December 2003 • NREL/SR-540-32919 

A. Rajagopalan, G. Washington, G. Rizzoni, and 

Y. Guezennec 

Center for Automotive Research 

Intelligent Structures and Systems Laboratory 

The Ohio State University 

Columbus, Ohio 

Development of Fuzzy Logic 

and Neural Network Control and 

Advanced Emissions Modeling 

for Parallel Hybrid Vehicles 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

NREL is a U.S. Department of Energy Laboratory 

Operated by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337 
December 2003 • NREL/SR-540-32919 

Development of Fuzzy Logic 

and Neural Network Control and 

Advanced Emissions Modeling 

for Parallel Hybrid Vehicles 

A. Rajagopalan, G. Washington, G. Rizzoni, and 

Y. Guezennec 

Center for Automotive Research 

Intelligent Structures and Systems Laboratory 

The Ohio State University 

Columbus, Ohio 

NREL Technical Monitor: K. Kelly 

Prepared under Subcontract No. LCL-1-31074-01 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

NREL is a U.S. Department of Energy Laboratory 

Operated by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337 
This publication was reproduced from the best available copy 

Submitted by the subcontractor and received no editorial review at NREL 

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 1

TABLE OF CONTENTS 

TABLE OF CONTENTS ............................................................................................................................. 1

LIST OF FIGURES...................................................................................................................................... 2

LIST OF TABLES........................................................................................................................................ 3

ACKNOWLEDGEMENTS ......................................................................................................................... 4

EXECUTIVE SUMMARY .......................................................................................................................... 5

INTRODUCTION ........................................................................................................................................ 7

INTELLIGENT CONTROL OF A HEV USING EMISSIONS CONSTRAINTS................................. 8

INTRODUCTION........................................................................................................................................... 8

INTELLIGENT CONTROL – THE BIG PICTURE............................................................................................... 8

Instantaneous control............................................................................................................................ 9

Why Fuzzy Logic?................................................................................................................................. 9

THE CONTROL STRATEGY .......................................................................................................................... 9

Optimal Torque Computation ............................................................................................................. 11

Effect of the Driver’s torque request and battery SOC....................................................................... 17

PREDICTIVE CONTROL .............................................................................................................................. 21

Future State Prediction Algorithm...................................................................................................... 22

RESULTS................................................................................................................................................... 25

Urban driving cycle ............................................................................................................................ 25

Highway cycle..................................................................................................................................... 27

US06 aggressive driving cycle............................................................................................................ 28

WILLANS’S LINE MODEL OF AN INTERNAL COMBUSTION ENGINE .................................... 31

INTRODUCTION......................................................................................................................................... 31

MODELING ............................................................................................................................................... 31

Symbols Used:..................................................................................................................................... 31

USAGE...................................................................................................................................................... 33

RESULTS................................................................................................................................................... 34

Example 1: .......................................................................................................................................... 34

Example 2: .......................................................................................................................................... 35

IMPLEMENTATION IN THE ADVISOR 3.2 SOFTWARE ................................................................ 38

EMISSIONS CONTROL STRATEGY.............................................................................................................. 38

PREDICTIVE CONTROL STRATEGY WITH EMISSIONS................................................................................. 39

Sample Driving cycles......................................................................................................................... 40

WILLAN’S LINE MODEL ........................................................................................................................... 40

CONVENTIONS FOR VARIABLES................................................................................................................ 41

SUMMARY OF CONCLUSIONS ............................................................................................................ 42

REFERENCES ........................................................................................................................................... 44

APPENDIX ................................................................................................................................................. 45
 2

LIST OF FIGURES 

Figure 1: Structure of the Powertrain Control System........................................................ 9 

Figure 2: Base block diagram with fuzzy controller......................................................... 10 

Figure 3: Fuzzy Controller block components.................................................................. 10 

Figure 4: IC Engine emissions – a general trend .............................................................. 11 

Figure 5: Finding an optimal IC Engine operating point.................................................. 12 

Figure 6: Optimal IC Engine torques based on individual weighting .............................. 14 

Figure 7: Optimal IC Engine torque based on equal weighting........................................ 15 

Figure 8: Sample cost function minimization at 1000 rpm............................................... 16 

Figure 9: Sample cost function minimization at 3000 rpm............................................... 16 

Figure 10: Optimal torque computation block diagram.................................................... 17 

Figure 11: Main fuzzy logic controller ............................................................................. 18 

Figure 12: Concept of operating about optimum torque................................................... 19 

Figure 13: Visualization of the Fuzzy Controller action .................................................. 20 

Figure 14: Predictive control algorithm (supervisory control structure) .......................... 21 

Figure 15: Predictive controller inputs and output ........................................................... 22 

Figure 16: Look-ahead window........................................................................................ 22 

Figure 17: Look-behind window ...................................................................................... 23 

Figure 18: Emissions results - UDDS............................................................................... 26 

Figure 19: Emissions results - HWFET............................................................................ 28 

Figure 20: Emissions results – US06................................................................................ 29 

Figure 21: Input – output relationship in an IC Engine .................................................... 31 

Figure 22: ICE fuel flow map for a scaled 3.0 L SI Engine ............................................. 34 

Figure 23: ICE Efficiency map for a scaled 3.0 L SI Engine ........................................... 35 

Figure 24: ICE fuel flow map for a scaled 4.0 L CI Engine............................................. 36 

Figure 25: ICE efficiency map for a scaled 4.0 L CI Engine ........................................... 37 

Figure 26: Use of the Willan’s line model........................................................................ 40 

Figure 27: Urban Dynamometer Driving Schedule (UDDS) ........................................... 45 

Figure 28: Highway Fuel Economy Test (HWFET)......................................................... 46 

Figure 29: US06 aggressive driving cycle ........................................................................ 46 
 3

LIST OF TABLES 

Table 1: Default weights in the control strategy............................................................... 12 

Table 2: Weights during a cold start – prioritize emissions.............................................. 13 

Table 3: Weights during high torque demand – compromise emissions.......................... 13 

Table 4: Overview of the control strategy ........................................................................ 20 

Table 5: Rule base for predictive FLC.............................................................................. 24 

Table 6: Results - UDDS .................................................................................................. 25 

Table 7: Results – HWFET............................................................................................... 27 

Table 8: Results – US06 ................................................................................................... 28 

Table 9: Files added in the ADVISOR 3.2 software – Emissions control........................ 39 

Table 10: Files added in the ADVISOR 3.2 software – Predictive control...................... 40 

Table 11: Sample driving cycles for the predictive algorithm.......................................... 40 

Table 12: Conventions for Variables ................................................................................ 41 
 4

ACKNOWLEDGEMENTS 

This work was funded by the Center for Transportation Technologies and Systems 

(CTTS) of the National Renewable Energy Laboratory (NREL), Golden, CO. We wish to 

thank Kenneth J. Kelly (Senior Engineer and point of contact, Systems Analysis Team, 

NREL) and Keith Wipke (NREL’s Vehicle Systems Analysis Task Leader) for their 

continued support. Dr. Giorgio Rizzoni (Director, Center for Automotive Research and 

Intelligent Transportation, the Ohio-State University) and Dr. Yann Guezennec (Faculty, 

OSU) provided project oversight and technical expertise. Xi Wei offered a helping hand 

in the development of the Willan’s Line model. We also thank students of the Center for 

Automotive Research, The Ohio-State University, for their support and ideas in the 

project. 
 5

EXECUTIVE SUMMARY 

This report describes the development of new control strategies and models for Hybrid 

Electric Vehicles (HEV) by the Ohio-State University, on a contract with the National 

Renewable Energy Laboratory (NREL), Golden, CO. The contract was assigned to the 

Center for Automotive Research and Intelligent Transportation (CAR-IT) by the Center 

for Transportation Technologies and Systems (CTTS) at the National Renewable Energy 

Laboratory. The report indicates results from models created in NREL’s ADvanced 

VehIicle SimulatOR (ADVISORTM 3.21

) software. Also presented are the results of a 

scalable IC Engine model, called the Willan’s Line technique, implemented in the 

ADVISOR 3.2 software. 

