Arnett Thesis

7/30/2019 Arnett Thesis

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Modeling and Simulation of a Hybrid Electric

Vehicle for the Challenge X Competition

Submitted to:

The Engineering Honors Committee

119 Hitchcock Hall

College of Engineering

The Ohio State University

Columbus, OH 43210

By

Michael Arnett

4091 Millsboro Rd WMansfield, OH 44903

Dr. Giorgio Rizzoni, Advisor

May 20, 2005


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Abstract:

As the market shifts toward larger vehicles and growing concerns regarding

petroleum consumption and emissions emerge, automakers have begun to explore new

vehicle propulsion solutions. General Motors and The Department of Energy have joined

together to create the Challenge X competition to explore hybrid-electric vehicles as one

such solution. Seventeen teams across the United State will experience real-world HEV

development over the three year competition. This process begins with vehicle

architecture selection, modeling and simulation. A dynamic model of a hybrid-electric

powertrain is developed here. This model is then implemented into two Simulinkbased

simulators: the quasi-static cX-SIM, the dynamic cX-DYN. These simulators are used to

validate the control strategy being developed for the Challenge X vehicle. Verification

will include optimal performance in regards to fuel consumption, battery state-of-charge,

and drivability. Techniques of validating the model and simulators using a rolling chassis

are also being implemented. Preliminary data from the quasi-static simulator and the

rolling chassis is presented herein.

Acknowledgements:

I would like to thank Dr. Giorgio Rizzoni, Joe Morbitzer, Osvaldo Barbarisi,

Kerem Koprubasi, Jason Disalvo, John Neal and Christopher C. Mabry, for all of the help

and support they have given me throughout the duration of this research.

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Table Contents

CHAPTER 1: INTRODUCTION ............................................................................................................... 6

1.1MOTIVATION ....................................................................................................................................... 61.2VEHICLE ARCHITECTURE ................................................................................................................... 8

1.2.1 Classifications of HEVs..............................................................................................................81.2.2 Vehicle Components.................................................................................................................. 101.2.3 Modes of Operation................................................................................................................... 11

CHAPTER 2: MODELING.......................................................................................................................14

2.1MODEL OF THE DRIVELINE .............................................................................................................. 142.1.1 Dynamic Equations of the Front Driveline.............................................................................. 152.1.2 Dynamic Equations of Rear Driveline..................................................................................... 172.1.3 Dynamic Equation of the Vehicle............................................................................................. 17

2.2DYNAMIC SIMULINKMODEL OF THE FRONT DRIVELINE ................................................................ 182.2.1 ICE Model ................................................................................................................................. 192.2.2 Clutch Model.............................................................................................................................192.2.3 Front Gearbox Model ...............................................................................................................202.2.4 Transmission.............................................................................................................................21

2.2.5 Front Axle................................................................................................................................. 222.2.6 Front Brakes.............................................................................................................................232.2.7 Front Wheels.............................................................................................................................24

2.3DYNAMIC SIMULINKMODEL OF THE REAR DRIVELINE .................................................................. 252.3.1 Electric Motor ...........................................................................................................................252.3.2 Rear Gearbox............................................................................................................................ 262.3.3 Rear Axle...................................................................................................................................272.3.4 Rear Brakes...............................................................................................................................282.3.5 Rear Wheels............................................................................................................................... 29

CHAPTER 3: SIMULATION RESULTS................................................................................................31

3.1CX-SIM ............................................................................................................................................. 313.1.1 Driver.........................................................................................................................................32

3.1.2 HEV Powertrain........................................................................................................................333.1.3 Vehicle.......................................................................................................................................343.2CX-DYN ............................................................................................................................................ 343.3CXGRAPHICS.................................................................................................................................... 36

3.3.1 Set Layout for Plots Driver .......................................................................................................373.3.2 Set Layout for Plots Vehicle...................................................................................................... 383.3.3 Set Layouts for Plots Acceleration Test.................................................................................... 393.3.4 Set Layout for Plots HEV Operation........................................................................................403.3.5 Set Layout Plots for Conventional Operation.......................................................................... 413.3.6 Set Layout Plots for Electric Powertrain.................................................................................. 42

CHAPTER 4: RESULTS & MODEL VERIFI CATION........................................................................45

4.1CX-SIM ............................................................................................................................................. 45

