development for EPA. 
7 

24) Trapp, C., et al., NOx emission control options for the Lean Boos downsized gasoline 

engine, (2 Feb 07), 34 p. 

Paper compares lean NOx trap and selective catalytic reduction technologies. Includes some 

engine map data for NOx emissions. Includes cost data for aftertreatment. 

Comment: Good academic paper with useful data. Not clear what or how Ricardo used. 

25) Trap, C., et al., NOx emission control options for the lean boost downsized gasoline 

engine, (2 Feb 07), 27 p. 

Paper review international emissions regulation and technologies to meet. 

Comment: This paper contains some of the same information as the preceding two. Simulated 

date presented, again for SCR and LNT technologies. 

26) Ricardo, Lean/Stoichiometric switching load for 2020 Hybrid Boost Concept, (no date), 2 

p.

Presents space velocity and fuel maps. 

Comment: Relevance not clear. 

27) Ricardo, Proposed Lean/Stoichiometric switching load for hybrid boost concept, 29 Apr 

10, 1 p. 

Identifies proposed lean zone operating region on engine map. 

Comment: relevance not clear. 

28) Lymburner, J.A., et al., Fuel consumption and NOx Trade-offs on a Port-Fuel-Injected 

SI Gasoline Engine Equipped with a Lean NOx Trap, 4 Aug 09, 20 p. 

This technical paper examines the trade-off between NOx control and CO2 emissions. 

Comment: Good work but relevance not clear. 

29) Lotus(?), (from Kapus, P.E. et al., May 2007), Comparison to other downsized engines 

This one figure is a partial engine map with context vague. 
8 

Comment: Significance is not clear. 

30) Turner, J.W.G., et al., Sabre: a cost-effective engine technology combination of high 

efficiency, high performance and low CO2 emissions, Low Carbon Vehicles, May 09, IMechE 

Proceedings, 14 p. 

This paper describes a technology for reducing COs emissions in a downsized engine. The Sabre 

engine is a collaboration between Lotus Engineering and Continental Automotive Systems. 

Comment: Limited performance data provided. 

31) Ricardo, Conventional Automatic Nominal Results, 16 Mar 10, 17 p. (proprietary) 

This presentation includes mileage versus 0-60 mph time maps for a range of vehicles (light duty 

to large truck). Also presented are comparisons of fuel economy for different regulatory test 

cycles and technologies. 

Comment: Significance not clear. 

32) Ricardo, Report on light-duty vehicle technology package optimization, 4 Dec 09, 32 p. 

This is a progress report on Ricardo’s modeling work for the EPA. A range of engine 

technologies, hybrid technologies, transmission, and vehicle technologies are described. 

Comment: A comprehensive list of near term technologies are included. The report is incomplete 

and optimization apparent is not included here. 

33) Ricardo, Revised follow-up answers for hybrid action items, 23 Jun 10, 16 p. 

(proprietary) 

This report answers questions on electric drive train efficiency, battery characteristics, and 

available braking energy, and more. 

Comment: Interesting data, but implication not clear. 

34) Ricardo, Response to questions regarding the generation of the diesel fuel maps for fuel 

efficiency simulation, 16 Feb 10, 10 p. (proprietary)

Paper answers a series of EPA questions on how the diesel fuel maps were generated. 

Comment: This is relevant information and provides a convincing description of the technical 
9 

basis for the diesel fuel maps. 

35) Ricardo, Scaling Methodology Review, 19 Jan 10, 9 p. 

This document explains the scaling methodology used in the EASY5 vehicle model. 

Comment: This description in clear and useful. 

36) Ricardo, SCR as an Enabler for Low CO2 Gasoline Applications, no date, 35 p. 

This presentation describes technology and implementation for exhaust NOx reduction for lean 

burn gasoline engines. 

Comment: Comprehensive discussion of technology, but if and how inconcorporated in the 

model not clear. 

37) Ricardo, Simulation Input Data Review, 18 Mar 10, 17 p. (proprietary) 

This document reviews the engine maps used in the model. Includes are examples of the baseline 

maps plus modifications associated with a range of technologies. Data apply to all 7 vehicle 

classes.

Comment: This is the documentation that was missing in the earlier review material. Looks 

reasonable and is reassuring. 

38) Ricardo, Assessment of Technology Options, 19 Nov 09, 22 p. (confidential) 

This document reviews and rates a range of spark-ignition adaptable technologies to reduce CO2

emissions. Biofuels are included. 

Comment: An interesting compendium but some previously reported. 

39) Shimizu, R., et al., Analysis of a Lean Burn Combustion Concept for Hybrid Vehicles, 

2009, 13 p. 

A technical paper, this document describes early (1984) and more recent Toyota lean burn 

engines. 

Comment: Interesting technical description but no clear if or how used in the Ricardo model. 
10 

40) Takoaka, T., et al., Toyota, Super high efficient gasoline engine for Toyota hybrid system, 

(no date), 16 p. 

This paper describes the hybrid system, IC engine interaction that allows increased IC engine 

efficiency. 

Comment: Of general interest but application to the model not clear. 

41) Ricardo, Assessment of Technology Options, Technologies related to Transmission and 

Driveline, 19 Nov 09, 21 p. 

This document described transmission technologies, including timing of their introduction. 

Comment: Seems reasonable.

42) Ricardo, Transient Performance of Advanced Turbocharged Engines, 15 Sep 10, 19 p. 

(proprietary) 

This report reviews expected advances in boosting technologies and anticipated effects on 

vehicle performance. 

Comment: Interesting information but how it impacts model is not clear. 

43) Kapus, P., Potential of VVA Systems for Improvement of CO2 Pollutant Emission and 

Performance of Combustion Engines, 30 Nov 2006, 9 p. 

This is a technical paper describing variable valve actuation approaches and performance effects. 

Comment: Useful general technical information. 

44) Ricardo, Assessment of Technology Options, Technologies related to Vehicle-level 

Systems, 24 Nov 09, 16 p. 

This review of vehicle technologies that can improve vehicle efficiencies provides a basic 

description and information on expected levels of CO2 reduction. 

Comment: This is a clear description of anticipated improvements in vehicle technologies that 

reduce load and fuel consumption. 
11 

CONCLUSIONS 

Ricardo has provided material, which is stated to be the data incorporated in the computer 

simulation. These data are consistent with the data expected to be the basis of the simulation. It is 

impossible to establish a precise correspondence between the data and the model. The 

performance data covered by the 44 separate documents seem reasonable and provide additional 

assurance that the simulation is soundly based on measured performance. There is no reason to 

doubt either the integrity or capability of Ricardo in their incorporation of appropriate data into 

their simulation model. 
E-1

Appendix E. Draft Project Report by Ricardo
Prepared for: Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

2565 Plymouth Road 

Ann Arbor, Michigan 48105 

Prepared by: Ricardo, Inc. 

40000 Ricardo Drive 

Van Buren Twp., Michigan 48111 

Systems Research and Applications Corporation (SRA) 

652 Peter Jefferson Parkway, Suite 300 

Charlottesville, Virginia 22911 

EPA Contract No.: EP-W-07-064 

Work Assignment: 2-2 

Ricardo Archive: RD.10/157405.6 

Date: 6 April 2011 

Client Confidential and Deliberative 

DRAFT PROJECT REPORT 

COMPUTER SIMULATION OF LIGHT-DUTY 

VEHICLE TECHNOLOGIES FOR 

GREENHOUSE GAS EMISSION REDUCTION 

IN THE 2020–2025 TIMEFRAME 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 2 

The following organizations contributed to this study: 

Ricardo, Inc. 

SRA International, Inc. (Perrin Quarles Associates, Inc.) 

U.S. Environmental Protection Agency 

California Air Resources Board 

International Council on Clean Transportation 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 3 

COMPUTER SIMULATION OF LIGHT-DUTY VEHICLE TECHNOLOGIES FOR 

GREENHOUSE GAS EMISSION REDUCTION IN THE 2020–2025 TIMEFRAME 

EXECUTIVE SUMMARY

Ricardo, Inc. was subcontracted by SRA International, Inc. (SRA), under contract to the United 

States Environmental Protection Agency (EPA) to assess the effectiveness of future light duty 

vehicle (LDV) technologies on future vehicle performance and greenhouse gas (GHG) 

emissions in the 2020–2025 timeframe. GHG emissions are a globally important issue, and 

EPA's Office of Transportation and Air Quality (OTAQ) has been chartered with examining the 

GHG emissions reduction potential of LDVs, including passenger cars and light-duty trucks. 

This program was performed between October 2009 and March 2011. 

The scope of this project was to execute an independent and objective analytical study of LDV 

technologies likely to be available within the 2020–2025 timeframe, and to develop a data 

visualization tool to allow users to evaluate the effectiveness of LDV technology packages for 

their potential to reduce GHG emissions and their effect on vehicle performance. This study 

assessed the effectiveness of a broad range of technologies, including powertrain architecture 

(conventional and hybrid), engine, transmission, and other vehicle attributes such as engine 

displacement, final drive ratio, vehicle weight, and rolling resistance on seven light-duty vehicle 

classes. The methodology used in this program surveyed the broad design space using robust 

physics-based modeling tools and generated a computationally efficient response surface to 

enable extremely fast surveying of the design space within a data visualization tool. During this 

effort, quality assurance checks were employed to ensure that the simulation results were a 

valid representation of the performance of the vehicle. Through the use of the data visualization 

tool, users can query the design space on a real time basis while capturing interactions between 

technologies that may not be identified from individual simulations. 

This report documents the work done on the program “Computer Simulation of Light Duty 

Vehicle Technologies for Greenhouse Gas Emission Reduction in the 2020–2025 Timeframe.” 

This work has included identifying and selecting technologies for inclusion in the study, 

developing and validating baseline models, and developing the data visualization tool. 

 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 4 

TABLE OF CONTENTS

PAGE

1. INTRODUCTION ................................................................................................................ 8 

2. OBJECTIVES ..................................................................................................................... 8 

3. BACKGROUND .................................................................................................................. 9 

3.1 Study Background........................................................................................................ 9 

3.2 Ground Rules for Study ............................................................................................... 9 

3.3 Technology Package Selection Process .....................................................................10 

3.4 Complex Systems Modeling Approach ........................................................................10 

3.5 Data Visualization Tool ...............................................................................................11 

4. TECHNOLOGY REVIEW AND SELECTION .....................................................................11 

4.1 Advanced Engine Technologies ..................................................................................12 

4.1.1 Advanced Valvetrains ..........................................................................................12 

4.1.1.1 Cam-Profile Switching Valvetrain .........................................................................12 

4.1.1.2 Digital Valve Actuation Valvetrain ........................................................................12 

4.1.2 Direct Injection Fuel Systems ...............................................................................13 

4.1.3 Boosting System ..................................................................................................13 

4.1.4 Other Engine Technologies ..................................................................................14 

4.2 Engine Configurations .................................................................................................14 

4.2.1 Stoichiometric DI Turbo .......................................................................................15 

4.2.2 Lean-Stoichiometric Switching .............................................................................15 

4.2.3 EGR DI Turbo ......................................................................................................16 

4.2.4 Atkinson Cycle .....................................................................................................16 

4.2.5 Advanced Diesel ..................................................................................................16 

4.3 Hybrid Technologies ...................................................................................................17 

4.3.1 Micro Hybrid: Stop-Start .......................................................................................17 

4.3.2 P2 Parallel Hybrid ................................................................................................17 

4.3.3 Input Powersplit ...................................................................................................18 

4.4 Transmission Technologies ........................................................................................18 

4.4.1 Automatic Transmission .......................................................................................19 

4.4.2 Dual Clutch Transmission (DCT) ..........................................................................19 

4.4.3 Launch Device: Wet Clutch ..................................................................................19 

4.4.4 Launch Device: Dry Clutch Advancements ..........................................................20 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

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TABLE OF CONTENTS (CONT.)

PAGE

4.4.5 Launch Device: Multi-Damper Torque Converter .................................................20 

4.4.6 Shifting Clutch Technology ..................................................................................20 

4.4.7 Improved Kinematic Design .................................................................................20 

4.4.8 Dry Sump .............................................................................................................20 

4.4.9 Efficient Components ...........................................................................................20 

4.4.10 Super Finishing ....................................................................................................21 

4.4.11 Lubrication ...........................................................................................................21 

4.5 Vehicle Technologies ..................................................................................................21 

4.5.1 Intelligent Cooling Systems ..................................................................................21 

4.5.2 Electric Power Assisted Steering .........................................................................21 

5. TECHNOLOGY BUNDLES AND SIMULATION MATRICES .............................................22 

5.1 Technology Options Considered .............................................................................22 

5.2 Vehicle configurations and technology combinations ...............................................23 

6. VEHICLE MODEL .............................................................................................................24 

6.1 Baseline Conventional Vehicle Models ....................................................................24 

6.2 Baseline Hybrid Vehicle Models ..............................................................................25 

6.3 Engine Models.........................................................................................................25 

6.3.1 Warm-up Methodology .........................................................................................26 

6.3.2 Accessories Models .............................................................................................26 

6.4 Transmission Models...............................................................................................27 

6.5 Torque Converter Models ........................................................................................28 

6.6 Final Drive Differential Model ...................................................................................29 

6.7 Driver Model ............................................................................................................29 

6.8 Hybrid Models .........................................................................................................29 

7. MODEL VALIDATION RESULTS ......................................................................................30 

7.1 Baseline Conventional Vehicle Models ....................................................................30 

7.2 Nominal Runs ..........................................................................................................31 

8. COMPLEX SYSTEMS MODEL VALIDATION ...................................................................31 

8.1 Evaluation of Design Space.....................................................................................31 

8.2 Response Surface Modeling....................................................................................32 
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TABLE OF CONTENTS (CONT.)