The project was aimed at the development of Advanced Control Techniques for Hybrid 

Vehicles. Techniques such as Fuzzy Logic were used in the implementation of the 

complex control problem. An optimal torque split between the Internal Combustion 

Engine (ICE) and the Electric Motor is the outcome of the new algorithm. The new 

control algorithm also takes into account emissions from the IC Engine in calculating the 

torque produced. The Willan’s line model was used to produce a scaled fuel flow map of 

an Internal Combustion Engine of any arbitrary size. 

The following are the highlights of the new control algorithm: 

1. Optimization structure for contending parameters such as emissions and fuel 

efficiency. 

2. Flexibility in assigning a relative importance between the various emissions and 

fuel economy. 

3. Non-Linear correction for IC Engine torque output based on battery State of 

Charge (SOC). 

4. The scalable control strategy embedded in the ADVISOR 3.2 software can be 

used for all Parallel Hybrid Electric Vehicle configurations. 

This document reveals that effective control of emissions can be performed even under 

demanding road conditions. An optimality issue exists when dealing with various 

emissions, but any specific emission characteristic can be effectively targeted by 

modifying the relative importance of that emission parameter. 

Changing relative importance between efficiency and emissions during the vehicle 

operation is useful in curtailing excess output of a particular parameter at certain 

instances. An example would be to heavily penalize operating points with high emissions 

when the engine is still cold. This mechanism is found to effectively reduce cold start 

emissions. 

 

1

 ADVISOR is a trademark of the Midwest Research Institute. 
 6

Future work may include bringing other vehicle parameters into the optimization criteria 

such as battery State of Charge (SOC), etc. and the addition of intelligent algorithms that 

modify an existing control strategy based on information about the future route of the 

vehicle. Vehicle navigation systems these days can pinpoint the current location of the 

vehicle and allow planning routes to a destination. Information along that planned route 

may be used in the control strategy to aim at optimizing performance for an entire trip. 

These algorithms are currently being studied by faculty and students at the Ohio-State 

University. 

An algorithm to produce the fuel-use maps of IC Engines is implemented in the 

ADVISOR 3.2 software. This algorithm, called the Willan’s Line Model, is based on the 

scaling of parameters from engines with similar characteristics. A database of the 

parameters of the Willan’s Line model for SI and CI engines are included in the software. 

There is a provision for adding parameters of newer engines into the database if 

available. The advantage with this algorithm lies in the fact that the user obtains a fuel–

use map, from entering some basic engine parameters such as stroke length, bore 

diameter, etc. 

The programs associated with all the algorithms are integrated into the ADVISOR 3.2 

software, consistent with the format for all ADVISOR data files. 
 7

INTRODUCTION 

Hybrid Electric Vehicles (HEVs) are projected as one of the solutions to the world’s need 

for cleaner and more fuel-efficient vehicles. The efficacy of an HEV lies in its control 

strategy. The diligent use of the 2 power sources, namely the internal combustion engine 

(ICE) and the electric motor (EM) determines the fuel consumption, the emissions output, 

and the charge-sustaining behavior of the vehicle, while maintaining drivability. 

The work presented below, was performed with the support of the National Renewable 

Energy Laboratory (NREL), Golden, CO. The task is to develop and validate an 

Intelligent Control Strategy for a Parallel Hybrid Electric Vehicle using Emissions 

constraints in the ADVISOR software. It must be noted that the strategy is programmed 

for the ADVISOR 3.2 software, in the form suitable for a backward simulator. A 

backward simulator is one in which the road-load experienced by a vehicle is translated 

through the drivetrain as a load on the power sources, namely the IC Engine and the 

electric motor. Implementing this strategy in an actual vehicle would require modification 

in the way the inputs are fed into the algorithm. But the core algorithm would still be 

based on the same concept. In actual implementation, there are other factors which affect 

the performance of the vehicle in terms of fuel economy and emissions outputs. The 

results of this algorithm are based on steady – state (static) data maps available in the 

ADVISOR 3.2 software. 
 8

INTELLIGENT CONTROL OF A HEV USING EMISSIONS CONSTRAINTS 

Introduction 

An Internal Combustion Engine (IC Engine) produces harmful emissions as a by-product 

of the combustion process inside a cylinder. These emissions have been controlled 

effectively in a conventional vehicle by the diligent use of fuel-injection systems. But in a 

conventional vehicle, the torque and speed produced by the IC engine is dependent on the 

driver’s request (effectively the road load). This prohibits the IC Engine from operating at 

its optimal operating point (optimal torque and speed) based on fuel economy and 

emissions, and is always at the driver’s discretion. This is the main advantage of a Hybrid 

Electric Vehicle (HEV) where the IC Engine can be commanded to run near it’s optimal 

operating point, while load-balancing using a buffer, such as an electric motor. The 

following section describes an intelligent control strategy using Fuzzy Logic that helps 

reduce emissions and improve fuel economy. 

Intelligent Control – the Big Picture 

Numerous methods have been designed for choosing the operating point of an IC Engine 

in a parallel HEV. The concept presented in this section is based on choosing an 

operating point about an optimal operating point, based on the road load, the battery State 

of Charge (SOC), and the optimal operating point of an IC Engine. An optimal operating 

point (ideal case) is calculated based on the minimization of a criterion, of which fuel and 

emissions are contending parameters. Based on a set of weights, the relative importance 

of fuel economy and emissions is dynamically chosen, and an optimal torque that can be 

requested from the IC Engine is calculated. Based on the battery SOC constraints and the 

road load (driver’s request), the actual output torque of the IC Engine is computed. The 

remaining torque (at that speed) required to meet the road load is provided by a buffer, 

the Electric Motor (EM). The buffer may produce either positive or negative torque. This 

is the well-known concept of load-leveling. The basic idea is schematically shown in 

Figure 1. The strategy is implemented in the ADVISOR software, a backward simulator. 