4.2CX-DYN ............................................................................................................................................ 524.3ROLLING CHASSIS............................................................................................................................. 524.3.1 Launch Test...............................................................................................................................534.3.2 Model Verification & Mapping.................................................................................................574.3.3 cX Test.......................................................................................................................................60

CHAPTER 5: CONTROL STRATEGY DEVEL OPMENT ..................................................................62

5.1OVERVIEW......................................................................................................................................... 625.2ECMS................................................................................................................................................ 635.3BATTERY STATE-OF-CHARGE .......................................................................................................... 64

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5.4DRIVABILITY ..................................................................................................................................... 65

REFERENCES...........................................................................................................................................67

APPENDIX ................................................................................................................................................. 68

List of Figures:

Figure 1. Ohio State Challenge X Vehicle Architecture [5]............................................. 10Figure 2. Dynamic Model of the Driveline [8]. ................................................................ 15

Figure 3. SimulinkDiagram of ICE & ISA....................................................................... 19

Figure 4. SimulinkDiagram of the Clutch. ....................................................................... 20Figure 5. SimulinkDiagram of the Front Gearbox. .......................................................... 21

Figure 6. SimulinkDiagram of the Automatic Transmission. .......................................... 22

Figure 7. SimulinkDiagram of the Front Axle. ................................................................ 23Figure 8. SimulinkDiagram of the Front Brakes. ............................................................. 23

Figure 9. SimulinkModel of the Front Wheels and Differential. ..................................... 24Figure 10. SimulinkDiagram of the Traction Motor. ....................................................... 26Figure 11. SimulinkBlock Diagram of the Rear Gearbox. ............................................... 27

Figure 12. SimulinkDiagram of the Rear Axle................................................................. 28

Figure 13. SimulinkDiagram of the Rear Brakes. ............................................................ 29

Figure 14. SimulinkDiagram of the Rear Wheels. ........................................................... 30Figure 15. cX-SIM Top Layer. ......................................................................................... 32

Figure 16. cX-SIM Powertrain Subsystem. ...................................................................... 33

Figure 17. cX-DYN Top Layer......................................................................................... 35Figure 18. cX-DYN Powertrain Subsystem. .................................................................... 35

Figure 19. cX Graphics Top Layer. .................................................................................. 37

Figure 20. Driver Plot Options Screen.............................................................................. 38Figure 21. Vehicle Plot Options Screen............................................................................ 39

Figure 22. Acceleration Test Parameter Selection Screen................................................ 40Figure 23. HEV Operation Plot Options Screen............................................................... 41

Figure 24. Conventional Powertrain Plot Options Screen. ............................................... 42

Figure 25. Electric Powertrain Plot Options Screen. ........................................................ 44Figure 26. Actual & Desired Velocity from cX-SIM Preliminary Simulation................. 46

Figure 27. Deviation of Actual & Desired Vehicle Speed of cX-SIM Simulation........... 46

Figure 28. Total Output and Requested Torque during the cX-SIM Preliminary

Simulation. ........................................................................................................................ 47Figure 29. Deviations Between Actual & Desired Torque of HEV Powertrain............... 48

Figure 30. ICE Operating Points during cX-SIM Preliminary Simulation....................... 49Figure 31. Operating Points of the EM during the cX-SIM Preliminary Simulation. ...... 50Figure 32. ISA Power during the cX-SIM Preliminary Simulation. ................................ 51

Figure 33. Battery SOC during cX-SIM Preliminary Simulation..................................... 52

Figure 34. Rolling Chassis Experimental Set-Up. ............................................................ 55Figure 35. Launch Test EM Motor Speed -40% Torque Limit. ....................................... 55

Figure 36. Launch Test Vehicle Speed- 40% Torque Limit............................................. 56

Figure 37. Launch Test EM Motor Speed--50% Torque Limit. ....................................... 56

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Figure 38. Launch Test Vehicle Speed-- 50% Torque Limit............................................ 57

Figure 39. EM Torque Mapping Test: Vehicle Speed (B=30). ........................................ 59Figure 40. EM Torque Mapping Test: Acceleration (B=30). ........................................... 60

Figure 41. cX Test Top Layer........................................................................................... 61

Figure 42. cX -Test Graphics............................................................................................ 61

