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Co-Optimization of Fuels and Engines

John Farrell

SAE High Efficiency Internal Combustion Engine Symposium

April 11, 2016

NREL/PR-5400-66333

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
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Goal: better 

fuels and better 

vehicles

sooner

Fuel and Engine 

Co-Optimization

o What fuel properties maximize engine 

performance?

o How do engine parameters affect 

efficiency?

o What fuel and engine combinations are 

sustainable, affordable, and scalable?
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30% per vehicle 

petroleum 

reduction via

 efficiency and 

displacement

source: EIA 2014 reference case
Fuel selection overview

If we identify target values for the critical fuel properties that maximize 

efficiency and emissions performance for a given engine architecture, 

then fuels that have properties with those values (regardless of chemical 

composition) will provide comparable performance

Governing Co-Optima hypotheses:

There are engine architectures and strategies that provide higher 

thermodynamic efficiencies than available from modern internal combustion 

engines; new fuels are required to maximize efficiency and operability across a 

wide speed/load range
Current fuels constrain engine design 5
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Fuel is more 

than just 

octane
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Leveraging expertise 

and facilities from 10 

national labs
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Integrated

multi-lab teams

with significant 

external 

stakeholder

engagement

8 Biofuel

companies

13 Light and heavy duty vehicle 

manufacturers

10 Oil companies/

refiners

End consumer

2 organizations

Regulatory 

4 agencies
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Parallel efforts are underway

Low reactivity fuel Range of fuel properties TBD High reactivity fuel

Thrust I: Spark Ignition 

(SI)

Thrust II: Advanced Compression Ignition (ACI)

kinetically-controlled and compression-ignition combustion
Applicable 10

to 

light, medium, and heavy-duty engines

hybridized and non-hybridized powertrains
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identify and 

mitigate 

barriers to 

wide-scale

deployment

Identify and 

mitigate 

barriers to 

wide-scale

deployment
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Co-Optimization: 9-14% 

GHG reduction 

(beyond “business as usual”)

National goal:

80%

reduction in transportation GHG by

2050
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Six integrated teams

Low Greenhouse

Gas Fuels

Advanced Engine

Development

Market

Transformation

Fuel Properties

Analysis of Sustainability,

Scale, Economics, Risk, 

and Trade

Modeling and 

Simulation Toolkit
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FY16 

Activities

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What fuels can we make?
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Fuel selection criteria (“decision tree”)
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Thrust I decision tree results
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Fuel property database

Database of critical fuel properties of 

bio-derived and petroleum blendstocks 

366 molecules, 12 mixtures (at present)

25 database fields for fuel properties

Will add capability for fully blended fuels

Data from experiment and literature or 

calculated/estimated (where needed)

Shared resource for team and public

Fioroni et al., NREL
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Identification of Thrust I candidates

Tier I criteria

Melting point/cloud point below -10C

Boiling point between 20C and 165C

Measured or estimated RON ≥ 98

Meet toxicity, corrosion, solubility, 

and biodegradation requirements

34 promising bio-blendstocks from 

many functional group classes

Not final – this is an iterative process!
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High-level LCA, TEA,* 

feedstock availability analyses

Identify cost/environmental/scale 

attributes

Fifteen key metrics identified

GHG, water, economics, TRL 

Evaluation of 20 Thrust I 

blendstocks underway

Cost and environmental impact analyses

* LCA = Life cycle analysis; TEA = techno-economic analysis; 

 TRL = technology readiness level 
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Identify and mitigate 

challenges of moving 

new fuels/ engines 

to markets 

Historical analysis of 

new fuel and vehicle 

introduction

Engage stakeholders 

across value chain

Identifying/mitigating market barriers

Adapted from S. Przesmitzki
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Fuel-related tasks
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Fuel-related tasks (continued)
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Heat of vaporization (HOV): complex mixtures

Pure compound approach not 

applicable to gasoline

True HOV underestimated

Approach: directly measure HOV 

by DSC/TGA* and calculate via 

detailed hydrocarbon analysis

Very similar HOV for wide range 

of gasolines and ethanol blends

* DSC = differential scanning calorimetry; 

 TGA = thermogravimetric analysis Fioroni et al., NREL
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Rapid compression machine study 

of CRC FACE-F / ethanol blends 

(E0–E30, E100)

Data to validate LLNL gasoline 

surrogate kinetic mechanism

Bench-scale autoignition studies 

combined with engine experiments

Data from customized IQT to validate 

LLNL kinetic mechanisms Zigler (NREL)

Kinetics and SI autoignition behavior

Goldsborough, ANL
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Develop archival mechanisms for representative bio-blendstocks and 

surrogates

Validate against high-fidelity experimental data

Anisole - surrogate for methylated phenolics from biomass (Pitz et al., LLNL)

Kinetic mechanism development 

Mole fractionMole fraction
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Thrust I tasks
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Engine performance merit function