Thus, actual implementation will be different from the way control is performed. But the 

basic concepts of control will remain the same. 
 9

Figure 1: Structure of the Powertrain Control System 

Instantaneous control 

This control strategy is an instantaneous control strategy. It is based on IC Engine 

parameters at the current instantaneous vehicle speed. Most vehicles today are built 

around a powertrain containing a fixed-ratio transmission. (Manual or Automatic). The 

IC Engine nearly always rotates at a speed determined by the rotational speed of the 

wheels (except while the clutch is slipping). If a continuously variable transmission is 

available, then the speed of the IC Engine is also a degree of freedom in control. In this 

project, we deal with fixed-ratio transmissions, where IC Engine speed control is not 

performed. This control strategy can also be coupled with a speed control scheme (shiftcontrol) if developed for a vehicle coupled to an automatic transmission. 

Why Fuzzy Logic? 

Intelligent control is performed with the help of Fuzzy Logic as a tool. Fuzzy Logic 

enables the development of rule-based behavior. The knowledge of an expert can be 

coded in the form a rule-base, and used in decision making. The main advantage of Fuzzy 

Logic is that it can be tuned and adapted if necessary, thus enhancing the degree of 

freedom of control. It is also a non-linear structure, and this is especially useful in a 

complex system such as an advanced powertrain. For more details on Fuzzy Logic 

Control (FLC), refer to Fuzzy Control by Passino and Yurkovich [7]. In essence a FLC is 

a natural extension of many of the rule based controllers implemented (via look-up 

tables) in many vehicles today. 

The Control Strategy 

The control strategy involves calculating the torque produced by the IC Engine based on 

various parameters such as road-load and battery SOC. This includes the calculation of an 

optimal torque based on contending IC Engine parameters, and deciding the actual torque 

output by later modifying the optimal torque based on road load and battery SOC. The 

control strategy is made scalable, and can be used with any parallel HEV model in the 
 10

ADVISOR 3.2 software. The controller programs are created using MATLAB script 

files, and can be modified by the user. The following Figure 2 indicates the position of 

the Fuzzy Logic Controller in the ADVISOR Simulink block. 

Figure 2: Base block diagram with fuzzy controller 

The structure of the Fuzzy Controller is shown below in Figure 3. 

Figure 3: Fuzzy Controller block components 
 11

Optimal Torque Computation 

The data for an IC Engine in the ADVISOR 3.2 software is in the form of a 2-

dimensional map, indexed by torque and speed. Information regarding fuel economy (g/s) 

and emissions such as CO, HC and NOX (g/s) is available for various speeds and torques. 

Shown below as a sample are the fuel use and emissions data for a 1.9L Turbo Diesel 

Engine available in the ADVISOR software. 

Figure 4: IC Engine emissions – a general trend 

As seen in the above figure, we can determine the parameters at all torques for any given 

speed, up to the maximum torque point. 

The process of finding an optimal point is shown in Figure 5 below. 
 12

Figure 5: Finding an optimal IC Engine operating point 

The four competing parameters in the determination of an optimum are fuel efficiency, 

NOX, CO and HC emissions. At any particular point in time, the simulation determines a 

speed of rotation for the IC Engine (based on the powertrain configuration and the current 

gear ratio). This is the speed at which the “instantaneous” optimization is performed. For 

the current speed, all possible torques that the IC Engine can provide are considered. The 

four competing parameters for all torques at the current speed are taken from the data 

maps. 

min ( (1 ) ) J = w1 −η + w2 NOx + w3CO + w4 HC (1) 

 

where J = cost function 

 wi = weights, i=1,2,3,4 

η = Normalized efficiency 

NOX = Normalized NOX emissions 

CO = Normalized CO emissions 

HC = Normalized HC emissions 

Here, the values are normalized with respect to the maximum for that particular speed. 

Relative weights are assigned to each parameter based on their importance. This is one 

large degree of freedom, and the weights must be selected for each IC engine based on 

their individual data maps. For our example in the report, we use the following default 

weights. 

Parameter Weight 

Efficiency 0.7 

NOX 0.3 

CO 0.1 

HC 0.1 

Table 1: Default weights in the control strategy 

The above weights provide an optimum based heavily on efficiency and NOX, while CO 

and HC are also considered. These weights can be varied during the operation of the 

vehicle, based on certain vehicle parameters. 
 13

COLD START: When the coolant temperature is still below acceptable values (cold 

start), emissions are very high, since the catalysts are not up to their operating 

temperatures yet. In this case, higher weights can be assigned to emissions. This increases 

the cost function near high-emission zones, and thus would be avoided when a minimum 

is found. This is seen to improve cold-start emissions considerably. The following are 

sample weights for a cold-start scenario. 

Parameter Weight 

Efficiency 0.25 

NOX 0.25 

CO 0.25 

HC 0.25 

Table 2: Weights during a cold start – prioritize emissions 

INADEQUATE TORQUE: The control strategy is primarily used to control the IC 

Engine torque output. The electric motor is used to perform load-leveling, i.e. provide 

extra torque or absorb excess torque, according to the driver’s torque demand. In case we 

determine that the motor has been providing it’s maximum torque during the previous 

time step, it most probably indicates that the IC Engine and the motor assisting together 

have not met the driver’s torque request. This warrants some increase in IC Engine 

torque, so as to satisfy the driver’s torque demand. In this case, efficiency is given higher 

priority, since high efficiencies occur near higher torque regions. It must be noted that 

this case may not work for all IC Engines. For engines who’s peak efficiency at each 

speed is not near high torque, these weights cannot be used. 

Parameter Weight 

Efficiency 1 

NOX 0.1 

CO 0.1 

HC 0.1 

Table 3: Weights during high torque demand – compromise emissions 

These are sample weights and can be modified to suit a particular IC Engine. The 

following plots show the effect of choosing the weights on the position of the optimal 

torque point. 

The following plots in Figure 6 show the optimum operating torques for the contending 

parameters taking them individually into consideration. This is obvious when we refer to 

Figure 4, which shows us a 3-D map of the same parameters. The dotted lines indicate the 

optimal torques at the various speeds based on the weights given therein. 
 14

Figure 6: Optimal IC Engine torques based on individual weighting 

Some inferences can be made from taking the above plots to indicate a general trend: 

- We cannot operate always at the peak efficiency point, since it is bound to lie near 

the maximum torque envelope (Wide open throttle line). This would lead to 

excessive fuel consumption and heavy charging due to load-leveling. It may result 

in damage to the battery, if excess charging is not controlled. 

- We cannot optimize for NOX alone, since NOX appears to be lowest at zero torque 

values. 

Note: An optimal solution is always a compromise. Since fuel economy and emissions 

are usually contending parameters, there is a trade-off obtained between these 

parameters. But from simulations, we can obtain a fairly considerable decrease in 

emissions with only a negligible loss in fuel economy. Another important advantage of 

this method is the ability to control any one of the parameters decisively, by increasing 

their relative weights. 

If we equally weight the importance of the four contending parameters, then we get an 

optimal curve as given in Figure 7. 
 15

Figure 7: Optimal IC Engine torque based on equal weighting 

When a cost function is calculated for a particular speed, a minimum is found for all 

torques from zero to the maximum torque at that speed. The following figures show us 

the cost function and the minimum for a particular set of weights given in Table 1. 

Figure 8 shows us the cost function for the Optimal point at 1000 rpm. Figure 9 below 

shows the cost function and minimum (optimal) at 3000 rpm. 
 16

Figure 8: Sample cost function minimization at 1000 rpm 

Figure 9: Sample cost function minimization at 3000 rpm 
 17

From the above plots, we see that an optimal torque based on our definition is calculated. 