Figure 43. Control Strategy Schematic. ............................................................................ 63

List of Tables:

Table 1. Summary of Challenge X Vehicle Technical Specifications [9, 5]...................... 7

Table 2. Vehicle Operating Modes [8]. ............................................................................ 12

Table 3. Nomenclature...................................................................................................... 68

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Chapter 1

Introduction

The motivation for this research stems from the growing concern of energy

consumption and environmental impacts of current automobiles. These issues have lead

to the creation of Challenge X and Ohio States participation in this competition. Hybrid-

electric vehicle architecture is developed using the general classifications of HEVs while

considering the advantages and disadvantages of each solution. The components to drive

this architecture and various modes of operation are also defined.

1.1 Motivation

As the global economy begins to strain under the pressure of raising petroleum

prices and environmental concerns, automobile manufacturers constantly strive to

produce more fuel efficient and environmentally friendly vehicles. The primary objective

of manufacturing automobiles subject to such constraints is to ensure consumer mobility.

Given the current resources and technologies, the most feasible solution is hybrid electric

vehicles. In order to accelerate the research involved with creating these vehicles, the

General Motors Corporation and The Department of Energy have created the Challenge

X Competition. This is a three year long competition that requires 17 universities acrossthe United States to design, develop, and build a hybrid-electric Chevrolet Equinox sport-

utility vehicle. The goal of this competition is to secure consumer sustainable mobility.

The first, and current, year involves the preliminary design of the vehicle,

component selection, control strategy development, and vehicle modeling and simulation.

General Motors and The Department of Energy have created a list of goals each team is

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to achieve with the development of their hybrid electric vehicle. These Vehicle

Technical Specifications can be seen in Table 1. The idea behind such specifications is

to maintain stock Chevrolet Equinox performance and capacity while decreasing fuel

consumption and emissions. Throughout the first year each team must develop and

simulate a hybrid electric vehicle architecture that meets, or surpasses, these goals.

Table 1. Summary of Challenge X Vehicle Technical Specifications [9, 5].

DESCRIPTIONCOMPETITION

GOALOHIO STATE

VTS

IVM60MPH


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1.2 Vehicle Architecture

The most critical task to achieve the goals listed in Table 1 is selecting and

developing an effective HEV architecture. Performance of the vehicle automatically

increases with a hybrid electric configuration due to increased fuel savings, reduced

energy losses with regenerative braking, and emissions control. Hybrid electric vehicle

architectures are organized into three classes: parallel, series and power-split hybrids.

1.2.1 Classifications of HEVs

The parallel configuration allows for the electric machine(s) and the internal

combustion engine to provide mechanical power to the driveline. Parallel hybrids can be

further classified in two ways: electrical assist parallel hybrids and ICE assist parallel

hybrids. In the electrical assist architecture, the ICE operates only within the optimal

region to reduce emissions and fuel consumption. The EM assists whenever necessary in

order to uphold this constraint. The ICE assist architecture involves the ICE only being

turned on for hard accelerations, hill climbing, and high speeds. This also reduces

emissions and fuel consumption as the EM acts as the primary actuator for vehicle

motion [1].

The other configuration is a series hybrid. In a series HEV, the vehicle is

propelled solely by the EM. The EM obtains the required energy from either a battery, ora motor-generator set. The MG set supplies power to the EM and an energy storage

device (i.e. a battery or super capacitor). The purpose of the energy storage device is to

allow more power to be drawn by the EM during more demanding driving conditions

(hill climbing, hard accelerations, etc.) [6].

Both configurations have their own distinct advantages. The parallel configuration

allows for the ICE and EM to be reduced in size since their propelling power can be

summed to meet driver demand. Moreover, efficiency increases with this configuration

since fewer energy conversions take place. However, a parallel configuration is far more

complex than the series architecture. The series architecture allows for easier packaging

since the EM and MG set do not need to be close together. The control problem for this

type of HEV is also far less complex [1, 6]. Moreover, the series configuration allows for

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direct battery charging and electric power supply which is essential for effective hybrid

travel.