PAGE

9. RESULTS ..........................................................................................................................33 

9.1 Basic Results of Simulation .....................................................................................33 

9.2 Design Space Query ...............................................................................................33 

9.3 Exploration of the Design Space .............................................................................33 

9.4 Identification and Use of the Efficient Frontier .........................................................39 

10. RECOMMENDATIONS FOR FURTHER WORK ...............................................................39 

11. CONCLUSIONS ................................................................................................................40 

12. REFERENCES ..................................................................................................................41 

APPENDICES...........................................................................................................................42 

Appendix 1: Abbreviations ........................................................................................................42 

Appendix 2: Output Factors for Study .......................................................................................43 

Appendix 3: Nominal Runs Results ...........................................................................................44
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

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LIST OF FIGURES

Figure 3.1: Technology package selection process. ..................................................................10

Figure 6.1: Comparison of CVT and optimized DCT gear ratios over drive cycle. .....................28

Figure 9.1: Design Space Query screen in Data Visualization Tool. ..........................................34

Figure 9.2: Design Space Analysis screen in Data Visualization Tool. ......................................35

Figure 9.3: Full Size Car Design Space Analysis example. .......................................................36

Figure 9.4: Full Size Car Design Space Analysis example. .......................................................37

Figure 9.5: Full Size Car Design Space Analysis example. .......................................................37

Figure 9.6: Standard Car design space analysis example comparing powertrains with EGR DI 

Turbo engine. ..........................................................................................................38

Figure 9.7: Efficient Frontier screen of Data Visualization Tool with example plot. ....................39

LIST OF TABLES

Table 5.1: Engine technology package definition. .....................................................................22

Table 5.2: Hybrid technology package definition. ......................................................................22

Table 5.3: Transmission technology package definition. ...........................................................23

Table 5.4: Baseline and Conventional Stop-Start vehicle simulation matrix. ..............................23

Table 5.5: P2 and Input Powersplit hybrid simulation matrix......................................................24

Table 6.1: Vehicle classes and baseline exemplar vehicles. .....................................................25

Table 6.2: Advanced powertrain configurations and baseline exemplar vehicles. ......................25

Table 7.1: Baseline vehicle fuel economy performance. ............................................................31

Table 8.1: Continuous input parameter sweep ranges with conventional powertrain. ................32

Table 8.2: Continuous input variable sweep ranges for P2 and Powersplit hybrid powertrains. .32

Table A3.1: Nominal Runs Results. ...........................................................................................45
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

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1. INTRODUCTION 

Ricardo was subcontracted by SRA International (SRA), under contract to the United States 

Environmental Protection Agency (EPA) to assess the effectiveness of future LDV technologies 

on future vehicle performance and GHG emissions in the 2020–2025 timeframe. GHG 

emissions are a globally important issue, and EPA's Office of Transportation and Air Quality 

(OTAQ) has been charged with examining the GHG emissions reduction potential of LDVs, 

including passenger cars and light-duty trucks. 

SRA is an interdisciplinary environmental consulting firm specializing in environmental program 

development and implementation support, with a major focus on air quality and GHG reduction 

initiatives. In addition to the SRA–Ricardo team working for EPA, other stakeholders for the 

program included the International Council on Clean Transportation (ICCT) and the California 

Air Resources Board (ARB). Representatives from each stakeholder, together with EPA staff, 

formed the Advisory Committee for this project 

Ricardo, Inc. is the U.S. division of Ricardo plc., a global engineering consultancy with nearly 

100 years of specialized engineering expertise and technical experience in internal combustion 

engines, transmissions, and automotive vehicle development. This program was performed 

between October 2009 and March 2011. 

The scope of the program was to execute an independent and objective analytical study of LDV 

technologies likely to be available in the 2020–2025 timeframe, and to develop a data 

visualization tool to allow users to evaluate the effectiveness of LDV technology packages for 

their potential to reduce GHG emissions. An assessment of the effect of these technologies on 

LDV cost was beyond the scope of this study. 

This work was done in collaboration with EPA and its external partners, and the approach 

included the following activities: 

• Extrapolate selected technologies to their expected performance and efficiency levels in 

the 2020–2025 timeframe. 

• Conduct detailed simulation of the technologies over a large design space, including a 

range of vehicle classes, powertrain architectures, engine designs, and transmission 

designs, as well as parameters describing these configurations, such as engine 

displacement, final drive ratio, and vehicle rolling resistance. 

• Interpolate the results over the design space using a functional representation of the 

responses to the varied model input factors. 

• Develop a Data Visualization Tool to facilitate interrogation of the simulation results over 

the design space. 

2. OBJECTIVES 

The goal of this technical program has been to evaluate objectively the effectiveness and 

performance of a large LDV design space with powertrain technologies likely to be available in 

the 2020–2025 timeframe, and thereby assess the potential for GHG emissions reduction in 

these future vehicles while also understanding the effects of these technologies on vehicle 

performance. 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 9 

3. BACKGROUND 

3.1 Study Background 

EPA and other program stakeholders have a mutual interest in improving the environmental 

performance and efficiency of cars, trucks, buses, and transportation systems to protect and 

improve public health, the environment, and quality of life. Additionally, reduction of GHG 

emissions—emphasizing carbon dioxide (CO2)—is an increasing priority of national 

governments and other policymakers worldwide. 

The purpose of this study is to define and evaluate potential technologies that may improve 

GHG emissions in LDVs in the 2020–2025 timeframe. These technologies represent a mixture 

of future mainstream technologies and some emerging technologies for the study timeframe. 

3.2 Ground Rules for Study 

Several ground rules for the study were agreed at the beginning of the program to bound the 

design space considered in the study. These ground rules identified content that should be 

included in the study as well as content that should be excluded. 

Some examples of the ground rules include the following items for the technology assessment: 

• Seven vehicle classes will be included, as described below 

• LDV technologies must have the potential to be commercially deployed in 2020–2025 

• Vehicle sizes, particularly footprint and interior space, for each class will be largely 

unchanged from 2010 to 2020–2025 

• Hybrid vehicles will use an advanced hybrid control strategy, focusing on battery state of 

charge (SOC) management, but not at the expense of drivability 

• Vehicles will use fuels that are equivalent to either 87 octane pump gasoline or 40 

cetane pump diesel 

• 2020–2025 vehicles will meet future California LEV III requirements for criteria 

pollutants, which are assumed to be equivalent to current SULEV II (or EPA Tier 2 Bin 2) 

levels 

Likewise, the Advisory Committee agreed that the technology assessment for this program 

should exclude the following: 

• Charge-depleting powertrains, such as plug-in hybrid electric vehicles (PHEV) or battery 

electric vehicles (EV) 

• Fuel cell power plants for fuel cell-electric vehicles (FCEV) 

• Non-reciprocating internal combustion engines (ICE) or external combustion engines 

• Manual transmissions and automated manual transmissions (AMT) with a single clutch 

• Kinetic energy recovery systems (KERS) other than battery systems 

• Intelligent vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) optimization 

technology 

• Bottoming cycles, such as organic Rankine cycles, for energy recovery 

• Vehicle safety systems or structures will not be explicitly modeled for vehicles. A full 

safety analysis of the technologies presented in this report is beyond the scope of this 

study 
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 10 

The seven vehicle classes considered in this study are the following, with a currently available 

example vehicle given for each class: 

1. Small (B-class) Car, such as the Toyota Yaris; 

2. Standard (D-class) Car, such as the Toyota Camry; 

3. Small Multi-Purpose Vehicle (MPV), such as the Saturn Vue; 

4. Full Sized Car, such as the Chrysler 300; 

5. Large MPV, such as the Dodge Grand Caravan; 

6. Light-Duty Truck (LDT), such as the Ford F150; and 

7. Light Heavy-Duty Truck (LHDT), such as the GM HD3500. 

3.3 Technology Package Selection Process 

The program team used the process shown in Figure 3.1 to identify the technology options 

described in Chapter 4 and downselect to the technology packages described in Chapter 5. 

F1Figure 3.1: Technology package selection process.

The program team first developed a comprehensive list of potential technologies that could be in 

use on vehicles in the study timeframe, 2020–2025. These technologies were grouped by 

subject area, such as transmissions, engines, or vehicle, and given to Ricardo subject matter 

experts (SMEs) for assessment and evaluation. These SME assessments were reviewed with 

and discussed by the program's Advisory Committee. Technology options were assembled into 

technology packages for use in the vehicle performance simulations. 

3.4 Complex Systems Modeling (CSM) Approach 

Complex systems modeling (CSM) is an objective, scientific approach that supports decision 

making when there are a large number of factors to consider that influence the outcome, as with 

LDV development for vehicle performance and GHG emissions reduction. To be objective, 

performance metrics were identified by the Advisory Committee; these metrics were outputs of 

the vehicle performance simulation effort and characterize key vehicle attributes. To be 

scientific, the performance simulations use a physics-based modeling approach for detailed 

simulation of the vehicle. 

 

The design of experiments (DoE) approach surveys the design space in a way that extracts the 

maximum information using a limited budget of simulation runs. The purpose of the DoE 

simulation matrix was to efficiently explore a comprehensive potential design space for LDVs in 

the 2020–2025 timeframe. The simulation matrix was designed to generate selected 

Technology 

Identification

Subject 

Matter 

Expert 

Assessment

Advisory 

Committee 

Review & 

Technology 

Discussion

Technology 

Package 

Selection

Technology 

Identification

Subject 

Matter 

Expert 

Assessment

Advisory 

Committee 

Review & 

Technology 

Discussion

Technology 

Package 

Selection
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 11 

performance results over the selected drive cycles, such as fuel consumption or acceleration 

times. 

A statistical analysis was used to correlate variations in the input factors to variations in the 

output factors. Because of the complex nature of the LDV configurations and constituent 

technology packages, a neural network approach was used to quantify the relationships 

between input and output factors over the design space explored in the simulations. The result 

of this analysis was a set of response surface models (RSM) that represent in simplified form 

the complex relationships between the input and output factors in the design space. 

3.5 Data Visualization Tool 

The Data Visualization Tool allows the user to query the RSM and develop an understanding of 

how various combinations of future technologies may affect GHG emissions and other vehicle 

performance metrics. Vehicle configurations with unacceptable performance, such as too-low 

combined fuel economy or too-slow acceleration times, can be excluded from further study. 

The Data Visualization Tool uses the RSM set generated by the Complex Systems approach to 

represent the vehicle performance simulation results over the design space. These simulations 

cover multiple variations of vehicle configuration, including several combinations of advanced 

powertrain and vehicle technologies in the seven LDV classes. 

The tool samples vehicle configurations from a selected subset of the design space by using 

Monte Carlo type capabilities to pick input parameter values from a uniform distribution. Defining 

selected portions of the design space and plotting the results visualizes the effect of these 

parameters on vehicle fuel economy and performance, allowing trade off analysis via 

constraints setting to be performed over a wide design space representing the 2020–2025 

technologies as applied. 

4. TECHNOLOGY REVIEW AND SELECTION 

Following the process outlined above, a broad range of potential technologies were identified for 

consideration in the study. These technologies were evaluated qualitatively against the following 

criteria for further consideration: 

• Potential of the technology to improve GHG emissions on a tank to wheels basis 

• State of development and commercialization of the technology in the 2020–2025 

timeframe 

• Current (2010) maturity of the technology 

Based on these criteria, a subset of the full list of technologies was selected for inclusion in the 

study. These technologies are described in this chapter. 

In the study timeframe of 2020–2025, spark-ignited (SI) engines are projected to continue to be 

the dominant powertrain in the U.S. light-duty vehicle market, especially since the efficiency of 

SI engines is expected to approach the efficiency of compression ignition (CI, or diesel) engines 

at the required 2020–2025 emissions levels. Nevertheless, diesel engines are expected to 

contribute to future GHG emissions reduction, especially for the heavier vehicle classes. Thus, 

diesel engine technologies were also considered in the study. 
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The first two sections of this chapter therefore describe the technologies expected to appear in 

these future engines and specific engine configurations, respectively. The other sections in this 

chapter describe the transmission and driveline, vehicle, and hybrid system technologies that 

were included in the overall design space of the study. The implementation of these 

technologies in the vehicle performance models is described in Chapter 6, Vehicle Model. 

4.1 Advanced Engine Technologies 

The primary challenge for advanced engines in the 2020–2025 timeframe is to reduce GHG 

emissions and maintain performance without increasing criteria pollutants. This challenge is 

expected to be met through a range of improvements, from the application of highly-efficient 

downsized engines through to detailed optimization of components and systems. This section 

describes specific technologies or systems that are expected to be included in future engines, 

each of which supports the overall goal of reduced GHG emissions in future vehicles. The 

following section, 4.2, Engine Configurations, describes the complete engine technology 

packages that combine these technologies. 

4.1.1 Advanced Valvetrains 

Several advances in valvetrain technology are expected to be available in the study timeframe. 

These technologies are expected to apply to engines across the whole set of vehicle classes 

examined in the study. 

Advanced valvetrain systems improve fuel consumption and GHG emissions mainly by 

improving engine breathing, thereby reducing pumping losses in the engine. The pumping loss 

mitigation provides larger benefits at part-load operation, such as during urban driving. 

Advanced valvetrains also support engine downsizing, which provides fuel consumption benefits 

across the complete engine operating map. Lastly, they can be used to support faster 

aftertreatment warm-up through varied timing, leading to additional, synergistic gains if the 

faster aftertreatment warm-up creates a benefit to tailpipe-out NOx emissions that can be traded 

off to improve GHG emissions. 

Two advanced valvetrain options, cam-profile switching and digital valve actuation, were 

included in the study and are discussed below. 

4.1.1.1 Cam-Profile Switching Valvetrain 

Cam-profile switching (CPS) systems use a hydraulically-actuated mechanical system to select 

between two or three cam profiles. CPS systems, such as the Honda VTEC, Mitsubishi MIVEC, 

Porsche VarioCam, and Audi Valvelift, have been developed by a number of Japanese and 

European manufacturers. CPS systems can be designed to improve low-speed torque or to 

improve fuel economy by reducing pumping losses at light load. CPS systems are applicable in 

all LDV classes. The benefit to GHG emissions is expected in part-load operation, and will 

therefore provide a larger benefit in city driving than in highway driving. 

4.1.1.2 Digital Valve Actuation Valvetrain 

Digital valve actuation (DVA) uses a mechanical, hydraulic, or electrical system to actuate the 

valves independently of a camshaft. The full realization of DVA in the study timeframe will be a 

camless DVA system, where there is no mechanical linkage between the engine crank and the 

valves. The engine fueling maps with DVA were assumed to use camless DVA systems, such 
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as electrohydraulic or electromagnetic systems. Electropneumatic systems are less mature 

currently, but may yet be available late in the timeframe. An example DVA system in current 

production is the Fiat MultiAir system, an electro-hydraulic system (Fiat, 2009) that still uses a 

camshaft to provide the primary timing for the valve open and valve close events. The DVA 

system could be implemented to provide flexibility, with valve event timing, valve lift profiles, or 

both. As with the CPS systems, the main benefit in GHG emissions is a result of reducing 

pumping losses at part-load operation. 