It must be noted that ADVISOR IC Engine data may be given for points above the 

maximum torque envelope. This algorithm takes the optimal, considering only the region 

between zero and maximum torques. 

The Simulink block associated with the optimal torque calculation is shown in Figure 10. 

Figure 10: Optimal torque computation block diagram 

This optimum is fed in to the Fuzzy controller, where the driver’s demand and the battery 

SoC are also considered. Running the engine at the calculated optimal torque may not 

satisfy either the driver’s demand or the charge-sustaining requirement of the HEV. Thus, 

adjustment of this ‘optimal’ torque is necessary, and is discussed in the following section. 

Effect of the Driver’s torque request and battery SOC 

In powertrain control, the bottom line is that the vehicle must follow the driver’s 

command. Torque requested from the powertrain must be met at all times. If the driver 

demands very low torque (indicated by small throttle pedal angles), such as while 

cruising, and the optimum IC Engine torque at that speed is near high torques, the IC 

Engine must be commanded to operate below optimum, to satisfy the road load and also 

to prevent the Electric Motor from generating large negative torques. This also depends 

on the battery SOC. In the above case, the higher the battery SOC, the less capable the 

Electric Motor is of generating electricity and thus load-leveling the IC Engine. That is 

where the main Fuzzy Logic controller is actively used. 

Similarly, if the driver demands very high torque, such as during an acceleration, and the 

optimum found based on the weights at that speed happens to lie near low torques, 

deviation from the optimal is warranted. This is due to the fact that satisfying the driver’s 

demand is the primary concern. In this case, if the battery is sufficiently charged, the IC 

Engine can still be allowed to operate at lower torques (near optimal) and the Electric 

Machine can be used to assist. If the battery SOC does not look promising, then deviation 
 18

from the optimal point by a large amount is inevitable. These non-linearities and 

exceptions are handled by the fuzzy logic controller. 

A schematic of the main FLC is shown in Figure 11. 

Figure 11: Main fuzzy logic controller 

Fuzzy Logic Control for SoC Correction 

A Fuzzy Logic Controller (FLC) is used to decide the operating point of the ICE. It 

follows the idea of load leveling, where the EM is used to provide assist or generate, 

while running the ICE at an optimum. The idea behind this approach is to vary an optimal 

ICE torque, based on SOC constraints. This concept has been explored in other ways, 

such as by Baumann et al [1]. One may refer to [1] for an in-depth explanation of fuzzy 

logic control and it’s application to HEV control. Care is taken in the rule-base to make 

sure the FLC does not shift too much from this optimal point so as to compromise the 

overall efficiency of the system. It must be remembered that one of the constraints is to 

remain charge-sustaining, and this is implemented in the from of the user inputting 

certain SOC limits within which operation is desired. 

Figure 12 depicts the operating principles of the main FLC. The FLC receives 2 inputs, 

the required driveline torque (engine + motor) and the battery pack SOC. Each input and 

output has 11 triangular membership functions, giving a total of 121 rules. The desired 

driveline torque is first fed as a request to the IC Engine, and is scaled from ‘1’ to ‘11’, 

‘1’ representing zero torque, ‘6’ representing the calculated ‘optimal’ ICE torque and 

‘11’ representing maximum ICE torque at that speed. To elaborate further, a driveline 

torque input of ‘1’ indicates that the driver request torque that is very low, near zero 

torque output of the IC Engine. If the driver’s demand is at the calculated ‘optimal’ ICE 

torque, the FLC input value is set to ‘6’. If the driver’s demand is ‘11’, it indicates that 

the required driveline torque is at the maximum IC Engine torque (at that speed), or 

higher (due to saturation of fuzzy membership functions at 11). To perform the scaling of 

the driveline torque (first input to the FLC), it is compared to the calculated ‘optimal’ 

torque at each time step. It must be noted that the calculated ‘optimal’ torque (value ‘6’) 

itself varies every time step, based on the weights and the minimization criterion. 

Similarly, SoC is scaled from ‘1’ to ‘11’, ‘1’ corresponding to the lowest SOC limit and 

‘11’ corresponding to the highest SOC limit, as set by the user. The general logic behind 

the main FLC is described in Table 4. Of course, the actual simulated controller is in 

more detail, with 121 rules, and displays some non-linear behavior. 
 19

Figure 12: Concept of operating about optimum torque 

In Fuzzy Logic terminology, the universe of discourse defines the range of values that an 

input or an output can take. In our case, the universe of discourse can take values from 

‘1’ to ‘11’. In our control strategy, we modify the scale of our universe of discourse of 

the first input to the FLC (driveline torque), based on the calculated ‘optimal’ torque. 

Further reference on Fuzzy Logic and the associated terminologies can be found in Fuzzy 

Control by Passino and Yurkovich [7]. 

Example case 1 – Let us consider a case, when the required torque is above the optimal 

torque point, as seen by the “X” in Figure 12 (case 1). Let us assume that the SOC is 

high. We would like to bring the ICE operating point near the optimal torque point 

marked ‘6’ (for that speed). This would mean a lower torque output by the ICE than what 

is required to meet the driver’s demand. This requires that the EM be run as a motor to 

make up for the remaining torque, provided there is enough battery charge. Since we do 

have sufficient charge in this case, the ICE is allowed to operate near the optimal torque 

point. 

Example case 2 – Let us consider another case, when the desired torque is below 

optimum at point 2 (‘X’ in Figure 12), and the battery SoC is very low. We would like to 

increase the ICE torque output, and bring it near the optimum. This would require loadleveling by the EM (EM functioning as a generator), so as to output only what the driver 

demands. This is possible only if the SOC is not high. In our case, we have low SOC, and 

thus we can run the EM as a generator, while running the ICE at the optimum. In both the 

cases, the EM torque is represented by TEM = TTotal - TICE. In this case, TEM is negative, 

generating some energy into the battery pack. 
 20

Inputs Low SoC Optimum SoC High SoC 

Desired torque 

lower than optimal 

↑ TICE 

EM as gen. 

↑ TICE near optimum

EM as gen (small) 

↓↓ TICE

EM as motor 

Desired torque is 

optimal 

↑ TICE

EM as gen 

↔ TICE

↔ EM 

↓ TICE

EM as motor 

Desired torque 

higher than optimal 

↑↑ TICE

EM as gen 

↓ TICE near optimum

EM as motor (small) 

↓ TICE

EM as motor 

Table 4: Overview of the control strategy 

The structure in Table 4 is used for this FLC. The “desired ICE torque “ can be 

summarized as a SoC adjusted optimal ICE torque, based on efficiency and emissions 

along a particular speed. 

Figure 13: Visualization of the Fuzzy Controller action 
 21

Predictive control 

Ideally, it is possible to minimize fuel and emissions along a trip by finding a global 

optimum if we know the entire trip information before hand. But in reality, we have no 

such facility. This problem can be circumvented with knowledge of the type of obstacles 

that will be faced in the near future, such as heavy traffic, or a steep grade etc. We can 

then perform control actions now, to account for those situations in the future. For 

example, if we are on a highway entering a city where we expect to encounter heavy 

traffic, it is useful to charge the batteries now to be able to use the motor later for city 

driving (possibly drive all-electric). Global Positioning Systems (GPS) have become a 

commonplace in vehicle navigation systems, installed in many vehicles around the world. 