There are two additional types of HEVs when not speaking of the vehicle

architecture: charge-sustaining and grid dependent. If the HEV can sustain the charge of

the battery in every driving condition without the assistance of an outside electrical

power grid, the HEV is considered charge-sustaining. If the vehicle needs to be integrated

with an outside electrical power grid from time-to-time to recharge the battery, the

vehicle is referred to as grid-dependent [6]. Charge-sustaining HEVs, although more

complex, are more appealing to the consumer. This is due to the longer range of the HEV

over a conventional vehicle without sacrificing performance [6]. Ohio State has chosen to

develop a charge-sustaining HEV.

In order to obtain the benefits from both HEV architecture configurations, the

Ohio State Challenge X team has chosen to develop the third class of HEV: power-split.

[5]. This configuration combines both parallel and series architectures. To accomplish

this, the powertrain is divided into two independent sections. The first is the conventional

powertrain. This consists of an ICE coupled with an integrated starter/alternator (ISA)

that powers the front axle of the Equinox. An electric powertrain drives the rear wheels of

the HEV. To do this, an electric machine, with a gearbox appropriately sized for vehicle

propulsion, is used [7]. This vehicle architecture is represented in Figure 1.

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(ISA)

Integrated Starter

Al ternator

Traction Motor

& Gearbox

(EM)

(ISA)

Integrated Starter

Al ternator

Traction Motor

& Gearbox

(EM)

(ISA)

Integrated Starter

Al ternator

(ISA)

Integrated Starter

Al ternator

Traction Motor

& Gearbox

(EM)

(ICE)

(AT)

Front Back

Figure 1. Ohio State Challenge X Vehicle Architecture [5].

1.2.2 Vehicle Components

With the architecture defined, the individual components that make up the vehicleare chosen. The main actuator of the conventional powertrain is a diesel engine.

Specifically, a Fiat 1.9L diesel engine is used. This is one of the engines provided by the

Challenge X organizers as being acceptable for use during the competition. Diesel

engines are more efficient than gasoline powered internal combustion engines due to

higher compression ratios and lack of engine pumping loses. To reduce emissions,

Biodiesel B20 is the selected fuel [7].

As shown in Figure 1, an ISA rigidly connects to the diesel engine via the

crankshaft. The ISA is simply an electric machine that is capable of being used to start

the diesel engine, or be used by the diesel engine as an alternator to provide electrical

energy. This is one of the components that make this HEV charge-sustaining. The ISA

can also supply a minimal amount of torque to the front wheels for propulsion assistance

[5].

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The EM, referred to as the Traction Motor the Figure 1, is the electrical

propulsion unit used to the power the EV1 electric car by General Motors. This 103 kW

electric motor and gearbox is now used to drive the rear wheels of the Equinox HEV. Not

only can this unit provide power to the wheels, but it can also absorb power from the

wheels and be used as a generator. This process occurs when the driver requests stopping

torque. At this point, the EM absorbs torque from the rear axle slowing the vehicle, and

will use this torque to generate energy for the replenishment of the battery [5]. This is

another feature that classifies this HEV as charge-sustaining.

Initially, the Ohio State Challenge X team agreed to use an automated manual

transmission [7]. However, these types of transmissions are hard to acquire, so the

automatic transmission that accompanies the Fiat diesel engine is used. Automatic

transmissions are not as efficient as automated manual or manual transmissions, but they

are the most acceptable to the consumer [5].

Only one nickel-metal hydride battery pack powers both electric machines and the

vehicle accessories. As shown above, there are two separate inverters for the EM and the

ISA, as well as, a DC-to-DC converter to power 12 volt vehicle accessories (interior

lamps, radio, head lights, etc.). A switch box routes high-voltage flow [5]. One of the

main reasons for selecting a charge-sustaining HEV configuration involves current

battery technology. The market is filled with low efficiency, low energy storage capacity,

and long charge time solutions [1]. As previously stated, to cater to the desires of the

consumer, a charge-sustaining strategy eliminates these undesirable issues.

1.2.3 Modes of Operation

With the architecture depicted in Figure 1, various operating modes for the

vehicle can be achieved. These operating modes have been summarized in Table 2.

During a typical driving mission, the HEV operates in both hybrid, and conventional

modes [1]. This can be seen in the table below.

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Table 2. Vehicle Operating Modes [8].

MODE ICE ISA EM TRAN.

Idle Off Off Off Neutral

ICE,EM, AND ISA ARE SHUTOFF.ELECTRICAL ACCESSORIES.