4.1.2 Direct Injection Fuel Systems 

Direct injection (DI) fuel systems are the standard fuel injection system in use on current diesel 

engines. One of the significant changes expected by the 2020–2025 timeframe is a continued 

transition from port fuel injection (PFI) to DI in SI engines as well. For SI engines with DI, the 

fuel is injected directly into the combustion cylinder before being ignited. DI fuel systems inject 

the fuel at a higher pressure than PFI injectors do, and allow the use of multiple injection events 

to support advanced combustion control. SI engines with DI were first introduced in Japan in 

1996, and an increasing number of new SI engines now feature DI. 

DI improves fuel economy because it facilitates a higher compression ratio in the engine, which 

helps improve the engine's volumetric and thermal efficiency. Using DI improves fuel 

consumption across the full range of engine operation, including at part-load and high-load 

conditions. 

The program team projected that in the 2020–2025 timeframe, spray-guided DI will be the 

mainstream DI technology in use, supplanting wall guided DI. Spray-guided DI offers the 

capability to deliver a stratified charge—where the fuel concentration decreases away from the 

spark plug—that will facilitate lower GHG emissions through lean-burn operation. 

For diesel engines, emissions requirements will cause the injection pressures to continue to 

increase to the 2000–2400 bar injection pressure range. These very high injection pressures 

support better combustion and reduced engine-out emissions. In addition, multiple injection 

events will be used to better control the onset and progress of the combustion event in the 

cylinder. 

4.1.3 Boosting System 

Using devices to boost the engine's intake pressure will increase the torque and power available 

from a given engine displacement. By increasing the boost pressure while decreasing engine 

displacement, the power level is maintained while reducing pumping work in the engine through 

shifting engine operation to higher-load operating points. 

The advanced engines in the 2020–2025 timeframe are expected to have advanced boosting 

systems to increase the pressure of the intake charge. Various boosting approaches are 

possible, such as superchargers, turbochargers, and electric motor-driven compressors and 

turbines. The appropriate technology for 2020–2025 will need to provide cost-effective 

improvement in performance and efficiency while mitigating turbo lag. 

Turbocharged engines in the 2020–2025 timeframe are expected to have an advanced boost 

strategy that mitigates turbo lag while providing a smooth acceleration feel. 
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The advanced engines with boost systems were assumed to have two-stage series sequential 

turbocharger systems. Turbocharging means that there is some risk of the vehicle performance 

being affected by turbo lag, a delay in the torque rise that results from the dynamics of the gas 

flow through the engine. Turbo lag is most significant during hard acceleration events, especially 

when the engine starts at or near its idle speed and load. Mitigating turbo lag means carefully 

choosing the capacities of the high pressure and low pressure compressors and turbines and 

connecting pipes to provide acceptable steady-state torque across the engine speed range and 

an acceptable transient rate of torque rise, often expressed as the time required to reach 85% of 

maximum torque at a given engine speed. Modeling turbo-lag effects is described in Section 

6.3, below. 

4.1.4 Other Engine Technologies 

Other engine technologies incorporated into the future engines were further improvements in 

engine friction leading to a global reduction in engine fuel consumption. This friction reduction is 

expected to result from a combination of technology advances, including piston ringpack, bore 

finish, lower-viscosity crankcase lubricants, low-friction coatings, valvetrain components, and 

bearing technology. The details of these improvements in engine friction were not explicitly 

itemized in this study, and were instead treated as a global engine friction reduction. 

Another approach is to optimize the overall engine design, for example, by combining engine 

components to reduce mass and thermal inertia, giving an improved package and faster warmup. Ancillary systems may also be electrified to remove the front engine accessory drive (FEAD) 

and allow variable accessory performance independent of engine speed. (See, for example, 

Section 4.5.2, Electric Power Assisted Steering.) The combination of components, such as the 

exhaust manifold and cylinder head design, should improve the response time for turbocharging 

and aftertreatment warm-up. Electrification of FEAD components, such as the electrical coolant 

pump, oil pump, or AC compressor, reduces parasitic losses on the engine and allows 

accessory operation to be optimized for the operating point independently of the engine. 

4.2 Engine Configurations 

Several engine configurations were defined using combinations of the advanced engine 

technologies described in Section 4.1 based on an assessment of what would be in mainstream 

use in the 2020–2025 timeframe. Five main types of engines were used in the study, and are 

described in this section. 

The engines considered for the 2020–2025 timeframe were developed using two main methods. 

The first method, used with the boosted SI engines, was to review the reported performance of 

current research engines, and assume that these current research engines would closely 

resemble the production engines of the 2020–2025 timeframe. With this approach, current 

research engines would be refined to meet production standards, including manufacturability, 

cost, and durability. The second method, used with the Atkinson and diesel engines, was to 

begin with current production engines and determine a pathway of technology improvements 

over the next 10–15 years that would lead to an appropriate engine configuration for the 2020–

2025 timeframe. With both methods, current trends in engine design and development were 

extrapolated to obtain an advanced concept performance for the 2020–2025 timeframe that 

should be achievable in production volumes. 

The combinations of technologies encompassed in each advanced engine concept provide 

benefits to the fueling map, or values of brake-specific fuel consumption (BSFC) over the 
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operating speed and load ranges of each engine. For these future engines, the BSFC is 

improved by up to 10%. Many of the engine concepts have low BSFC values over large zones 

of the engine operating map, with the best BSFC point often at part-load conditions when at 

lower speeds. 

4.2.1 Stoichiometric DI Turbo 

The basic advanced engine configuration is the Stoichiometric DI Turbo SI engine. This 

advanced engine assumes continued use of a stoichiometric air-fuel ratio for simplified 

aftertreatment using a three-way catalyst. The engine modeled has a peak brake mean effective 

pressure (BMEP) of 25–30 bar, which supports significant downsizing compared to current 2010 

engines. This high BMEP level is reached through a combination of engine technologies, 

including advanced valve actuation, such as CPS; spray-guided DI; and advanced boost 

systems, such as series-sequential turbochargers (see Sections 4.1.1, 4.1.2, and 4.1.3, 

respectively). 

Current research engines of this configuration have been developed by several groups. One 

example is the Sabre engine described by Coltman, et al. (2008) and by Turner, et al. (2009). 

MAHLE have also developed a Stoichiometric DI Turbo SI engine, described by Lumsden, et al. 

(2009). 

The future engine configuration was assumed to use a cooled exhaust manifold to keep the 

turbine inlet temperatures below 950°C over the full operating range of the engine to mitigate 

the need for upgraded materials in the exhaust manifold and turbine to accommodate higher 

exhaust gas temperatures. This design change allows the engine to operate with a 

stoichiometric air-fuel ratio over the complete operating map, even at high-speed, high-load 

operating conditions, which significantly improves the fuel consumption in this part of the 

operating map. 

4.2.2 Lean-Stoichiometric Switching 

The Lean-Stoichiometric DI Turbo SI engine configuration is similar in all respects to the 

Stoichiometric DI Turbo engine described above in Section 4.2.1, except that it uses a fuel-lean 

air-fuel ratio at moderate speeds and loads, such as those seen on the FTP75 cycle. 

Elsewhere, such as on the US06 cycle, the engine switches to stoichiometric operation to avoid 

exceeding the lean aftertreatment temperature limits. This mixed-mode operation allows the 

engine to take advantage of the efficiency benefits of lean operation while mitigating the 

technical challenges associated with lean-burn emissions control. 

Fuel lean operation improves fuel consumption by increasing the relative charge volume per unit 

of fuel burned. Nevertheless, lean operation leads to significant increases in engine-out nitrogen 

oxides (NOx) compared to stoichiometric operation, and therefore requires additional emissions 

control systems to remove NOx from net oxidizing exhaust gas, such as a lean NOx trap (LNT) 

or a urea-based selective catalytic reduction (SCR) system. The program team raised concerns 

about the effectiveness of these NOx removal systems at the high temperatures and exhaust 

gas flow rates, or space velocities, easily reached by SI engines at high engine speed or load, 

and also about catalyst durability under hot and oxidizing conditions over the vehicle life. These 

concerns suggest that meeting criteria pollutant levels over a drive cycle such as the US06 

could be challenging to the expected end of life, but advances would be made over the 

intervening years to make such systems production feasible. 
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Therefore, the engine switches to stoichiometric operation when the exhaust temperature 

crosses a threshold above which the NOx removal system catalysts would suffer accelerated 

degradation. At high load conditions, the exhaust emissions are treated using typical three-way 

catalysts. The engine therefore performs exactly like the Stoichiometric DI Turbo engine at 

higher load, but has improved BSFC at lower load because it switches to lean operation. A 

modest fuel consumption penalty is applied over each drive cycle to account for the use of fuel 

or other reducing agent to remove NOx during lean operation. 

4.2.3 EGR DI Turbo 

The EGR DI Turbo engine is also similar to the Stoichiometric DI Turbo Engine described in 

Section 4.2.1, except that it uses cooled external exhaust gas recirculation (EGR) to manage incylinder combustion and exhaust temperatures. The recirculated exhaust gas dilutes the air and 

fuel charge in the cylinder, thereby moderating the temperature during combustion and allowing 

operation without enrichment over the complete operating map. Additionally, the EGR reduces 

the need for throttling at low-load operation, reducing engine pumping losses. 

Dual high-pressure and low-pressure EGR loops were assumed for this engine configuration, 

which will require additional components such as EGR valves and a heat exchanger (EGR 

cooler) to manage the EGR flow and temperature. EGR allows a modest overall improvement in 

fuel consumption across the complete operating map compared to the Stoichiometric DI Turbo 

engine. 

4.2.4 Atkinson Cycle 

The Atkinson cycle is characterized by leaving the intake valves open during the start of the 

compression stroke, which lowers the effective compression ratio of the engine back to that of 

the normal SI engine, but allows for a larger effective expansion ratio. This change in engine 

operation improves fuel consumption, but penalizes torque availability at lower engine speeds. 

For this reason, Atkinson cycle engines are typically used only in hybrid vehicle applications, 

where the electric machine can be used to provide extra torque during launch or other hard 

acceleration events. 

Separate Atkinson cycle engine fueling maps were developed for the 2020–2025 timeframe with 

both CPS and DVA valvetrains. These engines are only used with the P2 parallel and Input 

Powersplit hybrid powertrains described in Section 4.3. The torque curve and fueling map thus 

generated also reflect so-called downspeeding, or a lower overall operating speed range, which 

yields further fuel consumption benefits by reducing frictional losses in the engine. 

4.2.5 Advanced Diesel 

The advanced diesel engines for the 2020–2025 timeframe were developed by starting with 

existing production engines and identifying technology advances that would lead to further 

improvements in fuel consumption. These technologies include many of the ones discussed in 

Section 4.1, as applied to diesel engines. 

This approach led to different maps being developed for each of the vehicle classes that had 

diesel engines available: the Small Car, Full Size Car, Large MPV, LDT, and LHDT. For 

example, the LHDT engine torque curve and fueling maps were generated by starting with a 

6.6 L diesel engine typical for this class and applying the benefits of improvements in pumping 

losses or friction to the fueling map. Engine displacements for the advanced diesels were 
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chosen based on the current torque and power levels available from these engines, the 

expected future requirements, and the effects of applying advanced technologies to support 

further downsizing, for example. Current diesel engines for LDVs already use advanced 

variable-geometry boost systems and high-pressure common-rail direct injection for better 

torque response and specific power. Improvements in these areas are therefore expected to be 

incremental, by contrast with the more extensive changes to SI engine architectures described 

above. For example, the peak BMEP of the advanced diesels is in the 17–23 bar range, which 

is noticeably lower than that expected for the advanced SI engines. This difference is, however, 

consistent with Ricardo's expectation of the pace and direction of technology development for 

diesel engines that comply with the expected emissions requirements defined in the study's 

ground rules defined in Section 3.2. 

4.3 Hybrid Technologies 

The selection of hybrid technology for a vehicle is complex, with an engineering trade-off 

between fuel consumption benefit and system complexity and cost. As hybrid vehicle market 

share continues to grow, consumers will have a range of choices. 

A wide range of hybrid configurations were considered in the initial part of the program, with the 

program studying three main approaches: micro hybrid (stop-start), P2 parallel, and Input 

Powersplit. For this study, it was assumed that the hybrid powertrain configurations will be 

studied in all but the LHDT vehicle class. 

4.3.1 Micro Hybrid: Stop-Start 

The most basic hybridization method shuts off the engine during idle periods, and typically uses 

an enhanced starter motor and limited use of driver comfort features during engine off, such as 

the radio and some heat but not air conditioning. This approach reduces fuel use over city drive 

cycles by minimizing idling, but provides no benefit for highway driving or when air conditioning 

is requested. 

The stop-start, micro hybrid approach is the lowest-cost hybrid system, and can be implemented 

relatively quickly on most vehicles on the market today. Stop-start systems are already in 

production and the technology is maturing. Further development will lead to increased user 

acceptance, for example, through transparent integration with low impact on vehicle 

performance or noise, vibration, and harshness (NVH). 

The program team has assumed that by the 2020–2025 timeframe, all vehicles with an 

otherwise conventional powertrain will have stop-start functionality implemented. For the vehicle 

models in this study, the starter motor does not provide motive power, but is capable of 

recovering enough energy to offset accessory loads. 

4.3.2 P2 Parallel Hybrid 

The P2 Parallel Hybrid powertrain places an electric machine on the transmission input, 

downstream of the engine clutch. This system allows stop-start, electrical launch, launch assist, 

and regenerative braking functionality. The clutch also allows the engine to be decoupled from 

the rear of the driveline, allowing pure electric propulsion, or electric vehicle (EV) mode 

operation. This wide application of electrical power in a variety of vehicle operating conditions 

facilitates downsizing the engine from that in the comparable conventional vehicle. 
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This hybrid powertrain is expected to significantly reduce GHG emissions, especially during city 

driving. Highway driving fuel consumption is expected to improve because the electric machine 

in the P2 hybrid allows for a smaller, more efficient internal combustion engine to be used. This 

smaller engine, however, may limit vehicle performance in situations requiring continuous 

engine power, such as a sustained hill climb. 

P2 Parallel hybrids are in limited production currently, including such vehicles as the Hyundai 

Sonata, the Porsche Cayenne, and the Volkswagen Touareg. Prototypes have also been built 

by various companies using existing off-the-shelf components. 