Provides systematic ranking of blendstock candidates on engine 

efficiency when multiple fuel properties are varying simultaneously

Allows fuel economy gains to be estimated based on fuel properties

RON = research octane number

K = engine-dependent constant 

S = sensitivity (RON-MON)

ON = effective octane number

HoV = heat of vaporization

SL = flame speed

LFV = liquid fuel volume at 150°C

H = Heaviside function

PMI = particle mass index
Relationship between sensitivity and HOV

Inconsistencies in literature regarding 

HOV impact on knock

HOV effect only been observed when 

covariant with octane sensitivity

Main conclusion: HOV is a thermal 

contributor to sensitivity

Consistent with vaporization effects in 

RON and MON tests

HOV appears to improve performance 

at elevated intake air temperatures

Sluder, Szybist (ORNL) McCormick, Ratcliff, Zigler (NREL)
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Quantify relative fuel impact on dilution 

tolerance and compare vs engine parameters

Fuel properties: flame speed, HOV

Engine: tumble, ignition energy, etc.

Hypothesis: laminar flame speed predicts 

dilution tolerance (lean and EGR) of an SI fuel

Preliminary results confirm positive correlation

Fuel effects on EGR and lean dilution limits

Wallner ANL

Iso-octane N-heptane

Toluene

Ethanol

Methanol

0

10

20

30

40

50

60

40 45 50 55 60

Heat of Vaporization [kJ/MJ]

LFS at 1atm, 358K [cm/s]
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Single cylinder version of GM 

Ecotec 2.0L, 9.2: CR

Dilution tolerance correlates to 

laminar flame speed

Flame speed at ignition provides 

good indication of spark-to-CA5, 

combustion stability

Fuel effects on EGR and lean dilution limits

Szybist

ORNL
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Thrust II tasks
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Evaluate Thrust I fuel performance 

in GCI engine, 

Particular focus: challenging low load 

operation

Identify relationships of fuel HoV, 

sensitivity with GCI combustion, 

emissions, and performance

Thrust I Fuel Behavior in GCI 

Ciatti ANL
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1.9L GM diesel engine platform with 

production viable hardware 

Modified for both single- and dual-fuel 

LTC operation 

Identify performance trends in CI/LTC

strategies spanning RCCI + GCI

Vary reactivity differential between 

premixed and DI fuels 

Matched experiments to optical work 

at SNL 

Multi-cylinder RCCI experiments

ORNL RCCI Multi-Cylinder 1.9L GM (Curran)
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Measure in-cylinder mixing/ kinetics to 

optimize dual-fuel heat-release

Noise, efficiency, and load range

Understand mixing/ignition interaction 

for different reactivity combinations

Provides in-cylinder diagnostic for 

measuring reactivity stratification

Adds new insights for CFD as well

Optical diagnostics of RCCI

Musculus SNL
18 month 

decision 

point

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First major milestone: 18 month decision point

Marks completion fuel discovery efforts (i.e., candidate 

identification) for Thrust I (advanced spark ignition) 

Will conduct rigorous assessment of fuel/engine options and 

identify promising* low-GHG fuel/engine combinations

Will identify whether new low-GHG fuel candidates have been 

identified that require additional development work 

Outcome will dictate balance between Thrust I vs Thrust II work 

after 18 months

* Sustainable, affordable, scalable
The 18 months decision point 41
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Approach

Need to explicitly account for 

uncertainty
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Identifying options: a multi-objective optimization problem
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18 month decision point summary

• Co-Optima team is conducting R&D across wide technology space to identify

− Properties and attributes of Thrust I blendstock candidates

− Engine/vehicle merit function to relate properties to performance

• At end of 18 months (3/31/17) we will have surveyed all promising 

candidates and characterized performance along multiple dimensions 

• Decision will address whether promising* low-GHG blendstocks/fuels have 

been identified that merit further fuel development/scale-up efforts

− If “no” – shift fuel discovery/development efforts to Thrust II (other Thrust I

efforts – engine testing, ASSERT and MT metrics, etc – will likely continue)

− If answer is “yes” – continue development and scale-up efforts

Need stakeholder input to define what constitutes “promising”

− Focus of 2016 Stakeholder Listening Days (stay tuned)

*
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Status and next steps

Initiative started October 1 2016

FY16 budget: $27M; FY17 budget request: $30M

External advisory board formed

Active stakeholder engagement efforts underway (sign up!)
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Acknowledgements

DOE Sponsors:

Alicia Lindauer (BETO)

Kevin Stork and Gurpreet Singh (VTO)

Co-Optima Technical Team Leads:

Dan Gaspar (PNNL), Paul Miles (SNL), Jim Szybist (ORNL), 

Jennifer Dunn (ANL), Matt McNenly (LLNL), Doug Longman (ANL)

Other Co-Optima Leadership Team Members:

John Holladay (PNNL), Art Pontau (SNL), Robert Wagner (ORNL)
Thank You