ELECTRIC LAUNCH OFF MOT. OFF NEUTRAL

VEHICLE STARTED FROM REST WITHEM.

ENGINE START START MOT. MOT. NEUTRAL

AT A CERTAIN VEHICLE SPEED,ICE QUICKLY STARTED BY ISA.

NORMAL ONMOT. OR

GEN.

MOT. OR

GEN.DRIVE

TORQUE REQUESTS DETERMINED BY PRIMARY CONTROL STRATEGY .

DECELERATION ON OROFF

GEN. GEN. DRIVE ORNEUTRAL

REGENERATIVE BRAKING BY EM AND ISA AS BATTERY ALLOWS.

4WD ONGEN. OR

OFFMOT. DRIVE

EM RECEIVES CONTINUOUS POWER THROUGH DC BUS FROM ISA.

With each of these, the fuel efficiency increases and the emissions decrease immensely.

During the idle mode, and decelerating cases the ICE is be turned off, unless recharging

of the battery requires this to drive the ISA to provide the necessary power (series HEV).

The fuel efficiency can increase by as much as 10% simply by eliminating fuel flow to

the ICE during braking and idling situations [5]. This concept accompanies the general

rule that the EM should be used during launch and immediate power request situations

[6]. This is because electric actuators can deliver high torque at low speeds while

emitting no environmentally harmful by-products. This general rule is satisfied during

Electric Launch mode when the EM motors (MOT.) the vehicle.

After a set speed, the ICE turns on during the Engine Start mode. Once the ICE is

up to speed, the automatic transmission engages and the ICE becomes the primary

actuator for vehicle propulsion. At this point, the vehicle enters the Normal mode.

Between the Electric Launch and Normal mode, the HEV satisfies the constraints of

being a parallel HEV as previously defined. Note that during Normal mode, the EM can

be used to supply regenerative power to the battery; moreover, the EM can draw power

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from the battery and assist the ICE with motoring the vehicle during four-wheel drive

situations. The ISA shares similar options during Normal mode. Basically, the 4WD

mode is merely a derivative of the Normal mode with the EM motoring and the ISA

generating the electrical power needed (a series/parallel hybrid combination).

The vehicle enters Deceleration mode when the driver uses the brakes to slow the

vehicle. Here the concept of Regenerative braking is implemented. Regenerative

braking involves the process of using the resistance between the field and armature of the

EM to generate power to replenish the battery. As the driver applies the brake, for a set

distance of pedal travel, the mechanical braking system does not activate and the EM

absorbs torque off of the rear axle. This mechanical energy is converted to electrical

energy and sent to the battery [6].

The power-split HEV solution with a charge-sustaining focus has been selected

by the Ohio State Challenge X team. This particular configuration leads to alternate

modes of operation all of which increase the efficiency of the vehicle. With the

architecture, and components thereof, defined, modeling of the HEV can begin.

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Chapter 2

Modeling

With the vehicle architecture and components selected modeling of the Challenge

X HEV is performed. The entire driveline is first visually represented by a figure which

leads to the development of the mathematical expressions governing the behavior of the

HEV. These expressions are then configured into Simulink block diagrams for further

analysis and simulation.

2.1 Model of the Driveline

The dynamic model of the driveline is displayed in Figure 2. Refer to Table 3 of

the Appendix for a list of the nomenclature. Only the necessary inertias are included in

the model. The inertias of the smaller components (the axles, brake assemblies, and

wheels) do not have a drastic effect on the dynamics of the system and can be ignored for

simplicity. Unnecessary damping and spring effects such as those intrinsic to the

automatic transmission and rear gearbox are also eliminated to further simplify the

model. Disregarding these dynamic effects does not alter the accuracy of the model since

they are insignificant in comparison to other driveline components (i.e. ICE, EM, and

ISA). The equations that follow are developed by the author, as well as separately

developed by others and represented in the referenced publications [2, 5, 7, 8].

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Figure 2. Dynamic Model of the Driveline [8].