A P2 Parallel Hybrid system can be used with an automatic transmission, automated manual 

transmission (AMT), continuously variable transmission (CVT), or dual clutch transmission 

(DCT). Hellenbroich and Rosenburg (2009) describe a P2 variant with AMT, for example. For 

this program, the P2 Parallel Hybrid powertrain was modeled using the DCT, which has fixed 

gear ratios and no torque converter. 

4.3.3 Input Powersplit 

The simplest Powersplit hybrid configuration replaces the vehicle’s transmission with a single 

planetary gearset and two electrical machines connected to the planetary gearset. The 

planetary gearset splits engine power between the mechanical path and the electrical path to 

achieve a continuously variable transmission. In some Input Powersplit configurations, a second 

planetary gearset is used to speed up one of the electrical machines; however, the CVT 

functionality is still retained. The Toyota Prius and the Ford Hybrid Escape are two examples of 

Input Powersplit hybrid vehicles currently sold in the United States. 

With the appropriate electric accessories, the Input Powersplit system allows for EV mode 

operation, as well as stop-start operation, electric launch, launch assist, and regenerative 

braking. In addition, the system allows for engine downsizing to help reduce fuel consumption, 

even though the smaller engine may limit vehicle performance in situations requiring continuous 

engine power, such as a sustained hill climb. The Powersplit system provides significant 

improvements in fuel consumption in city driving. During highway cycles, the benefits of 

regenerative braking and engine start-stop are reduced, however, the CVT feature of the engine 

helps during the highway cycle as the engine is kept at an efficient operating point. 

4.4 Transmission Technologies 

The U.S. vehicle market is currently dominated by automatic transmissions, with a development 

emphasis on increasing the launch-assist device efficiency and on increasing the number of 

gear ratios to allow the engine to operate more frequently in regions of high efficiency. 

Nevertheless, dual clutch transmissions (DCT) are expected to be adopted over the next 10 to 

15 years because of their potential to further improve fuel economy and maintain drivability. 

CVTs tend to have higher friction than DCTs and provide a different driving experience than 

stepped transmissions. CVTs were not included in the scope of this study, even though they are 

a current production technology. 

The development of DCT technology is expected to be implemented in the U.S. based on 

experience with European and Japanese applications. Some vehicles with DCTs are entering 

volume production, such as the Ford Fiesta, Ford Focus, and VW Passat. Automatic 

transmissions also continue to be developed and refined, with new technologies being 

implemented in luxury vehicles and cascading down to other vehicle classes. Given that 94% of 
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current U.S. transmissions are automatics, efficiency improvements that mitigate GHG 

emissions are expected to come from the following: 

• Increased gear count from 4–6 currently to 7 or 8 by 2020–2025 

• Improved kinematic design 

• Component efficiency improvement or alternative technologies 

• Launch devices 

• Dry sump technology 

The various base transmission technologies are described, followed by launch device options, 

and, finally, other technologies expected to improve transmission efficiency. The effects of these 

various technologies on transmission efficiency were incorporated into the models. 

4.4.1 Automatic Transmission 

The automatic transmission is hydraulically operated, and uses a fluid coupling or torque 

converter and a set of gearsets to provide a range of gear ratios. Viscous losses in the torque 

converter decrease the efficiency of the automatic transmission. For the study timeframe, it was 

assumed that eight-speed automatic transmissions will be in common use, as this supports 

more efficient operation. The Small Car is an exception, and was assumed to only have enough 

package space to support a six-speed transmission. For the 2020–2025 timeframe, losses in 

advanced automatic transmissions are expected to be about 20–33% lower than in current 

automatic transmissions from the specific technologies described below. Additional benefits will 

be realized by having more gear ratios available to help maintain the engine near its best 

operating condition. 

4.4.2 Dual Clutch Transmission (DCT) 

The DCT has two separate gearsets operating in tandem, one with even gears and the other 

with odd. As the gear changes, one clutch engages as the other disengages, thereby reducing 

torque interrupt and improving shift quality, making it more like an automatic transmission. The 

DCT, however, does not require a torque converter which improves its efficiency compared to 

an automatic transmission, and may use either wet or dry type launch clutches. For the 2020–

2025 timeframe, energy losses in both wet clutch and dry clutch DCTs are expected to be 40–

50% lower than in current automatic transmissions. Additional benefits will be realized by having 

more gear ratios available to help keep the engine near its best operating condition. 

4.4.3 Launch Device: Wet Clutch 

A wet clutch provides torque transmission during operation by means of friction action between 

surfaces wetted by a lubricant. The lubricant is required for cooling during gear shifts when the 

clutch is slipping in larger LDV classes. As a secondary lubrication system is needed for the 

actuation requirements, wet clutch systems are expected to be heavier, cost more, and be less 

efficient than dry clutch systems. 

By the 2020–2025 timeframe, wet clutch DCTs are expected to develop into so-called damp 

clutch DCTs, since it approaches the efficiency of a dry clutch with the longevity and higher 

torque capacity of a wet clutch. In damp clutch DCTs, a limited spray is applied to cool the 

clutch materials. A damp clutch requires a lubrication system but is more efficient due to 

improved control, leading to reduced windage and churning losses. 
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4.4.4 Launch Device: Dry Clutch Advancements 

The standard dry clutch requires advanced materials to dissipate heat and prevent slipping. The 

thermal load resulting from engagement prevents dry clutches from being used in high torque 

and heavy duty cycle applications, even though they are more efficient since they significantly 

reduce parasitic shear fluid losses and do not require an additional lubrication system. The GHG 

emissions benefit of a dry clutch over a wet clutch should be realized at launch and during 

transient driving, thus primarily for city driving. Advancements in materials or electric assist 

could enable this technology to be used in larger LDVs and more severe duty cycles by the 

study timeframe, but is generally assumed to be prevalent in the smaller vehicle classes. 

4.4.5 Launch Device: Multi-Damper Torque Converter 

Dampers added to the torque converter enable a lower lockup speed, therefore decreasing the 

more fuel-intensive period of hydrodynamic power transfer. Multi-damper systems provide 

earlier torque converter clutch engagement; however, drivability and limited ratio coverage have 

limited the deployment of this technology to date. The technology must be integrated during 

transmission design. The GHG emissions benefit should come from reduced slippage and 

smoother shifting. 

4.4.6 Shifting Clutch Technology 

Shift clutch technology improves the thermal capacity of the shifting clutch to reduce plate count 

and lower clutch losses during shifting. Reducing the number of plates for the shifting process 

and reducing the hydraulic cooling requirements will increase the overall transmission efficiency 

for similar drivability characteristics. Technology deployment has been limited by industry 

prioritization of drivability over shift efficiency, especially since shift events are a very small 

portion of typical driving. The technology will be best suited to smaller vehicle segments 

because of reduced drivability expectations—this technology may not be suitable for higher 

torque applications. 

4.4.7 Improved Kinematic Design 

Improved kinematic design uses analysis to improve the design for efficiency by selecting the 

kinematic relationships that optimize the part operational speeds and torques. Large 

improvements in efficiency have been noted for clean sheet designs for six-speed and eightspeed transmissions. This approach will provide a GHG emissions benefit across all vehicle 

classes and operating conditions. 

4.4.8 Dry Sump 

A dry sump lubrication system provides benefits by keeping the rotating members out of oil, 

which reduces losses due to windage and churning. This approach will provide a GHG 

emissions benefit across all vehicle classes, with the best benefits at higher speeds. 

4.4.9 Efficient Components 

A continuous improvement in seals, bearings and clutches all aimed at reducing drag in the 

system should provide GHG emissions benefits without compromising transmission 

performance. 
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4.4.10 Super Finishing 

This technology approach chemically treats internal gearbox parts for improved surface finish. 

The improved surface finish reduces drag which increases efficiency. 

4.4.11 Lubrication 

New developments in base oils and additive packages will reduce oil viscosity while maintaining 

temperature requirements, thereby improving transmission efficiency. 

4.5 Vehicle Technologies 

Several vehicle technologies were also considered for the study to the extent that they help 

support future ranges of vehicle mass, aerodynamic drag, and rolling resistance for each of the 

vehicle classes in the study. 

Technologies considered include mass reduction through use of advanced materials with a 

higher strength to mass ratio and through consolidation and optimization of components and 

systems. Aerodynamic drag is expected to see improvements through adoption of both passive 

and active aerodynamic features on vehicles in the 2020–2025 timeframe. Continued 

improvement in tire design is expected to reduce rolling resistance and thereby provide a benefit 

to fuel consumption. 

In addition, vehicle accessory systems such as the cooling pumps and power steering systems 

are expected to become electrified by the 2020–2025 timeframe. These electrified accessories 

should reduce the power required to keep them active, which will also improve fuel 

consumption, and are described in greater detail below. 

4.5.1 Intelligent Cooling Systems 

Intelligent cooling systems use an electric coolant pump to circulate engine coolant, removing 

the power required for this pump from the FEAD. Removing the coolant pump from the FEAD 

also enables independent pump speed control. Rather than running at a fixed multiple of the 

engine speed, the coolant pump can spin at the appropriate speed for the current cooling 

requirements. Standard cooling systems are sized to provide cooling at maximum load and 

ambient conditions, but most vehicles only rarely operate under these extreme conditions. 

Intelligent cooling also enables quicker warm-up of the engine by controlling coolant flow. This 

reduces engine friction by increasing engine temperature during the warm up process. 

Ricardo estimates this technology will lower fuel consumption over the FTP cycle. BMW is 

implementing this technology on their twin-turbo 3-L inline-6 cylinder engine, introduced in 2007 

in their 335i model. This technology is projected to be readily available by the 2020–2025 

timeframe. 

4.5.2 Electric Power Assisted Steering 

Electric Power Assisted Steering (EPAS) uses either rack or column-drive electric motors to 

assist driver effort instead of a hydraulic power assist system. EPAS replaces the engine-driven 

hydraulic pump, hydraulic hoses, fluid reservoir, fluid, and hydraulic rack. The efficiency of this 

system is a result of reduced FEAD losses and improved energy management that comes from 

decoupling the load from the engine. This technology is currently available for small and 
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medium sized passenger vehicles, and it is likely that this will be commercially available for 

LDVs up to the LDT class by the 2020–2025 timeframe. This technology is required for vehicles 

with any electrical launch or EV mobility, so that the vehicle can be steered during EV mode. 

5. TECHNOLOGY BUNDLES AND SIMULATION MATRICES 

The program team and external stakeholders bundled the technologies described in Section 4, 

"Technology Review and Selection," into a set of technology packages to be evaluated in the 

seven LDV classes described in Section 2.2, "Ground Rules for Study". These LDV classes are 

Small Car, Standard Car, Small MPV, Full Size Car, Large MPV, LDT, and LHDT. 

5.1 Technology Options Considered 

Definitions of the hybrid powertrain, engine, and transmission technology options are presented 

in Tables 5.1–5.4. The engine technologies are defined in Table 5.1; hybrids, in Table 5.2; and 

transmissions, in Table 5.3. Many of the engines in Table 5.1 use some measure of internal 

EGR, but for this table "Yes" means significant EGR flow through an external EGR system. All 

of the advanced transmissions in Table 5.3 include the technologies described in Section 4.4, 

including dry sump, improved component efficiency, improved kinematic design, super finish, 

and advanced driveline lubricants. 

T1Table 5.1: Engine technology package definition. 

T2Table 5.2: Hybrid technology package definition. 

Powertrain Configuration

Function 2010 Baseline Stop-Start P2 Parallel Powersplit

Engine idle-off No Yes Yes Yes 

Launch assist No No Yes Yes 

Regeneration No No Yes Yes 

EV mode No No Yes Yes 

CVT (Electronic) No No No Yes 

Power steering Belt Electrical Electrical Electrical 

Engine coolant pump Belt Belt Electrical Electrical 

Air conditioning Belt Belt Electrical Electrical 

Brake Standard Standard Blended Blended 

EGR CPS DVA

2010 Baseline NA PFI No No No

Stoich DI Turbo Boost DI No Yes No

Lean-Stoich DI Turbo Boost DI No Yes No

EGR DI Turbo Boost DI Yes No No

Atkinson NA DI No Yes Yes

Diesel Boost DI Yes Yes No

Engine

Fuel 

Injection

Air Valvetrain

System
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T3Table 5.3: Transmission technology package definition. 

Transmission Launch Device Clutch

Baseline Automatic Torque Converter Hydraulic 

Advanced Automatic Multidamper Control Hydraulic 

Dry clutch DCT None Advanced Dry 

Wet clutch DCT None Advanced Damp 

5.2 Vehicle configurations and technology combinations 

Vehicles were assessed using three basic powertrain configurations: conventional stop-start, P2 

hybrid, and Input Powersplit hybrid. Each vehicle class considered in the study was modeled 

with a set of technology options, as shown in Tables 5.4 and 5.5. Each of the 2020 engines 

marked for a given vehicle class in Table 5.4 was paired with each of the advanced 

transmissions marked for the same vehicle class. 

T4Table 5.4: Baseline and Conventional Stop-Start vehicle simulation matrix. 

 

Vehicle Class

 Stoich DI Turbo with CPS Lean DI Turbo with CPS EGR DI Turbo with CPS 2020 Diesel 6-Speed Automatic 6-Speed Dry DCT 8-Speed Automatic 8-Speed Dry DCT 8-Speed Wet DCT

Small Car X X X X X X X

Standard Car X X X X X X

Small MPV X X X X X X

Full Size Car X X X X X X X

Large MPV X X X X X X X

LDT X X X X X X X

LHDT X X X X X X X X

Advanced Engine Advanced Transmission

Baseline Engine & 2010 6-Speed Automatic Trans.2010 Diesel & 2010 6-Speed Automatic Transmission

Parameter

Engine Displacement 50 125

Final Drive Ratio 75 125

Rolling Resistance 70 100

Aerodynamic Drag 70 100

Mass 60 120

DoE Range (%)
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TT5Table 5.5: P2 and Input Powersplit hybrid simulation matrix. 

6. VEHICLE MODEL 

Vehicle models were developed to explore the complete design space defined by the 

technologies, vehicle classes, and powertrain architectures included for the 2020–2025 

timeframe. The modeling process started by developing baseline models to compare against 

data for current (2010) vehicles. A detailed comparison between baseline model results and 

vehicle test data were used to validate the models. 