Most systems pinpoint the location of the vehicle overlaid on top of a map of the 

surrounding area, along with traffic information, and topographical data such as elevation 

etc. Newer systems like General Motor’s OnStar navigation system [8] allow the user to 

plan a route to a destination and also obtain information along that path. We propose an 

algorithm to implement HEV control making use of the wealth of data available about the 

surroundings and specifically along a planned route. 

A predictive control strategy is being developed at Ohio-State the aim of achieving a 

higher degree of control over the emissions and fuel economy over an entire trip of a 

vehicle. Most Geographical Information Systems (GIS) have topographical information 

which contain details about the neighboring terrain. If a trip is planned on such a 

navigation system, information regarding the elevation along the route ahead can be 

accessed from a GIS database (perhaps available on a CD) and can be used in 

determining the change in elevation along that route. When coupled with speed 

predictions from traffic conditions along that route (highway / city traffic), this set of 

information can be used to perform control at the current instant, to prepare for 

adversities in the future. A general overview of the concept is shown in Figure 14. 

Figure 14: Predictive control algorithm (supervisory control structure) 
 22

Future State Prediction Algorithm 

Figure 14 displays the supervisory control system structure, which is explained below. 

An instantaneous control strategy is used to determine the optimal ICE torque 

contribution for the current speed. This is based on the ICE efficiency and emission 

maps, the battery state of charge and the total driveline torque required, as described 

earlier in this report. Another controller called the navigation controller or predictive 

controller varies the instantaneous controller parameters based on implied future states. A 

Fuzzy logic controller is used in each of the 2 cases, to enable rule-based behavior and to 

implement non-linear control. The inputs to the navigation controller are speed and 

elevation of sampled points along a pre-determined route, from a navigation system. This 

controller decides to perform control action now, based on the recent history of the 

motion of the vehicle and the changes to this motion in the near future. An example of 

this would be to charge the batteries now, if a hill is to be encountered in a few minutes, 

so as to allow the vehicle to climb up at acceptable speeds then. This concept is currently 

being developed, and the files / programs for this module will be available to NREL 

soon. A symbolic representation of the predictive controller is shown in Figure 15. 

Figure 15: Predictive controller inputs and output 

PREDICTION OF STATE – A Fuzzy Logic Controller (FLC) is used to predict the 

future state of the vehicle. The information supplied regarding the future is a sampled set 

in a look-ahead window along a planned route. Assuming a planned route ahead of us, we 

determine a look-ahead window, along which we sample to obtain elevation and traffic 

information. Traffic information is in the form of speed (of traffic flow) at those sampled 

points. 

Figure 16: Look-ahead window 
 23

Figure 16 shows the region of look-ahead for the prediction algorithm. The value of lookahead length and the number of samples in the region are set based on optimal values for 

a given drive cycle and perhaps vehicle configuration. 

SAMPLED POINT – At each sampled point, we can obtain traffic information (speed of 

vehicles) and elevation at that point. The speed state of the vehicle in the look-ahead zone 

is taken as the average of the sampled points. Similarly, the elevation of the look-ahead 

zone is taken as the average of the elevation at the sampled points. 

Future _ State = mean(sampled _ states) (2)

PREDICT CHANGE – To implement control, we need to determine if the future vehicle 

states vary from the current state. The predictive algorithm cannot be implemented in all 

cases. For example, there may be no gain in charging the batteries for future city traffic, 

if the vehicle is already in heavy traffic. To determine the current vehicle state, a 

weighted average of the past velocities over a look-behind interval is calculated. In actual 

implementation, the previous n-speeds can be stored in memory and a weighted average 

calculated. The look-behind time scale is easy to determine, since a time averaged value 

is used, and most recent velocities will have a higher impact. 

Figure 17: Look-behind window 

Note that in the first few seconds of a trip, we assume that there is not enough 

information to determine the vehicle speed, and thus the prediction algorithm is switched 

off initially. 

∑

∑=

i

i i

weight

weight velocity Speed*

Recent _ (3)

The inputs to the fuzzy logic controller are the change in vehicle speed corresponding to 

recent speeds and the change in elevation. The change in elevation is calculated with 

respect to the current vehicle elevation. 

Note that in implementation, if at any point, even if traffic information service may not 

be available, the prediction algorithm based on elevation alone can be implemented since 

navigation systems can carry terrain data from a Geographic Information System (GIS). 
 24

Figure 15 shows the FLC with 2 inputs and a “GPS signal” as the output. This “GPS 

signal” is designed to carry a range from +1 to –1. The aim of the prediction algorithm is 

to tell the main controller to charge or discharge the batteries now, to account for future 

vehicle states. This “GPS signal” is used to indicate the control logic, with ‘+1’ 

indicating a desire to charge the batteries now, for future heavy EM use (such as stopand-go traffic). Similarly, a ‘ –1’ indicates a desire to deplete the batteries now, in the 

hope that the EM will receive regenerative energy soon along the planned route (such as 

a downhill route). 

NAVIGATION FLC CONTRUCTION– The FLC used in the above algorithm has 2 

inputs and 1 output. Each input and output has 11 triangular membership functions, 

giving a total of 121 rules. For the change in speed, ‘1’ corresponds to slower traffic 

ahead, and ‘11’ corresponds to faster traffic ahead. Similarly, for the change in elevation, 

‘1’ corresponds to a steep down grade, while ‘11’ corresponds to a steep climb. The 

minimum condition is used for the premise calculation and Center of Gravity (CoG) is 

used for the output inference. The range of the universe of discourse for each of the 2 

inputs and 1 output is scalable. This is useful in tuning the fuzzy controller to different 

requirements. The rules base is created based on heuristics on how a vehicle should react 

to oncoming states. The following is a general overview of the rule base. 

Condition 

AHEAD 

Going 

downhill 

Constant 

elevation 

Going 

uphill 

Slower traffic 

(city) 

Do 

nothing Charge Charge more 

Similar 

traffic Discharge Do 

nothing Charge 

Faster traffic 

(highway) 

Discharge 

more Discharge Do nothing 

Table 5: Rule base for predictive FLC 

Table 5 shows the general trend of the rules in the controller. Detailed non-linear 

behavior is implemented in the actual rule base, which is an 11x11 matrix. 

A sample rule - For example, consider the case when the navigation system indicates a 

“Downhill grade”, and “heavy (slower) traffic” ahead. It is more efficient to use the EM 

in slower traffic, and thus a higher batter charge in the future is desired. This may warrant 

running the EM as a generator now. But a downhill grade into the ‘future zone’ will 

automatically create an opportunity to regenerate some energy. Thus, our controller does 

not command any specific charge now from the EM. 

UPDATE FREQUENCY – The predictive algorithm from the navigation system is used 

to vary the torque split control strategy, but is implemented only at discrete times. The 

algorithm updates must occur at optimal times only. An algorithm that updates very 
 25

frequently might defeat the purpose of prediction, not giving enough time for control. 