2.1.1 Dynamic Equations of the Front Driveline

Due to the fact that the ISA is rigidly connected to the ICE, they can be

considered as one lumped inertia (JICE+JISA) accelerating at the same rate as the

ICE( . The torque converter divides the front driveline into two parts. The first

includes the dynamic behavior of every component from the ICE and ISA to the pump

side of the torque converter. This relationship is as follows:

)ICE&

_( )

ICE ISA ICE ICE ISA ICE ICE TC PJ J T T b T + = + & (1)

Here the inertias of the ICE and ISA are combined and have the same rotational

speed ICE( ) . This inertial force must be equivalent to the torque of the ICE (TICE) and

ISA (TISA), as well as the damping effect of the ICE (bICEICE), and torque of the pump

side of the torque converter (TTC_P). When the torque converter is not locked, the

dynamic behavior of each component from the turbine side of the torque converter to the

wheels can be modeled as:

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_

( )

TC T VEH VEH TR TR F TR F TR

TR F F

T xJ k b

g r

=

&

v

r

(2)

The rotational inertia of the transmission accelerates ( TR TRJ & ) as a result of the excitation

imposed by the following quantities. Torque from the transmission depends on the gear

ratio (TR (g)), which in turn depends on the current gear (g). Each gear results in a

different torque on the turbine side of the torque converter (TTC_T). Coming off of the

transmission are the spring and damping ( dynamic effects introduced by the

front axles (half shafts). These quantities are functions the transmission rotational speed

(

( )Fk )Fb

TR) and angular position (TR). Vehicle speed (vVEH) and vehicle position (xVEH) are

manipulated by the wheel radius (rF) to be used here as well. Note the dynamic effects of

the front brakes have been ignored, and are only considered as a torque ( TB_F). This

torque is zero until the driver begins to demand stopping power. When this occurs,

equation (2) becomes:

_

_( )

TC T VEH VEH TR TR F TR F TR B F

R F F

T x vJ k b

g r r

=

& T (3)

The above equations are derived assuming that the torque converter is not locked.When the torque converter is locked, the fluid coupler becomes a rigid link connecting

the ICE and ISA inertia directly to the inertia of the automatic transmission through the

gears. This also means that the torque the pump side and turbine side are equal (TTC), or:

_ _TC P TC T TC T T T= = (4)\

However, the specific dynamic equations for the driveline when the torque converter is

locked have yet not been developed. Currently, the Ohio State Challenge X Team is not

concerned with such a model. This concludes the development of the dynamic equations

for the conventional (front) driveline.

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2.1.2 Dynamic Equations of Rear Driveline

The rear driveline is not nearly as complex as the front as there is no torque

converter. The rotational inertia of the EM is excited ( EM EMJ & ) by the torque of the EM

(TEM) and the intrinsic damping of the EM (bEM). The spring (kR) and damping (bR)

characteristics of the axle add to the dynamic response of this component as well. Here,

the angular speed ( )EM and angular position ( )EM of the EM are manipulated by the

ratio of the gearbox ( )GB and this interacts with the previously defined vehicle

parameters to yield the appropriate dynamic response from the rear axle. This portion of

the vehicle is modeled as follows:

VEH VEH EM EM EM EM EM GB R EM GB R EM GB

R R

x vJ T b k br r

= +

& (4)

Again, the dynamics of the rear brakes are disregarded and their effect is represented by a

torque (TB_R). This torque is zero until the driver commands otherwise. If the brakes are

being applied, then (4) becomes:

_

VEH VEH

EM EM EM EM EM GB R EM GB R EM GB B RR R

x v

J T b k b Tr r

= + &

(5)

2.1.3 Dynamic Equation of the Vehicle

The final expressions to derive are those of the vehicle. This component will be

affected by not only the tractive force (F) input from the powertrain, but also the

opposing forces due to aerodynamic drag (FDRAG), rolling resistance (FRR), and forces

from the road grade (FRD). These equations can be seen below.

VEH VEH RR DRAG RDm v F F F F = + + & (6)

cos( )RR VEH rF m gC = (7)

21

2DRAG AIR d f VEH

F C A= v (8)

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sin( )RD VEHF m g = (9)

The rolling resistance is the product of the vehicle mass (mVEH), the gravitational

acceleration due to gravity (g), the rolling resistance coefficient (Cr) and the cosine of the

grade angle of the road measured from the horizontal plane (). Air drag on the vehicle is

represented by the standard drag equation that includes the density of air (AIR), the drag

coefficient of the vehicle (Cd), the frontal area of the vehicle (Af) and the vehicle velocity

(vVEH). Finally, the force resulting from the grade of the road is merely a product of the

mass of the vehicle, the acceleration due to gravity, and the sine of the grade angle [6].