6.1 Baseline Conventional Vehicle Models 

For each of the seven LDV classes considered in this project, vehicle models were developed 

for a 2010 baseline case. Each LDV class was assigned a representative vehicle for the 

purposes of establishing a baseline against known vehicle data. 

A complete, physics-based vehicle and powertrain system model was developed and 

implemented in MSC.Easy5TM. MSC.Easy5TM is a commercially available software package 

widely used in industry for vehicle system analysis, which models the physics in the vehicle 

powertrain during a drive cycle. Torque reactions are simulated from the engine through the 

transmission and driveline to the wheels. The model reacts to simulated driver inputs to the 

accelerator or brake pedals, thus enabling the actual vehicle acceleration to be determined 

based on a realistic control strategy. The model is divided into a number of subsystem models. 

Within each subsystem the model determines key component outputs such as torque, speeds, 

Vehicle Class

 P2 Hybrid with 2020 DCT Input Powersplit Stoich DI Turbo with CPS Lean DI Turbo with CPS EGR DI Turbo with CPS Atkinson with CPS Atkinson  with DVA

Small Car X X X X X X X

Standard Car X X X X X X X

Small MPV X X X X X X X

Full Size Car X X X X X X X

Large MPV X X X X X X X

LDT X X X X X X

LHDT

Hybrid Architecture Advanced Engine

Engine Displacement 50 150 50 125

Final Drive Ratio 75 125 75 125

Rolling Resistance 70 100 70 100

Aerodynamic Drag 70 100 70 100

Mass 60 120 60 120

Electric Machine Size 50 300 50 150

P2 Hybrid Powersplit

DoE Range (%)

Parameter
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and heat rejection, and from these outputs, appropriate subsystem efficiencies can be 

calculated or reviewed as part of a quality audit. 

The seven vehicle classes considered in this study are shown in Table 6.1, along with the 

baseline vehicles for each class. Each of the baseline exemplar vehicle models had vehiclespecific vehicle, engine, and transmission model parameters. The models were exercised over 

the FTP75 and HWFET fuel economy drive cycles, and the results compared with the EPA 

Vehicle Certification Database (Test Car List) fuel economy data for each of the baseline 

exemplar vehicles. 

T6Table 6.1: Vehicle classes and baseline exemplar vehicles. 

Vehicle Class Baseline Exemplar

Small car Toyota Yaris 

Standard car Toyota Camry 

Small MPV Saturn Vue 

Full sized car Chrysler 300 

Large MPV Dodge Grand Caravan 

LDT Ford F150 

LHDT Chevy Silverado 3500HD 

6.2 Baseline Hybrid Vehicle Models 

For each hybrid technology, Ricardo developed a baseline model to calibrate the hybrid control 

strategy and vehicle, engine, and driveline parameters. As with the conventional vehicles 

described in Section 6.1, a full physical model of each baseline hybrid exemplar vehicle was 

developed and implemented in MSC.Easy5TM. The hybrid control algorithms are also 

implemented in the respective MSC.Easy5TM models. 

The vehicles were modeled using published information from various sources and Ricardo 

proprietary data. Each of the baseline exemplar hybrid vehicle models had vehicle-specific 

vehicle, engine, and transmission model parameters. The exemplar hybrid vehicles are listed in 

Table 6.2, along with the exemplar used to confirm the DCT powertrain. 

T7Table 6.2: Advanced powertrain configurations and baseline exemplar vehicles. 

Powertrain Configuration Exemplars

DCT (conventional) Audi A3 / VW Passat 

P2 Hybrid Hyundai Sonata Hybrid 

Input Powersplit Ford Escape Hybrid 

6.3 Engine Models 

The engines considered in the design space are defined by their torque curve, fueling map, and 

other input parameters. For the 2010 baseline vehicles, the engine fueling maps and related 

parameters were developed for each specific baseline exemplar vehicle. For the engines used 

in the 2020–2025 vehicles, reference engine models were developed and scaled to each of the 

LDV classes. 

As described in Section 4.2, the program used two methods to develop the engine models for 

the 2020–2025 timeframe. The first was to look at the reported performance of current research 

engines, and assume that these current research engines would closely resemble the 
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production engines of the 2020–2025 timeframe. With this method, current research engines 

would be refined to meet production standards, including manufacturability, cost, and durability. 

The second method was to begin with current production engines and determine a pathway of 

technology improvements over the next 10–15 years that would lead to an appropriate engine 

configuration for the 2020–2025 timeframe. 

The fueling maps and other engine model parameters used in the study were based on 

published data and Ricardo proprietary data. These initial maps were developed into a map 

reflecting the effects on overall engine performance of the combination of the future 

technologies considered. Each proposed map was reviewed and approved by EPA and the 

Advisory Committee. This process was repeated for each of the engine technologies included in 

the simulation matrix, as shown in Tables 5.4 and 5.5 for conventional stop-start and hybrid 

powertrain configurations, respectively. 

Engine downsizing effects were captured by changing the engine displacement in the given 

vehicle. This approach assumes that the downsized engines have the same brake mean 

effective pressure (BMEP), which scales the engine's delivered torque by the engine swept 

volume, or displacement. The BSFC of the scaled engine map is also adjusted by a factor that 

accounts for the change in heat loss that comes with decreasing the cylinder volume, and 

thereby increasing the surface to volume ratio of the cylinder. The minimum number of cylinders 

in an engine was set to three, and the minimum per-cylinder volume, to 0.225 liters. These 

constraints established the minimum engine displacement in the design space to 0.675 liters. 

Engine efficiency is therefore assumed to be a function of engine speed and BMEP, with 

specific fueling rates (mass per unit time) calculated from the torque. Thus, downsizing the 

engine directly scales the delivered torque, and the fueling map is adjusted accordingly. The 

engine speed range was held constant over the engine displacement ranges of interest. 

Turbo lag was represented in the model by applying a first order transfer function between the 

driver power command and the supplied engine power at a given speed. This transfer function 

was only used during the performance cycle, which is a hard acceleration from a full stop used 

to assess vehicle acceleration performance. The transfer function approximates the torque rise 

rate expected in the engines with turbocharger systems during vehicle launch. Adjusting the 

time constant in the transfer function allowed the acceleration performance to see the effect of 

turbo lag. A time constant of 1.5 seconds was selected to represent the expected delay in 

torque rise on the advanced, boosted engines from the spool up of the turbine. 

6.3.1 Warm-up Methodology 

A consistent warm-up modeling methodology was developed for the study to account for the 

benefits of an electrical water pump and of warm restart for the advanced vehicles. To account 

for engine warm-up effects, Ricardo used company proprietary data to develop an engine warmup profile. This engine warm-up profile is used to increase the fueling requirements during the 

cold start portion of the FTP75 drive cycle. This correction factor for increased fueling 

requirements is applied to the fuel flow calculated during the warm-up period in the FTP75 drive 

cycle. 

6.3.2 Accessories Models 

Parasitic loads from the alternator were assumed constant over the drive cycles and were 

included in the engine model. Alternator efficiency was assumed to be 55% for baseline vehicle 
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simulations and 70% for the high efficiency alternator in all of the advanced technology package 

simulations to represent future alternator design improvements. 

Power-assisted steering (PAS) systems—full electric or electric hydraulic—were modeled as 

being independent of engine speed and were included in the engine model for each baseline 

vehicle. The EPAS systems assumed no engine parasitic loads on the EPA drive cycles and 

acceleration performance cycles, which require no steering input. All advanced package 

simulations included the benefit of EPAS. The LHDT and LDT classes used electric hydraulic 

PAS, whereas the five smaller vehicle classes used full electric PAS. 

The LDT and LHDT models also include engine parasitic losses due to a belt-driven engine 

cooling fan. The other vehicles were assumed to have electric radiator fans, with the load being 

drive cycle dependent and added to the vehicle’s base electrical load. 

Current production cars have begun incorporating advanced alternator control to capture 

braking energy through electrical power generation. This is done by running the alternator near 

or at full capacity to apply more load on the engine when the driver demands vehicle 

deceleration. It is believed that this feature will be widespread in the near future and, hence, the 

study captures it by incorporating this function into the Conventional Stop-Start model. For 2020 

vehicle configurations, the alternator efficiency was increased to 70% to reflect an improved 

efficiency design. The advanced alternator control strategy monitors vehicle brake events and 

captures braking energy when available. The control strategy also limits the maximum power 

capture to 2800 Watts based on the assumption that the advanced alternator is limited to 200 

Amps at 14 Volts charging. By integrating power, energy is accumulated from every brake event 

and when there is available “stored” brake energy, the control strategy switches the parasitic 

draw from the engine to the battery until the accrued energy is consumed, at which point the 

load switches back to the engine. For the five smaller LDV classes, both the fan and base 

electrical loads are included in the advanced charging system as electric fans are employed. 

The system will only benefit the two truck classes, LDT and LHDT, in terms of base electrical 

load as these vehicle classes use mechanical fans. 

6.4 Transmission Models 

Efficiencies for each gear ratio were calculated based on data from several transmission and 

final drive gear tests. Different efficiency curves were mapped for planetary, automatics, and 

dual-clutch, with the DCT efficiency modified depending on whether a dry or wet clutch is used. 

Hydraulic pumping losses were included in the efficiency calculations. Transmission efficiencies 

were calculated to represent the average of the leading edge for today’s industry and not one 

particular manufacturer’s design. Advanced automatic transmission designs are projected to 

reduce losses by 20–33% from current automatic transmissions. In addition, the advanced 

automatic transmissions use advanced torque converters, described in Section 6.5, below. Wet 

clutch DCT efficiencies are also projected to approach current dry clutch DCT efficiencies. 

In anticipation of future technology packages, it is expected that some advanced level of 

transmission shift optimization will be implemented in year 2020–2025 vehicles. For the 2020-

2025 Conventional Stop-Start architecture, an advanced transmission option was implemented 

to determine the most favorable gear for a given driver input and vehicle road load. This 

approach takes the place of predefined calibration shift maps based on throttle and vehicle 

speed. These strategies presently cause significant implications for drivability and hence affect 

consumer acceptability. Nevertheless, it was assumed that by 2020, manufacturers will develop 

a means of yielding the fuel economy benefit without adversely affecting acceptability. 
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The advanced transmission shift optimization strategy attempts to keep the engine operating 

near its most efficient point for a given power demand. In this way, the new shift controller 

emulates a traditional CVT by selecting the best gear ratio for fuel economy at a given required 

vehicle power level. Gear efficiency of the desired gear is also taken into account. More often 

than not, the optimal gear ratio will be between two of the fixed ratios, and the shift optimizer will 

decide when to shift up or down based on a tunable shift setting. This will enable the shift 

optimizer to make proper shift decisions based on the type of vehicle and the desired 

aggressiveness of the shift pattern. To protect against operating conditions out of normal range, 

several key parameters were identified, such as maximum engine speed, minimum lugging 

speed, and minimum delay between shifts. For automatic transmissions, the torque converter is 

also controlled by the shift optimizer, with full lockup only achievable when the transmission is 

not in 1st gear. During development of this strategy, it was noted that fuel economy benefits of 

up to 5% can be obtained when compared to traditional shift maps. Figure 6.1 shows a 

comparison between the shift optimizer strategy and a CVT. 

F2Figure 6.1: Comparison of CVT and optimized DCT gear ratios over drive cycle. 

6.5 Torque Converter Models 

Torque converter characteristics curves for torque ratio and K-factor were generated using 

typical industry standards for efficiency. Each vehicle’s torque converter characteristics for 

torque ratio and K-factor were tailored for the application based on Ricardo experience. Impeller 

and turbine rotational inertias are also input to the model and were estimated based upon 

Ricardo experience. Vehicle simulations with advanced automatic transmissions include a slight 

improvement in torque converter efficiency. 

A lockup clutch model was used with all torque converters and was of sufficient capacity to 

prevent clutch slip during all simulation conditions. Lockup was allowed in 3rd and 4th gears with 

the 4-speed automatics; 3rd, 4th, and 5th gears with the 5-speed automatics; and 4th, 5th, and 6th

CVT DCT (shift optimizer) 
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gears with the 6-speed automatics. During light throttle conditions a minimum engine operating 

speed of 1400 rpm for I3 engines, 1300 rpm for I4 engines, 1200 rpm for V6 engines, and 1100 

rpm for V8 engines with the converter clutch locked was considered in developing the baseline 

lock/unlock maps. The advanced automatic transmission applications allow torque converter 

lockup in any gear except first gear. 

6.6 Final Drive Differential Model 

Baseline final drive ratios were taken from published information and driveline efficiencies and 

spin losses were estimated based upon Ricardo experience for typical industry differentials. The 

spin losses of the 4-wheel-drive LDT and LHDT front axle and transfer case were included in 

the model to capture the fuel economy and performance of the 4-wheel-drive powertrain 

operating in 2-wheel-drive mode. This approach is similar to the EPA procedure for emissions 

and fuel economy certification testing. 

6.7 Driver Model 

The vehicle model is forward facing and has a model for the driver. The driver model applies the 

throttle or brake pedal as needed to meet the required speed defined by the vehicle drive cycle 

within the allowed legislative error. This allows the modeling of the actual vehicle response to 

meet the target drive cycle. 

The driver model contains the drive cycle time/velocity trace, controls for the throttle and brake 

functions and maintains vehicle speed to the desired set point. Vehicle simulations for fuel 

economy were conducted over the EPA FTP75 (city), HWFET (highway) and US06 drive cycles. 

The FTP75 cycle consists of three "bags" for a total of 11.041 miles on the conventional 

vehicles and an additional bag 4 on hybrid vehicles for a total of 14.9 miles. A ten minute 

engine-off soak is performed between bags 2 and 3 (after 1372 seconds of testing). A bag 1 

correction factor is applied to the simulated "hot" fuel economy result of the vehicles to 

approximate warm-up conditions of increased friction and sub-optimal combustion. The 

correction factor reduces the fuel economy results of the FTP75 bag 1 portion of the drive cycle 

by 20% on the current baseline vehicles and 10% on 2020–2025 vehicles that take advantage 

of fast warm-up technologies. 

6.8 Hybrid Models 

The hybrid models include all of the conventional vehicle components with the addition (or 

replacement) of components for electric motor-generators, high voltage battery, high voltage 

battery controller/bus, transmission, regenerative braking and hybrid supervisory controller. Of 

these, the critical systems for the model were the electric machines (motor-generators), power 

electronics, and high-voltage battery system. For each of these systems, current, state of the art 

technologies were adapted to an advanced, 2020–2025 version of the system, such as by 

lowering internal resistance in the battery pack to represent 2010 chemistries under 

development and decreasing losses in the electric machine and power electronics to represent 

continued improvements in technology and implementation. 