This time is a variable in simulation, and we may choose to update the control every 

look-ahead period. In implementation, the distance along the planned route can be taken 

as a factor in dynamically choosing this look-ahead window. 

Currently (as of December 2001), this algorithm is still being tuned, and thus will be 

made available as soon as it is completed. For further details, please refer to Rajagopalan 

et al [10]. 

Results 

To demonstrate our control algorithm considering efficiency and emissions, a sample 

small parallel HEV with the following parameters is chosen. All the data are taken from 

existing sources in the ADVISOR 3.2 software. 

• 34 kW CI Engine (scaled from 1.9L 67 kW Wolkswagen ICE tested by ORNL) 

• 25 kW AC Solectria ACgtx20/AC300 Induction Electric Motor 

• Hawker Genesis 26 Ah Lead Acid Battery, 25 modules 

• Vehicle Mass = 1266 kg 

• Coeff. of drag = 0.335 

• Frontal area = 2 m2

The various parameters for the control strategy are entered as variables and are 

implemented in Simulink blocks. Static maps for efficiency and emissions of the ICE are 

used. 

We compare the emissions and fuel economy for various cycles. Some of the results are 

shown in the following sections. 

Urban driving cycle 

When tested with the urban driving cycle (UDDS), we see a significant improvement in 

the NOX emissions as well as some improvement in fuel economy. The emissions figures 

are compared in Table 6. 

Control Strategy Fuel Economy 

(mpg) 

CO Emissions

(g/mi) 

HC Emissions 

(g/mi) 

NOX Emissions 

(g/mi) 

ADVISOR Parallel Electric 

Assist strategy 59.3 0.113 0.044 0.988 

New fuzzy logic control strategy 63.9 0.107 0.036 0.784 

Table 6: Results - UDDS 

A visual comparison of emissions is shown in Figure 18. 
 26

Figure 18: Emissions results - UDDS 
 27

It is observed that many of the high emissions regions seen in the plots on the left side 

(default parallel strategy) are avoided in the plots on the right side (fuzzy control). The 

reduction in cold-start emissions is also achieved. 

Highway cycle 

In the highway cycle also, we see a significant improvement in the NOX emissions, with 

about the same fuel economy. 

Control Strategy Fuel Economy 

(mpg) 

CO Emissions

(g/mi) 

HC Emissions 

(g/mi) 

NOX Emissions 

(g/mi) 

ADVISOR Parallel Electric 

Assist strategy 68.2 0.043 0.023 0.82 

New fuzzy logic control strategy 70 0.042 0.018 0.523 

Table 7: Results – HWFET 

The following plots indicate the emissions from the highway cycle, for the base control 

strategy and the Fuzzy Logic control strategy. 
 28

Figure 19: Emissions results - HWFET 

As seen above in Figure 19, we see a large improvement in the NOX emissions. Coldstart emissions are reduced, as expected. 

US06 aggressive driving cycle 

The new algorithm is compared with the base strategy when run over the US06 driving 

cycle, and the results are tabulated in Table 8. 

Control Strategy Fuel Economy 

(mpg) 

CO Emissions

(g/mi) 

HC Emissions 

(g/mi) 

NOX Emissions 

(g/mi) 

ADVISOR Parallel Electric 

Assist strategy 48 0.045 0.033 2.179 

New fuzzy logic control strategy 48.7 0.047 0.034 1.639 

Table 8: Results – US06 
 29

The resulting emissions are plotted below. 

Figure 20: Emissions results – US06 
 30

As seen in Figure 20 and Table 8, NOX emissions can be reduced without much 

compromise on fuel economy. 

The change in overall SOC was considered in the above results. As expected, both 

strategies were able to charge sustain between the sample range of 0.55 to 0.45 SOC. The 

order of change in SOC for the 2 strategies were similar. It is observed from these results 

that it is easy to control NOX emissions, as long as we avoid very high torque operating 

points, and points near the wide-open-throttle (WOT) range. But when it becomes 

necessary to charge the batteries or to produce extra torque, NOX emissions have to be 

compromised. The ability to vary the weights in the strategy enables us to prioritize the 

emissions of our choice. 
 31

WILLANS’S LINE MODEL OF AN INTERNAL COMBUSTION ENGINE 

Introduction 

The Willan’s line model is a scaling technique used to create numerical models of IC 

Engines of virtually any desired size. It is a good quasi-static representation of the fuel 

efficiency of CI/SI Engines. It’s advantages lie in it’s scalability within engines of a 

certain class. However, this technique does not take into account several aspects of IC 

engines such as detailed engine geometry, fuel injection, thermal effects, engine 

dynamics, etc. Also, emissions are not modeled using this technique. Details on the 

Willan’s Line model can be looked up on Xi Wie et al [8]. 

Modeling 

In the Willan’s Line Model, the energy conversion efficiency is represented as the ratio 

between the input and output power. The schematic in Figure 21 shows the affine 

relationship between input and output power. 

Figure 21: Input – output relationship in an IC Engine 

The basic idea is to non-dimensionalize certain engine parameters such as torque and 

speed, so as to obtain a linear relationship between torque, speed and fuel use. The speed 

of the engine is represented as ‘mean piston speed (m/s)’ instead of ‘rpm’ and torque is 

represented as ‘mean effective pressure (N/m2

)’ instead of ‘Nm’. The basic empirical 

equations used in the Willan’s line technique are as follows: 

Symbols Used: 

PME - Mean Effective Pressure 

PMF - Mean Fuel Pressure 

PMR - Mean Friction Pressure 
 32

C M - Mean Piston Speed 

Vd - Engine displacement 

η e - Engine Efficiency 

η f - Fuel conversion Efficiency 

Te - Torque produced by the ICE 

ij a - Coefficients of fuel-conversion efficiency 

PMRi - Coefficients of Friction pressure 

PMAXi - Coefficients of Max. torque production 

QLHV - Lower calorific value of fuel 

mf  - fuel flow rate (g/s) – used in data maps 

Mean effective Pressure: 

d

e

ME V

T

P

4π = (4) 

Mean Piston Speed: 

π

Sω Cm = (5) 

Fuel Conversion Efficiency: 

f m m Cm PMF (a a C a C ) (a a ) 10 11

2

η = 00 + 01 + 02 − + (6) 

Mean friction Pressure: 

2 PMR ≅ PMR0 + PMR2Cm (7) 

Maximum Torque envelope: 

3

3

2 PMAX = PMAX 0 + PMAX1Cm + PMAX 2Cm + PMAX Cm (8) 
 33

Mean Fuel Pressure: 

f

ME MR

MF

P P P

η

( − ) = (9) 

Engine Efficiency (Used in the ADVISOR data files) 

MF

ME

e P

P

η = (10) 

Fuel flow map (final result, used in the ADVISOR software) 

LHV

MF d

f Q

P V

m

π

ω

4  = (11) 

Using the above equations, we calculate the efficiency map of the IC Engine based on the 

availability of scaling coefficients. Scaling coefficients for a set of CI and SI Engines are 

obtained from researchers at the Center of Automotive Research (CAR) at the Ohio-State 

University. Scaling is best done when a given engine size is scaled from another engine 

of similar size, and more importantly, similar speed range, to avoid problems due to 

extrapolation. Data for more engines can be added to the database, since it is in the form 

of a matrix. Further, the database is split between SI and CI engines. To add more data 

from other engines to the database, one can use the Willan’s Line algorithm to produce a 

set of coefficients for that particular engine, and add them to the database. The database 

is defined in the file FC_WILLANS_LINE.M. 