With all of the dynamic equations derived for the entire driveline, models can be created

in Simulinkfor further analysis and simulation.

2.2 DynamicSimulinkModel of the Front Driveline

One of the most convent ways to analyze dynamic equations such as those

previously derived is with Simulinkmodeling software. Each of the above equations are

represented by a block diagram. Each individual diagram is connected to create the entire

system. Then an input can be introduced and the response of this system can be analyzed

in great detail. When this Simulink model was initially developed, the Ohio StateChallenge X team was considering using a manual transmission; therefore, the model

presented below is slightly different than the aforementioned vehicle model. Also, the

variables seen in these diagrams are different than those seen in Figure 2. This is done for

programming convenience. Finally, the version of the front driveline developed here is

not used for the HEV vehicle simulator discussed later; however, this model, after slight

alterations, is used by another for initial testing of the control strategy that is discussed in

a subsequent chapter [2].

In general, torque, or speed in some cases, flows forward through the following

model component by component. The speed, or torque in some cases, and position are

then fed back when necessary. This model operates on an initial torque request from the

driver. This request acts as an input to each actuator. The particular actuator then

converts this input to an output torque and a feed back speed. The output torque then

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serves as the input of the next component in the driveline. This process continues until

torque is delivered to the wheels, and vehicle speed is fed back to the driver. The driver

is ultimately represented by a Simulinksubsystem that can be seen in Figure 15.

2.2.1 ICE Model

The Simulinkblock diagram of the ICE can be seen in Figure 3 along with the

dynamic equation that governs this model. Note that the inertia of both the ICE and ISA

are lumped together into one variable ( F ICE EJ J JM= + ) as seen in the diagram. The first

two inputs represent the torque command for the ICE (TICE) and ISA (TISA). The third

input is the opposing torque coming from the clutch of the manual transmission (Tc). This

torque is subtracted from the other input torques. The output from the inertia gain is

integrated twice: once for speed ( ICE ), and the other for position ( )ICE . The value of the

ICE speed is sent to the clutch, but the value of ICE position is used only for observation

purposes. The dynamic equation is as follows:

(10)( )ICE ISA ICE ICE ISA ICE ICE CJ J T T b T + = + &

Figure 3. Simulink Diagram of ICE & ISA.

2.2.2 Clutch Model

The speed and position of the ICE and ISA enter the clutch. The clutch is fairly

simple and operates under the assumption that no slip occurs. This is a valid assumption

for modeling purposes as predicting and modeling the magnitude of any slip presents a

rather complex problem. However, provisions have been made in the model for the

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damping ( ) and stiffness ( intrinsic to the clutch and are represented in the

respective gain blocks of Figure 4. The input, , is a command sent to the clutch by the

driver that either engages, or disengages, the clutch. This value can either be 0 to

represent the clutch being disengaged, or 1 to represent the clutch being engaged. If the

driver is shifting and has the clutch disengaged, the product will be zero, and no torque

passes through the clutch. When the driver has the clutch engaged, the speeds will be

multiplied by 1 with the product block and the torque flows through this device and onto

the gearbox. The clutch speed

cb )ck

( )c is an input here and is sent from the gearbox as a

feedback. The governing equation of the clutch is:

( ) ( )c C ICE C C ICE C

T b k = +

(11)

Figure 4. Simulink Diagram of the Clutch.

2.2.3 Front Gearbox Model

The torque from the clutch enters the front gearbox. This block diagram can

be seen in Figure 5. Here the torque and speed are manipulated according to the gear and

the respective gear ratio. The only inputs are the torque from the clutch, the gear

command, the feed back speed

( )cT

( )T and position ( )T of the transmission. These inputs

will be manipulated to result in an output torque , speed@( C TT ) ( )c , and position( )c .

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The ratios for each gear are contained within the look-up table and provide the

appropriate constants for accurate speed reduction and torque increase. To ensure ideal

operation, a switch allows torque to pass through the clutch if the clutch command is 1

(engaged). Otherwise, the switch will force the output torque to be zero. Torque, speed,

and position are calculated as follows:

( )@C

C T

TR

TT

g= (12)

( )C TR g T = (13)

( )C TR g T = (14)

Figure 5. Simulink Diagram of the Front Gearbox.