In addition, a Ricardo proprietary methodology was used to identify the best possible fuel 

consumption for a given hybrid powertrain configuration over the drive cycles of interest: FTP, 

HWFET, and US06. The methodology used the drive cycle profile to identify the features of a 

control strategy that provide the lowest possible fuel consumption over the drive cycle. The 

result of this assessment enabled the development of an optimized control system. The 
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simulation results using the hybrid controller were compared against the best case scenario 

from the methodology to ensure that the hybrid controller in the models is obtaining the most out 

of the hybrid powertrain. 

A key feature of the hybrid controller is that it used a hybrid load following and load averaging 

strategy to help keep the engine on or near its line of best efficiency on the engine operating 

map, with some accommodation for the efficiency of the overall powertrain. During low-load 

conditions, the engine can be made to work harder and more efficiently and to store the excess 

energy in the battery. In other cases, the energy in the battery can be used to provide launch 

assist or EV mode driving. All hybrid vehicle simulations were SOC neutral over the drive cycle, 

so that there is no net accumulation or net depletion of energy in the battery; thus, fuel 

consumption is an accurate measure of the effectiveness of technologies. 

7. MODEL VALIDATION RESULTS 

Before executing the DoE simulation matrix, the vehicle models described in Section 6 were 

validated. Baseline vehicles were modeled, and the simulation results compared against 

publicly available data on vehicle performance, including acceleration times and fuel economy. 

Details of the model validation process and results are presented below. In addition, nominal 

runs were prepared for each major powertrain type to provide a reference point for the input 

parameters against which to compare the full design space explored in the DoE simulation 

matrix. 

7.1 Baseline Conventional Vehicle Models 

Vehicle models were developed for a 2010 baseline case for each of the seven LDV classes. 

Each LDV class was assigned a representative vehicle for the purposes of establishing a 

baseline against known vehicle data. Ricardo leveraged the peer-reviewed baseline models 

from its 2008 study with Perrin Quarles Associates (PQA, now part of SRA) for the five LDV 

classes from Standard Car through LDT to provide the 2010 baseline case, and to build new 

baseline models for the Small Car and LHDT classes. The 2010 baseline vehicles use sixspeed automatic transmissions and the engines with comparable displacement and peak torque 

to the exemplar vehicles listed in Table 6.1. 

Vehicle performance simulation results are shown in Table 7.1, below, comparing the raw fuel 

economy results in the EPA Test Car List (EPA, 2010) against the calculated results. The 

results were considered acceptably close. In addition to the fuel economy tests, the launch 

performance was also assessed for each of the exemplar vehicles, with particular attention paid 

to the 0–60 mph acceleration time, as this is readily available for validation. 0–60 mph 

acceleration times for the exemplar models were within a few tenths of a second of published 

times for each vehicle. 
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T8Table 7.1: Baseline vehicle fuel economy performance. 

7.2 Nominal Runs 

Once the models were developed and validated, a series of nominal runs were prepared to 

assess the accuracy and robustness of the model. The nominal conditions are the reference 

point for the design space explored by the DoE simulation. 

For the conventional vehicles, the nominal condition was calculated using the same vehicle 

parameter values, such as for mass and aerodynamic drag, as the 2010 baseline vehicles. The 

advanced engine size was adjusted to match the baseline 0–60 mph acceleration time. For the 

hybrids, the engine size was reduced 20% from the corresponding conventional nominal size, 

and the electric machine sized to again match the baseline 0–60 mph acceleration time. 

The full table of nominal runs results for the conventional stop/start, P2 hybrid, and Input 

Powersplit hybrid vehicle combinations is in Appendix 3. These summary results and the rest of 

the simulation output data were used to assess the quality of the simulation results before 

executing the DoE simulation matrix, for example, by assessing power flows to and from the 

battery over the drive cycle. 

8. COMPLEX SYSTEMS MODEL (CSM) VALIDATION 

CSM is an objective, scientific approach for evaluating several potential options or 

configurations for benefits relative to each other and to a baseline. For this program, the CSM 

methodology was used to define the design space for LDVs in the 2020–2025 timeframe, and 

then to effectively evaluate LDV performance over this large design space. 

8.1 Evaluation of Design Space 

The purpose of the DoE simulation matrix is to efficiently explore the potential design space for 

LDVs in the 2020–2025 timeframe. The simulation matrix was designed to generate selected 

performance results, such as fuel consumption or acceleration times, over selected drive cycles. 

The DoE approach allows an efficient exploration of the design space while limiting the number 

of runs needed to survey the design space. 

For each discrete combination of vehicle class, powertrain architecture, engine, and 

transmission in the design space, the continuous input variables were varied over the ranges 

shown in Tables 8.1 and 8.2 for the conventional and hybrid powertrains, respectively. In the 

analysis, continuous input variables are evaluated using a combination of the design corner 

FTP75 HWFET US06 FTP75 HWFET US06

Small car Toyota Yaris 38 50 32 40 49 30

Standard car Toyota Camry 27 42 26 30 44 29

Small MPV Saturn Vue 24 37 23 24 34 24

Full sized car Chrysler 300 21 34 21 24 36 24

Large MPV Dodge Grand Caravan 20 32 21 22 31 21

LDT Ford F150 16 23 13 16 26 15

LHDT Chevy Silverado 

3500HD (diesel)

— — — 16 19 12

EPA Test List Fuel Econ (mpg) Calculated Raw Fuel Econ (mpg)

Vehicle Class Baseline Exemplars
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points in a two-level full factorial design and design points within the space based on a Latin 

hypercube sampling methodology. Note that vehicle mass is considered independently of the 

combination of discrete technologies; for example, switching from an automatic transmission to 

a DCT does not automatically adjust the vehicle mass in the simulation. 

T9Table 8.1: Continuous input parameter sweep ranges with conventional powertrain. 

T10Table 8.2: Continuous input variable sweep ranges for P2 and Powersplit hybrid 

powertrains. 

 

Latin hypercube sampling is a statistical method originally developed by McKay et al. (1979), 

used to generate a set of parameter values over a multidimensional parameter space. The 

method randomly samples the multidimensional parameter space in a way that provides 

comprehensive and relatively sparse coverage for best efficiency. It also allows one to efficiently 

continue to fill the multidimensional parameter space by further random sampling. It provides 

more flexibility than traditional multi-level factorial designs for assessing a large parametric 

space with an efficient number of experiments. 

The vehicle simulations were run in batches and the results were collected and processed. 

Vehicle fuel economy and performance metrics were recorded as well as diagnostic variables 

such as the total number of gear shifts and the distance traveled during the drive cycle. The 

data were reviewed using a data mining tool and outliers were analyzed, and, as necessary, 

debugged and re-run. This approach allowed issues to be detected and diagnosed very quickly 

within a large amount of data. Once the data were reviewed and approved, response surface 

models were generated. 

8.2 Response Surface Modeling 

RSM were generated in the form of neural networks. The goal was to achieve low residuals 

while not over-fitting the data. Initially, 66% of the data were used for fitting the model while the 

remainder was used to validate the response surface model’s prediction performance. Once a 

good fit was found, all the data was used to populate the RSM. Each neural network fit contains 

Parameter

Engine Displacement 50 125

Final Drive Ratio 75 125

Rolling Resistance 70 100

Aerodynamic Drag 70 100

Mass 60 120

DoE Range (%)

Engine Displacement 50 150 50 125

Final Drive Ratio 75 125 75 125

Rolling Resistance 70 100 70 100

Aerodynamic Drag 70 100 70 100

Mass 60 120 60 120

Electric Machine Size 50 300 50 150

P2 Hybrid Powersplit

DoE Range (%)

Parameter
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all of the continuous and discrete variables used in the study for a given transmission. One 

Neural Network fit per transmission was generated to improve the quality of the fits. 

9. RESULTS 

The key project results consist of the raw data sets obtained from over 350,000 individual 

vehicle simulation cases, the Data Visualization Tool developed to query the response surfaces 

based upon the raw data sets, and this report describing these results. These results are 

discussed below. 

9.1 Basic Results of Simulation 

Each of the simulation cases generated data at 10 Hz which allowed evaluation of the 

performance of a specific vehicle configuration in the design space over each of the drive 

cycles. These results include parameters such as vehicle speed, calculated engine power, and 

instantaneous fueling rate. The detailed data from each simulation run were distilled into the 

main output factors of interest, such as acceleration time and fuel economy, used in the 

parametric fit of the RSM. 

For this study, the main output factors include raw fuel economy and GHG emissions over each 

of the drive cycles studied and also performance metrics, such as 0–60 mph acceleration times. 

The complete list of output factors is listed in Appendix 2. 

9.2 Design Space Query 

The Design Space Query within the Data Visualization Tool allows the user to assess a specific 

vehicle configuration in the design space by selecting a platform, engine, and transmission and 

setting the continuous variables within the design space range. The generated performance 

results are reported in a table that is exportable to Excel. The user can assess multiple vehicle 

configurations and compare them in Excel. The tool table also allows the user to apply 

spreadsheet formulas for quick, on-the-side computation. An example of the Design Space 

Query is shown in Figure 9.1. 

9.3 Exploration of the Design Space 

A more comprehensive survey of the design space can be conducted using the Design Space 

Analysis in the Data Visualization Tool, which allows the user to assess the performance of 

multiple vehicle configurations from a significant portion of the design space simultaneously. 

Each design is generated by first selecting a vehicle platform, engine, and transmission, and 

then ranges for the continuous input variables. Figure 9.2 shows the screen where the design 

space analysis is set up. For each of the continuous variables, values are generated using a 

Monte Carlo analysis from a uniform distribution over the range selected. These data are stored, 

and may be exported or plotted. 

Once generated, the design points are stored and may be plotted to visualize the tradeoff 

analysis of the design space. By carefully building a design and varying the parameters, the 

user can gain an understanding of the effect of each technology and the interactions between 

technologies. Figures 9.3–9.5 show examples of plots that compare two design space analyses. 

In these cases, the red points are for a Full Size Car with advanced diesel engine and dry-clutch 

DCT, whereas the blue points are for a Full Size Car with stoichiometric DI turbo engine and 
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automatic transmission. The black point is the 2010 baseline value. For these examples, the 

engine displacement was varied from 50% to 125% of nominal, or 0.71 to 1.8 L displacement for 

the stoichiometric DI turbo engine and 1.4 to 3.6 L for the diesel, and the vehicle mass, from 

70% to 100% of nominal, or 2800 to 4000 kg. 

The example in Figure 9.6 compares various configurations of the Standard Car, all with the 

EGR DI Turbo engine but with different powertrains. The two Conventional Stop-Start cases 

have the advanced eight-speed automatic and dry-clutch DCT, shown in blue and gray, 

respectively. The Powersplit hybrid is shown in green, and the P2 Hybrid, in red. Again, the 

black point is the 2010 baseline value. 

 

F3Figure 9.1: Design Space Query screen in Data Visualization Tool. 
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F4Figure 9.2: Design Space Analysis screen in Data Visualization Tool. 
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F5Figure 9.3: Full Size Car Design Space Analysis example. Black point is 2010 baseline; 

red points are for advanced diesel and dry-clutch DCT; blue points, Stoichiometric DI 

Turbo with advanced automatic transmission. 
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F6Figure 9.4: Full Size Car Design Space Analysis example. Black point is 2010 baseline; 

red points are for advanced diesel and dry-clutch DCT; blue points, Stoichiometric DI 

Turbo with advanced automatic transmission. 

F7Figure 9.5: Full Size Car Design Space Analysis example. Black point is 2010 baseline; 

red points are for advanced diesel and dry-clutch DCT; blue points, Stoichiometric DI 

Turbo with advanced automatic transmission. 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Combined FTP-HWFET (mpg)

Engine Displacement (L)

Baseline

Stoich DIT+8AT

Diesel+DCT
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F8Figure 9.6: Standard Car design space analysis example comparing powertrains with 

EGR DI Turbo engine. Blue points are with advanced automatic; gray, dry-clutch DCT; 

green, Powersplit; and red, P2 Hybrid. Black point is 2010 baseline.
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F9Figure 9.7: Efficient Frontier screen of Data Visualization Tool with example plot. 

9.4 Identification and Use of the Efficient Frontier 

Part of assessing the selected regions of the design space is to find configurations that balance 

efficiency and performance. The Data Visualization Tool identifies an Efficient Frontier, which is 

the bound of the sampled design space that has the most desirable performance. The user 

must first define a dataset using the Design Space Query, described in Section 9.2, above, and 

select the Efficient Frontier tab in the Data Visualization Tool. An example of the Efficient 

Frontier screen is shown in Figure 9.7. The Efficient Frontier is marked out in red. The user can 

click on the data points along the frontier to discover the vehicle configurations that lie on the 

frontier. 

10. RECOMMENDATIONS FOR FURTHER WORK 

Ricardo has the following recommendations for further work on this program: 

• More rigorous analysis and simulation of turbo lag effects in the advanced, boosted 

engines through engine performance simulation tied in with the vehicle models. 

• Expansion of the design space to encompass additional drive cycles, such as the NEDC, 

JC08, or the cold ambient FTP, to understand how the technology packages may apply 

to other global regions. 

• Expansion of the design space to mix 2010 baseline engines and transmissions with the 

advanced technologies to better understand the relative contributions of engine or 

transmission technology to the performance of the advanced vehicles. 
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• Expansion of the design space to include engines with different technology packages, 

such as a version of the Stoichiometric DI Turbo engine that has a single, fixed cam 

profile instead of using the CPS valvetrain. 

• Expansion of the design space to include additional technologies in one vehicle class to 

improve understanding of additional technologies. 

• Expansion of the design space by sweeping battery capacity. 

• Conduct detailed study of simulation results to understand main and interaction effects 

between technologies. 

11. CONCLUSIONS 

The following conclusions are supported by this project: 

• An independent, objective, and robust analytical study of effectiveness of selected LDV 

technologies expected to be prevalent in the 2020–2025 timeframe, and their effects on 

vehicle performance has been completed. 

• A comprehensive review process was completed to identify technologies likely to be 

available in the 2020–2025 timeframe, and to estimate their future performance given 

current trends and expected developments. 