Usage 

To use the modeling feature, the user is required to input the following parameters 

pertaining to the desired IC Engine. 

• Type of Engine (SI / CI) 

• Cylinder bore diameter (m) 

• Cylinder stroke length (m) 

• Number of cylinders (--) 

• Idle speed (rpm) 

• Red Line (rpm) 

The algorithm then calculates the volume of the engine. Based on the speed range and 

volume, the algorithm scales an existing engine from the database and produces a fuel-
 34

flow map, which can be used in the appropriate Simulink block to calculate the fuel 

consumption of the IC Engine. 

Results 

Sample hypothetical IC Engine parameters are used in the program and the corresponding 

scaled data maps for fuel flow (in g/s) are produced. The results of these sample engines 

are given below: 

Example 1: 

Type: SI Engine 

Capacity: 3.0 L 

Idle: 700 rpm 

Redline: 5000 rpm 

The following fuel flow map is produced: 

Figure 22: ICE fuel flow map for a scaled 3.0 L SI Engine 
 35

Figure 23: ICE Efficiency map for a scaled 3.0 L SI Engine 

The fuel flow map is shown in Figure 22. It is seen that from simple IC engine 

parameters such as dimensions and engine type, we obtain a reasonable fuel-flow map, 

scaled from a database of Engines. 

Example 2: 

Type: CI Engine 

Capacity: 4.0 L 

Idle: 700 rpm 

Redline: 4000 rpm 

The following fuel flow map is produced: 
 36

Figure 24: ICE fuel flow map for a scaled 4.0 L CI Engine 
 37

Figure 25: ICE efficiency map for a scaled 4.0 L CI Engine 

The fuel-flow map (in g/s) for a 4.0 L CI Engine, scaled from similar engines is shown in 

Figure 24. As already seen, an acceptable fuel flow map is produced, with the 

specification of just a few IC engine parameters. 

NOTE: The Willan’s Line model is a technique used to produce the fuel-flow map for IC 

engines by scaling existing coefficients / parameters of IC Engines. It is not guaranteed 

that the fuel flow map and / or the efficiency map are accurate. Some IC Engine 

parameters obtain more accurate results than others. The model should be used diligently, 

and should be applied only when the scaled results are reasonable. 
 38

IMPLEMENTATION IN THE ADVISOR 3.2 SOFTWARE 

A total of 3 sets of changes are made to the ADVISOR 3.2 software. First, there is a set 

of files (PTC_FUZZY_EMISSIONS.M control strategy and 

BD_FUZZY_EMISSIONS.MDL Simulink Block diagram) added for the Fuzzy Logic 

strategy with emissions (without Predictive control). 

Also, development on a predictive control strategy based on GPS information is 

underway at the Ohio-State University. As a framework for this Predictive control 

strategy, a new set of files (PTC_FUZZY_EMISSIONS_GPS.M control strategy and 

BD_FUZZY_EMISSIONS_GPS.MDL Simulink Block diagram) is created, but the core 

predictive algorithm is not provided, and will be released when totally completed. 

A new fuel_converter file is added to implement the Willan’s line model for IC Engines. 

(FC_WILLANS_LINE.M) 

To implement the algorithms in the ADVISOR software, some changes and additions are 

made to the data files. All files and variables are coded and named according to the 

conventions followed in the ADVISOR 3.2 software. The conventions used are explained 

henceforth. 

Emissions Control Strategy 

A sample vehicle is defined with a choice of powertrain parameters. The Emissions 

control strategy contains the following files and variables. 

File name Function 

fuzzy_emissions_in.m ADVISOR 3.2 input file that contains the 

emissions control strategy vehicle configuration to 

be loaded with the GUI. 

bd_fuzzy_emissions.mdl The main block diagram that is used when the new 

fuzzy logic controller (ptc_fuzzy_emissions.m) is 

selected from the GUI interface 

fuzzy_target_compute_emissions.m Calculate the optimal torque for a given IC Engine 

speed, based on a set of weights. Also varies the 

weights based on vehicle parameters. 

ptc_fuzzy_emissions.m This file contains the powertrain control variables, 

including the battery SOC limits. When selected, 

uses the file bd_fuzzy_emissions.mdl to simulate 

the vehicle. 

mfuzzy_emissions.m The main Fuzzy Logic Controller which takes in 

the optimal torque and SOC as inputs and gives the 

actual torque as the output. This is used to shift the 

calculated optimal torque point based on the 
 39

driver’s request and the Battery SOC. Care is taken 

to control the deviation to a minimum, since a large 

correction from optimal destroys the purpose of 

optimization. The rule-base can be modified by the 

user if desired. 

Block_diagram_name.m Modifications are made to this file so as to enable 

the respective fuzzy logic Simulink blocks to be 

used, when the fuzzy powertrain controller is 

selected. 

Table 9: Files added in the ADVISOR 3.2 software – Emissions control 

Predictive Control Strategy with Emissions 

For the predictive controller, the framework is supplied in the ADVISOR 3.2 software. 

The main algorithm is under development, and the m-file with the algorithm will be 

released when completed. A sample vehicle is defined with a similar choice of powertrain 

parameters as the previous section (without predictive control). The predictive control 

strategy contains the following files and variables. 

File name Function 

fuzzy_emissions_gps_in.m ADVISOR 3.2 input file that contains the 

emissions control strategy vehicle configuration to 

be loaded with the GUI. 

bd_fuzzy_emissions_gps.mdl The main block diagram that is used when the new 

fuzzy logic controller 

(ptc_fuzzy_emissions_gps.m) is selected from the 

GUI interface 

fuzzy_target_compute_emissions.m Calculate the optimal torque for a given IC Engine 

speed, based on a set of weights. Also varies the 

weights based on vehicle parameters. 

ptc_fuzzy_emissions_gps.m This file contains the powertrain control variables, 

including the battery SOC limits. When selected, 

uses the file bd_fuzzy_emissions.mdl to simulate 

the vehicle. 

mfuzzy_emissions.m The main Fuzzy Logic Controller which takes in 

the optimal torque and SOC as inputs and gives the 

actual torque as the output. This is used to shift the 

calculated optimal torque point based on the 

driver’s request and the Battery SOC. Care is taken 

to control the deviation to a minimum, since a large 

correction from optimal destroys the purpose of 

optimization. 

Block_diagram_name.m Modifications are made to this file so as to enable 

the respective fuzzy logic Simulink blocks to be 

used, when the fuzzy powertrain controller is 

selected. 
 40

gps_predict.m Currently a dummy file which outputs a null value. 

This file will contain the actual predictive control 

parameters for implementation when released. 

Table 10: Files added in the ADVISOR 3.2 software – Predictive control 

All the MATLAB and SIMULINK files created for the contract are designed in similar 

lines to existing .M files in the ADVISOR 3.2 software. Comments and explanations are 

included where applicable. 

Sample Driving cycles 

To test the predictive strategy, 4 sample driving cycles are created from existing driving 

cycles, to simulate change in traffic or elevation for the route ahead. These 4 files are to 

be used only with the predictive control strategy. They are labeled as follows: 

File name Function 

CYC_GPS1.M Combines the HWFET, NREL2Vail, UDDS and 

the Vail2NREL cycles. 

CYC_GPS2.M HWFET followed by the UDDS 

CYC_GPS3.M UDDS followed by the HWFET 

CYC_GPS4.M UDDS with arbitrary elevation 

Table 11: Sample driving cycles for the predictive algorithm 

Willan’s Line Model 

For the Willan’s line model, we have an M-file which contains a sample configuration, 

and the parameters for generating a new efficiency map. The user can use the template 

and create his own Fuel_converter input file. 

Figure 26: Use of the Willan’s line model 
 41

Conventions for Variables 

The conventions for naming variables used in the 3 new algorithms are described below 

in Table 12. 

Variable Prefix Function 

flc_* Used in the Fuzzy Logic Control algorithm 

gps_* Used in the predictive control algorithm. Many GPS_* 

variables are Global variables. 

fc_wlm_* Used in the Willan’s Line model of an IC Engine 

fc_wlm_*_ci Willans Line model data pertaining to CI engines 

fc_wlm_*_si Willans Line model data pertaining to SI engines 

Table 12: Conventions for Variables 
 42

SUMMARY OF CONCLUSIONS 

This report describes the development of new control strategies and models for Hybrid 

Electric Vehicles (HEV) by the Ohio-State University, on a contract with the National 

Renewable Energy Laboratory (NREL), Golden, CO. The contract was assigned to the 

Center for Automotive Research and Intelligent Transportation (CAR-IT) by the Center 

for Transportation Technologies and Systems (CTTS) at the National Renewable Energy 

Laboratory. The report indicates results from models created in NREL’s ADvanced 

VehIicle SimulatOR (ADVISOR 3.2) software. Also presented are the results of a 

scalable IC Engine model, called the Willan’s Line technique, implemented in the 

ADVISOR 3.2 software. 

The project was aimed at the development of Advanced Control Techniques for Hybrid 

Vehicles. Techniques such as Fuzzy Logic were used in the implementation of the 

complex control problem. An optimal torque split between the Internal Combustion 

Engine (ICE) and the Electric Motor is the outcome of the new algorithm. The new 

control algorithm also takes into account emissions from the IC Engine in calculating the 

torque produced. The Willan’s line model was used to produce a scaled fuel flow map of 

an Internal Combustion Engine of any arbitrary size. 

The following are the highlights of the new control algorithm: 

1. Optimization structure for contending parameters such as emissions and fuel 

efficiency. 

2. Flexibility in assigning a relative importance between the various emissions and 

fuel economy. 

3. Non-Linear correction for IC Engine torque output based on battery State of 

Charge (SOC). 

4. The scalable control strategy embedded in the ADVISOR 3.2 software can be 

used for all Parallel Hybrid Electric Vehicle configurations. 

This document reveals that effective control of emissions can be performed even under 

demanding road conditions. An optimality issue exists when dealing with various 

emissions, but any specific emission characteristic can be effectively targeted by 

modifying the relative importance of that emission parameter. 

Changing relative importance between efficiency and emissions during the vehicle 

operation is useful in curtailing excess output of a particular parameter at certain 

instances. An example would be to heavily penalize operating points with high emissions 

when the engine is still cold. This mechanism is found to effectively reduce cold start 

emissions. 
 43

Future work may include bringing other vehicle parameters into the optimization criteria 

such as battery State of Charge (SOC), etc. and the addition of intelligent algorithms that 

modify an existing control strategy based on information about the future route of the 

vehicle. Vehicle navigation systems these days can pinpoint the current location of the 

vehicle and allow planning routes to a destination. Information along that planned route 

may be used in the control strategy to aim at optimizing performance for an entire trip. 

These algorithms are currently being studied by faculty and students at the Ohio-State 

University. 

An algorithm to produce the fuel-use maps of IC Engines is implemented in the 

ADVISOR 3.2 software. This algorithm, called the Willan’s Line Model, is based on the 

scaling of parameters from engines with similar characteristics. A database of the 

parameters of the Willan’s Line model for SI and CI engines are included in the software. 

There is a provision for adding parameters of newer engines into the database if 

available. The advantage with this algorithm lies in the fact that the user obtains a fuel–

use map, from entering some basic engine parameters such as stroke length, bore 

diameter, etc. 
 44

REFERENCES 

1. Bernd Baumann, Gregory Washington, Brad Glenn, Giorgio Rizzoni, 

“Mechatronic Design and Control of Hybrid Electric Vehicles”, IEEE/ASME 

Transactions on Mechatronics, Vol 5, No 1, March 2000. 

2. Valerie Johnson, Kieth Wipke and David Rausen, “HEV Control Strategy for 

Real-Time Optimization of Fuel Economy and Emissions”, SAE 2000-01-1543.

3. K.B. Wipke, M.R. Cuddy, S.D. Burch, “ADVISOR 2.1: A User-Friendly 

Advanced Power train Simulation Using a Combined Backward/Forward 

Approach”, NREL Technical Report, September 1999. 

4. N. Jalil, A. Kheir, M. Salman, “A rule-base energy management strategy for a 

series hybrid vehicle”, Proc. of American Control Conference, pp. 689-693, June 

1997. 

5. Hyeoun-Dong Lee, Seung-Ki Sul, “Fuzzy Logic based Torque control strategy for 

parallel type HEV”, IEEE Transactions on Industrial Electronics, vol 45, no.4, pp 

625-632, 1998. 

6. Bradley Charles Glenn, “Intelligent Control of Parallel Hybrid Electric Vehicles”, 

MS Thesis, The Ohio-State University, 1999 

7. Kevin Passino, Steve Yurkovich, “Fuzzy control,” Addison-Wesley Pub. Co., Sep 

1997. 

8. Xi Wei, Giorgio Rizzoni, “A scalable approach for energy converter modeling 

and supervisory control design,” Proc. of ASME IMECE’2001, November 2001. 

9. On-Star Navigation System, General Motors, www.onstar.com

10. Arun Rajagopalan, Gregory Washington, “Intelligent Control of hybrid electric 

Vehicles using GPS information,” SAE Future Car Congress 2002, Arlington VA, 

June 2002. 
 45

APPENDIX 

Figure 27: Urban Dynamometer Driving Schedule (UDDS) 
 46

Figure 28: Highway Fuel Economy Test (HWFET) 

Figure 29: US06 aggressive driving cycle 
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Development of Fuzzy Logic and Neural Network Control and Advance Emissions 

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This report describes the development of new control strategies and models for Hybrid Electric Vehicles (HEV) by the Ohio 

State University on a contract with NREL. The report indicates results from models created in NREL’s ADvanced VehIcle 

SimulatOR (ADVISOR 3.2), and results of a scalable IC Engine model, called in Willan’s Line technique, implemented in 

ADVISOR 3.2 

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Unclassified

20. LIMITATION OF ABSTRACT

UL 

 NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

Prescribed by ANSI Std. Z39-18 

298-102