• The vehicle performance models were based upon the underlying physics of the 

technologies and have been validated with good result to available test data. Quality 

assurance checks have been made throughout the study to ensure accuracy of the 

trends in the results. 

• The Data Visualization Tool allows EPA and other external stakeholders to examine the 

design space developed through the program's Complex Systems Modeling approach 

and to assess trade-offs between various vehicle configurations and their performance. 

The tool provides the necessary functionality to assess specific vehicle designs or more 

comprehensively explore the design space. 
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12. REFERENCES 

Coltman, D., J.W.G. Turner, R. Curtis, D. Blake, B. Holland, R.J. Pearson, A. Arden, and H. 

Nuglisch, 2008, "Project Sabre: A close-spaced direct injection 3-cylinder engine with 

synergistic technologies to achieve low CO2 output." SAE Paper 2008-01-0138. 

Environmental Protection Agency, "Test Car List Data Files", available from 

www.epa.gov/oms/tcldata.htm. 

Fiat Powertrain Technologies, S.p.A., 2009, "MultiAir: The ultimate air management strategy". 

http://www.fptmultiair.com/flash_multiair_eng/home.htm. 

Hellenbroich, G., and V. Rosenburg, 2009, "FEV's new parallel hybrid transmission with single 

dry clutch and electric torque support." Aachener Koolquium Fahrzeug- und Motorentechnik 

2009 18:1209–1222. 

Lumsden, G., D. OudeNijeweme, N. Fraser, and H. Blaxill, 2009, "Development of a 

turbocharged direct injection downsizing demonstrator engine". SAE Paper 2009-01-1503. 

McKay, M.D.; Beckman, R.J.; Conover, W.J., 1979, "A comparison of three methods for 

selecting values of input variables in the analysis of output from a computer code". 

Technometrics 21 (2): 239–245. 

PQA and Ricardo, 2008, A Study of Potential Effectiveness of Carbon Dioxide Reducing Vehicle 

Technologies. 

Shaw, J.R., 2009, “Testimony to Joint EPA/NHTSA Hearing on Proposed Rulemaking to 

Establish Light Duty Vehicle Greenhouse Gas Emissions Standards and Corporate Average 

Fuel Economy Standards”. Downloaded from www.autosteel.org and last accessed October 27, 

2009. 

Staunton, R.H., C.W. Ayers, L.D. Marlino, J.N. Chiasson, T.A., Burress, 2006, "Evaluation of 

2004 Toyota Prius Hybrid Electric Drive System". ORNL technical report TM-2006/423. 

Turner, J.W.G., R.J. Pearson, R. Curtis, and B. Holland, 2009, "Sabre: A cost-effective engine 

technology combination for high efficiency, high performance and low CO2 emissions." Low 

Carbon Vehicles 2009: Institution of Mechanical Engineers (IMechE) conference proceedings. 

USAMP, 2006, “Magnesium Vision 2020: A North American Automotive Strategic Vision for 

Magnesium”. January 11, 2006 

“USLAB materials and processes”. Downloaded from www.autosteel.org and last accessed 

November 4, 2009. 
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APPENDICES 

Appendix 1: Abbreviations 

AMT Automated manual transmissions 

ARB California Air Resources Board 

BMEP Brake mean effective pressure 

BSFC Brake specific fuel consumption 

CPS Cam profile switching 

CVT Continuously variable transmission 

DCT Dual clutch transmission 

DI Direct injection 

DoE Design of experiments 

DVA Digital valve actuation 

EGR Exhaust gas recirculation 

EPA United States Environmental Protection Agency 

EPAS Electric power assisted steering 

EV Electric vehicle 

FEAD Front end accessory drive 

FIE Fuel injection equipment 

GHG Greenhouse gas 

LHDT Light heavy-duty truck 

ICCT International Council on Clean Transportation 

KERS Kinetic energy recovery system 

LDT Light-duty truck 

LDV Light-duty vehicle 

LEV Low emissions vehicle 

MPV Multi-purpose vehicle 

NOx Nitrogen oxides 

NVH Noise, vibration, and harshness 

OEM Original equipment manufacturer 

OTAQ Office of Transportation and Air Quality 

PAS Power assisted steering 

PFI Port fuel injection 

PHEV Plug-in hybrid electric vehicle 

PQA Perrin Quarles Associates 

RSM Response surface model 

SCR Selective catalytic reduction 

SI Spark ignited 

SME Subject matter expert 

SOC State of charge 

SULEV Super ultra low emissions vehicle 

V2I Vehicle to infrastructure 

V2V Vehicle to vehicle 

VA Valve actuation 
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Appendix 2: Output Factors for Study 

Raw fuel economy in miles per U.S. gallon and GHG emissions in grams of CO2 per mile over 

• FTP75 

• HWFET 

• US06 

• HWFET and FTP combined 

Acceleration performance metrics, including 

• 0–10 mph acceleration time 

• 0–30 mph acceleration time 

• 0–50 mph acceleration time 

• 0–60 mph acceleration time 

• 0–70 mph acceleration time 

• 30–50 mph acceleration time 

• 50–70 mph acceleration time 

• Top speed at 5% grade 

• Top speed at 10% grade 

• Velocity at 1.3 sec 

• Velocity at 3.0 sec 

• Distance at 1.3 sec 

• Distance at 3.0 sec 

• Maximum grade at 70 mph at GCW 

• Maximum grade at 60 mph at GCVW (LDT and LHDT only) 
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Appendix 3: Nominal Runs Results 

The table lists the baseline (2010) vehicles first, followed by results by vehicle class. The P2 

Hybrids have an electric machine size listed, and all use the DCT. There were no Conventional 

Stop-Start nominal runs that used the DCT. For the Input Powersplit hybrids, only the traction 

motor size is listed, as the generator size is a function of the engine and traction motor sizes. 

Abbreviations used exclusively in the following table of Nominal Runs Results include the 

following: 

Baseline The 2010 baseline engine for the given vehicle class 

Stoich DIT Stoichiometric DI Turbo engine 

Lean DIT Lean-Stoichiometric DI Turbo engine 

EGR DIT EGR DI Turbo engine 

Adv Diesel Advanced (2020) diesel 

Atk CS Atkinson cycle engine with CPS 

Atk DVA Atkinson cycle engine with DVA 

AT6 Six-speed automatic transmission (baseline or advanced, as appropriate) 

AT8 Eight-speed automatic transmission (advanced only) 

DCT Dry or wet clutch DCT, per simulation matrix. 

PS Powersplit planetary gearset 

 
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T11Table A3.1: Nominal Runs Results. 

Engine

Vehicle Class Type Displ (L) FTP75 HWFET US06 0-10mph 0-30mph 0-50mph 0-60mph 0-70mph 30-50mph 50-70mph 5% Grade 10% Grade 60 mph 70 mph 1.3 sec 3 sec 1.3 sec 3 sec

Small Car Baseline 1.5 145

— AT6 39.8 48.7 30.3 1.4 4.0 7.4 9.9 13.0 3.4 5.6 94.6 82.2

— 10 2.6 14.7 9.3 22.8

Standard Car Baseline 2.4 219

— AT6 30.0 43.5 29.1 1.0 3.1 6.3 8.3 11.4 3.2 5.1 110.4 86.7

— 15 3.9 20.5 14.1 29.5

Full Size Car Baseline 3.5 335

— AT6 23.8 33.7 23.6 1.2 3.2 6.1 7.7 9.5 2.9 3.4 127.5 99.6

— 33 3.2 19.3 12.0 29.2

Small MPV Baseline 2.4 219

— AT6 27.0 36.1 24.1 1.1 3.4 6.8 9.0 12.6 3.4 5.8 97.9 85.1

— 13 3.3 18.4 12.5 26.9

Large MPV Baseline 3.8 319

— AT6 22.0 30.7 21.0 1.1 2.9 6.1 8.6 11.5 3.2 5.4 95.7 82.2

— 16 3.4 20.4 12.9 31.2

LDT Baseline 5.4 495

— AT6 16.2 22.6 15.1 1.1 3.1 6.0 8.2 10.8 2.9 4.8 101.4 88.3 20

— 3.4 19.9 12.6 29.8

LHDT Base SI 6.0 515

— AT6 11.2 15.1 9.8 1.0 3.1 6.9 9.9 13.3 3.8 6.4 92.8 69.8 15

— 3.7 20.7 14.2 29.9

LHDT Base Diesel 6.6 895 — AT6 15.6 19.0 12.3 1.0 3.0 6.7 9.4 13.1 3.7 6.4 99.3 79.3 17 — 4.0 21.3 14.7 30.4

Small Car Stoich DIT 0.74 157

— AT6 53.2 55.1 32.4 1.5 4.0 7.4 10.0 13.1 3.4 5.7 96.2 79.3

— 14 2.4 14.1 8.8 22.3

Small Car Lean DIT 0.74 157

— AT6 55.1 56.0 32.6 1.5 4.0 7.4 10.0 13.1 3.4 5.7 96.2 79.3

— 14 2.4 14.1 8.8 22.3

Small Car EGR DIT 0.74 157

— AT6 55.1 57.4 33.9 1.5 4.0 7.4 10.0 13.1 3.4 5.7 96.2 79.3

— 14 2.4 14.1 8.8 22.3

Small Car Adv Diesel 1.23 221

— AT6 55.8 59.4 41.5 1.2 3.7 7.3 9.8 13.2 3.6 5.9 94.0 76.0

— 13 3.2 16.8 10.9 25.1

Small Car Atk CS 1.66 138 14 DCT6 70.8 59.0 38.1 1.4 3.7 7.5 10.0 13.6 3.8 6.1 85.4 62.1

— 8 2.5 15.5 9.8 24.4

Small Car Atk DVA 1.66 138 14 DCT6 71.7 60.5 39.4 1.4 3.7 7.5 10.0 13.6 3.8 6.1 85.4 62.1

— 8 2.5 15.5 9.8 24.4

Small Car Stoich DIT 0.59 124 14 DCT6 68.2 57.3 37.0 1.4 3.8 7.2 9.6 12.6 3.4 5.4 87.5 70.7

— 10 2.4 15.1 9.4 23.6

Small Car Lean DIT 0.59 124 14 DCT6 68.4 57.7 37.3 1.4 3.8 7.2 9.6 12.6 3.4 5.4 87.5 70.7

— 10 2.4 15.1 9.4 23.6

Small Car EGR DIT 0.59 124 14 DCT6 70.2 59.9 39.0 1.4 3.8 7.2 9.6 12.6 3.4 5.4 87.5 70.7

— 10 2.4 15.1 9.4 23.6

Small Car Atk CS 1.66 138 40 PS 64.2 59.5 38.0 1.8 4.7 7.9 9.8 12.2 3.2 4.3 93.5 77.4

— 8 1.5 11.1 6.7 18.5

Small Car Atk DVA 1.66 138 40 PS 67.3 60.0 39.0 1.8 4.7 7.9 9.8 12.1 3.2 4.2 93.5 77.9

— 8 1.5 11.1 6.7 18.5

Small Car Stoich DIT 0.59 124 40 PS 64.7 57.2 35.2 1.9 4.8 8.2 10.4 13.1 3.4 4.9 89.5 71.3

— 8 1.5 10.8 6.5 18.1

Small Car Lean DIT 0.59 124 40 PS 65.8 57.4 35.2 1.9 4.8 8.2 10.4 13.1 3.4 4.9 89.1 71.3

— 8 1.5 10.8 6.5 18.1

Small Car EGR DIT 0.59 124 40 PS 67.7 60.1 36.7 1.9 4.8 8.2 10.4 13.1 3.4 4.9 89.1 71.3 — 8 1.5 10.8 6.5 18.1

Standard Car Stoich DIT 1.04 220

— AT8 44.8 54.5 32.5 1.0 3.1 6.2 8.5 11.4 3.1 5.2 107.7 86.2

— 15 3.7 20.7 14.0 29.7

Standard Car Lean DIT 1.04 220

— AT8 46.6 55.5 32.8 1.0 3.1 6.2 8.5 11.4 3.1 5.2 107.7 86.2

— 15 3.7 20.7 14.0 29.7

Standard Car EGR DIT 1.04 220

— AT8 46.4 56.7 34.0 1.0 3.1 6.2 8.5 11.4 3.1 5.2 107.7 86.2

— 15 3.7 20.7 14.0 29.7

Standard Car Atk CS 2.40 200 24 DCT8 64.6 59.7 39.8 1.2 3.4 6.5 8.6 11.4 3.1 4.9 101.3 78.4

— 11 2.8 17.4 11.0 27.3

Standard Car Atk DVA 2.40 200 24 DCT8 65.9 61.0 40.5 1.2 3.4 6.5 8.6 11.4 3.1 4.9 101.3 78.4

— 11 2.8 17.4 11.0 27.3

Standard Car Stoich DIT 0.83 176 24 DCT8 61.9 57.2 36.9 1.3 3.6 6.5 8.6 11.3 2.9 4.8 98.1 77.4

— 12 2.6 16.6 10.5 25.8

Standard Car Lean DIT 0.83 176 24 DCT8 62.9 58.0 36.7 1.3 3.6 6.5 8.6 11.3 2.9 4.8 98.1 77.4

— 12 2.6 16.6 10.5 25.8

Standard Car EGR DIT 0.83 176 24 DCT8 65.1 59.7 38.4 1.3 3.6 6.5 8.6 11.3 2.9 4.8 98.1 77.4

— 12 2.6 16.6 10.5 25.8

Standard Car Atk CS 2.40 200 80 PS 53.3 51.7 36.8 1.4 3.6 6.2 8.0 10.2 2.6 4.0 105.2 85.5

— 10 2.2 15.2 9.3 25.1

Standard Car Atk DVA 2.40 200 80 PS 56.4 53.3 37.6 1.4 3.6 6.2 8.0 10.2 2.6 4.0 105.2 85.8

— 10 2.2 15.2 9.3 25.1

Standard Car Stoich DIT 0.83 176 80 PS 55.6 51.7 35.4 1.5 3.7 6.6 8.7 11.3 2.9 4.7 99.0 78.1

— 10 2.1 14.9 9.1 24.4

Standard Car Lean DIT 0.83 176 80 PS 57.9 53.5 36.0 1.5 3.7 6.6 8.7 11.3 2.9 4.7 99.0 78.1

— 10 2.1 14.9 9.1 24.4

Standard Car EGR DIT 0.83 176 80 PS 58.0 54.8 37.1 1.5 3.7 6.6 8.7 11.3 2.9 4.7 99.0 78.1 — 10 2.1 14.9 9.1 24.4

Small MPV Stoich DIT 1.13 239

— AT8 38.8 42.6 25.7 1.2 3.3 6.5 8.9 12.0 3.2 5.5 99.8 81.2

— 14 3.1 18.7 11.6 28.3

Small MPV Lean DIT 1.13 239

— AT8 40.3 43.1 25.9 1.2 3.3 6.5 8.9 12.0 3.2 5.5 99.8 81.2

— 14 3.1 18.7 11.6 28.3

Small MPV EGR DIT 1.13 239

— AT8 40.3 44.4 27.0 1.2 3.3 6.5 8.9 12.0 3.2 5.5 99.8 81.2

— 14 3.1 18.7 11.6 28.3

Small MPV Atk CS 2.60 217 20 DCT8 52.9 45.5 29.6 1.4 3.7 7.1 9.3 12.6 3.4 5.5 91.3 76.7

— 10 2.3 15.4 9.4 25.1

Small MPV Atk DVA 2.60 217 20 DCT8 54.1 46.8 30.3 1.4 3.7 7.1 9.3 12.6 3.4 5.5 91.3 76.7

— 10 2.3 15.4 9.5 25.1

Small MPV Stoich DIT 0.90 190 20 DCT8 50.1 44.2 28.5 1.5 3.9 7.0 9.4 12.3 3.1 5.3 90.6 72.7

— 11 2.1 14.6 8.9 23.7

Small MPV Lean DIT 0.90 190 20 DCT8 50.8 44.5 28.5 1.5 3.9 7.0 9.4 12.3 3.1 5.3 90.6 72.7

— 11 2.1 14.6 8.9 23.7

Small MPV EGR DIT 0.90 190 20 DCT8 52.0 46.1 29.8 1.5 3.9 7.0 9.4 12.3 3.1 5.3 90.6 72.7

— 11 2.1 14.6 8.9 23.7

Small MPV Atk CS 2.60 217 70 PS 44.3 39.6 25.1 1.9 4.6 7.4 9.1 11.1 2.8 3.7 98.3 81.4

— 9 1.4 10.5 6.1 18.0

Small MPV Atk DVA 2.60 217 70 PS 49.3 42.3 28.6 1.9 4.6 7.4 9.1 11.1 2.8 3.7 98.3 81.6

— 9 1.4 10.5 6.1 18.0

Small MPV Stoich DIT 0.90 190 70 PS 49.1 42.2 27.4 1.9 4.7 8.0 10.3 13.1 3.3 5.1 92.2 73.3

— 8 1.4 10.4 6.1 17.7

Small MPV Lean DIT 0.90 190 70 PS 50.8 42.7 27.3 1.9 4.7 8.0 10.3 13.2 3.3 5.2 92.2 73.3

— 8 1.4 10.4 6.1 17.7

Small MPV EGR DIT 0.90 190 70 PS 51.3 44.9 28.8 1.9 4.7 8.0 10.3 13.2 3.3 5.2 92.2 73.3 — 8 1.4 10.4 6.1 17.7

Velocity (mph) at Distance (m) in Top Speed (mph) on Max grade at Acceleration times (s) Raw fuel economy (mpg) Peak Trq 

(N.m)

EM Pwr 

(kW)

Transmission
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6 April 2011 Ricardo, Inc. Page 46 Engine Vehicle Class Type Displ (L) FTP75 HWFET US06 0-10mph 0-30mph 0-50mph 0-60mph 0-70mph 30-50mph 50-70mph 5% Grade 10% Grade 60 mph 70 mph 1.3 sec 3 sec 1.3 sec 3 sec Full Size Car Stoich DIT 1.41 298

— AT8 37.1 43.2 27.2 1.2 3.0 5.7 7.4 9.8 2.7 4.1 116.1 95.8

— 20 3.2 19.7 12.1 30.7

Full Size Car Lean DIT 1.41 298

— AT8 38.8 44.0 27.6 1.2 3.0 5.7 7.4 9.8 2.7 4.1 116.1 95.8

— 20 3.2 19.7 12.1 30.7

Full Size Car EGR DIT 1.41 298

— AT8 38.6 44.9 28.5 1.2 3.0 5.7 7.4 9.8 2.7 4.1 116.1 95.8

— 20 3.2 19.7 12.1 30.7

Full Size Car Adv Diesel 2.85 503

— AT8 38.2 46.5 32.5 1.1 2.9 5.6 7.5 9.8 2.7 4.2 113.7 94.9

— 19 3.5 20.2 12.4 31.1

Full Size Car Atk CS 3.80 317 28 DCT8 49.9 46.2 31.5 1.2 3.0 5.5 7.1 9.0 2.5 3.5 113.4 87.9

— 22 2.8 18.6 11.4 30.1

Full Size Car Atk DVA 3.80 317 28 DCT8 51.1 47.4 31.9 1.2 3.0 5.5 7.1 9.0 2.5 3.5 113.4 87.9

— 22 2.8 18.6 11.4 30.1

Full Size Car Stoich DIT 1.13 238 28 DCT8 49.8 46.5 31.3 1.3 3.4 6.1 7.7 10.0 2.7 3.9 105.9 86.6

— 15 2.5 16.9 10.5 27.1

Full Size Car Lean DIT 1.13 238 28 DCT8 50.4 46.8 31.4 1.3 3.4 6.1 7.7 10.0 2.7 3.9 105.9 86.6

— 15 2.5 16.9 10.5 27.1

Full Size Car EGR DIT 1.13 238 28 DCT8 51.7 48.3 32.6 1.3 3.4 6.1 7.7 10.0 2.7 3.9 105.9 86.6

— 15 2.5 16.9 10.5 27.1

Full Size Car Atk CS 3.80 317 120 PS 40.3 38.7 29.2 1.3 3.2 5.6 7.1 8.7 2.4 3.1 119.2 103.5

— 16 2.7 17.7 10.7 28.6

Full Size Car Atk DVA 3.80 317 120 PS 43.0 40.8 29.8 1.3 3.2 5.6 7.1 8.7 2.4 3.1 119.2 103.5

— 16 2.7 17.7 10.7 28.7

Full Size Car Stoich DIT 1.13 238 120 PS 46.6 42.0 29.8 1.3 3.2 6.0 7.8 10.0 2.8 4.0 106.2 87.6

— 12 2.6 17.2 10.4 28.2

Full Size Car Lean DIT 1.13 238 120 PS 48.0 41.8 29.9 1.3 3.2 5.9 7.8 10.0 2.7 4.1 106.2 87.7

— 12 2.6 17.2 10.4 28.3

Full Size Car EGR DIT 1.13 238 120 PS 47.9 43.6 31.1 1.3 3.2 6.0 7.8 10.0 2.8 4.0 106.2 87.7 — 12 2.6 17.2 10.4 28.2

Large MPV Stoich DIT 1.31 277

— AT8 34.8 39.2 23.7 1.1 3.2 6.4 8.6 11.5 3.2 5.1 103.7 84.5

— 15 3.4 19.8 13.2 29.1

Large MPV Lean DIT 1.31 277

— AT8 36.0 39.8 23.9 1.1 3.2 6.4 8.6 11.5 3.2 5.1 103.7 84.5

— 15 3.4 19.8 13.2 29.1

Large MPV EGR DIT 1.31 277

— AT8 36.2 40.9 24.9 1.1 3.2 6.4 8.6 11.5 3.2 5.1 103.7 84.5

— 15 3.4 19.8 13.2 29.1

Large MPV Adv Diesel 2.61 460

— AT8 37.3 43.3 29.4 1.1 3.0 6.2 8.6 11.5 3.2 5.3 101.9 83.9

— 14 3.7 20.9 13.3 30.5

Large MPV Atk CS 3.15 263 25 DCT8 48.3 42.4 27.5 1.4 3.6 6.7 8.8 11.6 3.1 4.9 95.5 82.3

— 11 2.4 15.7 9.7 26.0

Large MPV Atk DVA 3.15 263 25 DCT8 48.8 43.5 27.7 1.4 3.6 6.7 8.8 11.6 3.1 4.9 95.5 82.3

— 11 2.4 15.7 9.7 26.0

Large MPV Stoich DIT 1.05 221 25 DCT8 47.7 42.2 26.2 1.5 3.8 6.9 9.1 11.9 3.1 5.0 94.3 75.6

— 12 2.2 14.7 9.0 23.9

Large MPV Lean DIT 1.05 221 25 DCT8 47.4 42.6 26.8 1.5 3.8 6.9 9.1 11.9 3.1 5.0 94.3 75.6

— 12 2.2 14.7 9.0 23.9

Large MPV EGR DIT 1.05 221 25 DCT8 47.6 43.0 27.6 1.5 3.8 6.9 9.1 11.9 3.1 5.0 94.3 75.6

— 12 2.2 14.7 9.0 23.9

Large MPV Atk CS 3.15 263 90 PS 41.7 38.6 26.5 1.8 4.2 7.0 8.8 11.1 2.8 4.1 103.4 86.0

— 11 1.5 11.7 6.8 20.2

Large MPV Atk DVA 3.15 263 90 PS 44.3 39.6 27.0 1.8 4.2 7.0 8.8 11.1 2.8 4.1 103.4 86.5

— 11 1.5 11.7 6.8 20.2

Large MPV Stoich DIT 1.05 221 90 PS 44.8 39.3 25.8 1.8 4.3 7.5 9.7 12.5 3.2 5.0 100.2 76.5

— 9 1.5 11.5 6.7 19.7

Large MPV Lean DIT 1.05 221 90 PS 45.7 40.6 25.7 1.8 4.3 7.5 9.7 12.5 3.2 5.0 100.8 76.5

— 9 1.5 11.5 6.7 19.7

Large MPV EGR DIT 1.05 221 90 PS 47.0 41.5 27.3 1.8 4.3 7.5 9.7 12.5 3.2 5.0 100.8 76.5 — 9 1.5 11.5 6.7 19.7

LDT Stoich DIT 1.94 410

— AT8 23.8 26.6 16.3 1.1 3.0 6.0 8.1 10.8 3.0 4.8 105.6 86.9 21

— 3.4 20.5 13.2 30.5

LDT Lean DIT 1.94 410

— AT8 24.6 27.0 16.4 1.1 3.0 6.0 8.1 10.8 3.0 4.8 105.6 86.9 21

— 3.4 20.5 13.2 30.5

LDT EGR DIT 1.94 410

— AT8 24.8 27.7 17.1 1.1 3.0 6.0 8.1 10.8 3.0 4.8 105.6 86.9 21

— 3.4 20.5 13.2 30.5

LDT Adv Diesel 4.28 694

— AT8 26.4 30.4 20.5 1.0 2.9 5.8 8.0 10.6 2.9 4.8 104.6 87.2 20

— 3.7 21.4 13.5 31.5

LDT Atk CS 4.60 384 50 DCT8 33.2 29.0 19.4 1.1 3.1 5.9 7.8 10.3 2.8 4.4 95.6 83.8 17

— 3.1 19.1 12.2 29.5

LDT Atk DVA 4.60 384 50 DCT8 33.9 29.7 19.7 1.1 3.1 5.9 7.8 10.3 2.8 4.4 95.6 83.8 17

— 3.1 19.1 12.2 29.5

LDT Stoich DIT 1.55 327 50 DCT8 32.5 28.4 18.9 1.2 3.3 6.1 7.9 10.4 2.8 4.3 96.1 78.0 16

— 3.0 18.2 11.7 27.9

LDT Lean DIT 1.55 327 50 DCT8 33.0 28.6 18.8 1.2 3.3 6.1 7.9 10.4 2.8 4.3 96.1 78.0 16

— 3.0 18.2 11.7 27.9

LDT EGR DIT 1.55 327 50 DCT8 33.8 29.6 19.7 1.2 3.3 6.1 7.9 10.4 2.8 4.3 96.1 78.0 16 — 3.0 18.2 11.7 27.9

LHDT Stoich DIT 2.30 486

— AT8 16.5 18.3 11.1 1.0 3.2 7.0 9.8 13.4 3.8 6.4 95.1 74.8 16

— 3.6 20.2 13.9 28.6

LHDT Lean DIT 2.30 486

— AT8 16.8 18.4 11.2 1.0 3.2 7.0 9.8 13.4 3.8 6.4 95.1 74.8 16

— 3.6 20.2 13.9 28.6

LHDT EGR DIT 2.30 486

— AT8 17.2 19.1 11.7 1.0 3.2 7.0 9.8 13.4 3.8 6.4 95.1 74.8 16

— 3.6 20.2 13.9 28.6

LHDT Adv Diesel 6.60 895 — AT8 19.8 21.5 14.1 0.9 2.9 6.3 8.8 11.8 3.4 5.5 101.5 79.3 17 — 4.2 22.2 15.1 31.0

Peak Trq 

(N.m)

EM Pwr 

(kW)

Transmission Raw fuel economy (mpg) Acceleration times (s) Top Speed (mph) on Max grade at Distance (m) in Velocity (mph) at
Computer Simulation of LDV Technologies for GHG Emission Reduction in the 2020–2025 Timeframe 

6 April 2011 Ricardo, Inc. Page 47 

DISCLAIMER 

Ricardo Inc., has taken all reasonable care in compiling the analyses and recommendations 

provided in this report. However, the information contained in this report is based on information 

and assumptions provided by the client or otherwise available to Ricardo, which, in all the 

circumstances, is deemed correct on the date of writing. Ricardo does not assume any liability, 

provide any warranty, or make any representation in respect of the accuracy of the information, 

assumptions, and, consequently, the analyses and recommendations contained in this report. 

The report has been compiled solely for the client's use. 

Any results of analysis and calculation are intended to be part of subsequent decision-making 

during design, development, and problem-solving stages. Although analysis may reduce the 

effort required to validate a product through testing prior to production, such results shall not be 

relied on as a validation in its own right. 

Analysis and calculations which are intended to predict physical behaviors are inherently 

theoretical in nature as they are subject to a range of assumptions and approximations. Physical 

behaviors and the measurements of those behaviors may vary for a variety of factors, some 

being outside the control of Ricardo or the capability of the predictive methodology used by 

Ricardo. Therefore, where any such predictions are subsequently compared with measured 

data or physical behavior, it is to be expected that differences will be apparent.