2.2.4 TransmissionThe transmission resembles that of the ICE and is shown in Figure 6. The inertia

and damping affect the input torque from the gearbox. The resistive torque of

the front axle is subtracted from the torque input . This result is integrated for

speed

( )T

J ( )T

b

@( C TT )

( )T and position ( )T respectively. These quantities are fed back to the gearbox

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and sent on to the front axle. The dynamic equation for this process is represented in (15)

below.

@ 1T T C T X f T J T T b = + & (15)

Figure 6. Simulink Diagram of the Automatic Transmission.

2.2.5 Front Axle

The front axle is assumed to be a mass-less component that only contributes a

torsional damping and stiffness to the system. As seen in Figure 7, the axle is

a very simple model with input speeds and positions from both the transmission and the

wheels(

1( Xb ) )1( Xk

)F . The output is merely the torque experienced at the axle . This is

somewhat backwards from the previous components, but effective for modeling this

particular device. The individual expression representing the axle is shown in (16) below.

1( XT )

( ) ( )1 1 1X X f t X f tT b k = + (16)

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Figure 7. Simulink Diagram of the Front Axle.

2.2.6 Front Brakes

The front brakes simply apply a torque to the powertrain that counter-acts the

motion of the vehicle when the driver demands to do so. As previously mentioned, beta

(), the input command from the driver, ranges from 0 to 1 corresponding to the position

of the brake pedal. This value is 0 at rest and 1 when the pedal is completely depressed.

The other input is rotational speed of the front wheels ( )F . Using these values along

with the damping coefficient of the brakes , results in an output torque from the

front brakes . This torque is sent to the wheels. Figure 8 is the Simulinkmodel while(17) is the mathematical representation of this figure.

( )BFb

( )BFT

BF BF FT b = (17)

Figure 8. Simulink Diagram of the Front Brakes.

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2.2.7 Front Wheels

The model of the wheels is displayed in Figure 9. The front wheels convert the

torque from the axle and potential torque from the brakes into a tractive force ( )fF that is

sent to the vehicle. The vehicle speed and position( )Fv ( )Fx are used to provide an

output rotational speed and position for feed back to the brakes and axle. The effect of the

front differential ratio is considered along with the wheel radius ((diff) )FR . Equations

18, 19 and 20 represent this process mathematically.

( 11 1

F

F

F Tdiff R

=

)X BFT (18)

F

F

diffF

R = (19)

F

F

diffv

R = F (20)

Figure 9. Simulink Model of the Front Wheels and Differential.

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2.3 DynamicSimulinkModel of the Rear Driveline

The development of an acceptable model for the rear driveline of the chosen

vehicle architecture is the one of the main thrusts of this thesis. Each model seen below

was developed specifically for the simulators discussed in a Chapter 3. Because of this,

the nomenclature seen in the figures to follow is significantly different than that of the

models previously discussed. The nomenclature of the models developed for the rear

driveline matches that of the early versions of cX-SIM and cX-DYN (See Chapter 3).

The nomenclature also differs from that of the previously seen dynamic equations for

convenience during programming. Each component is modeled as dynamic, but only the

more dominant dynamic characteristics are considered. Hence, the inertia of the axle is

once again ignored due to the smaller magnitude of this quantity when compared to that

of the EM.

2.3.1 Electric Motor

The electric motor, seen in Figure 10, is modeled identically to the ICE. There is

an input torque command from the driver that is affected by a gain equal to that

of the inertia of the EM . This value is then integrated twice for position

( )EMT

( )EMJ ( )EM and

speed ( )EM . The intrinsic damping of the EM is included. The output of the EM

diagram is rotational speed

( )EMb

( )EM and position ( )EM . The feed back to the EM is the

torque from the rear gearbox . The green box is another method of signal routing in

Simulinkthat is utilized to simplify the appearance of the system model seen in Figure 15.

The dynamic equation for this component is (21).

( )RGBT

EM EM EM RGB EM EMJ T T b = & (21)

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Figure 10. Simulink Diagram of the Traction Motor.

2.3.2 Rear Gearbox

The speed of the EM enters the gearbox and is reduced according to the gear ratio

( GB ) as seen in Figure 11. From the data given to Ohio State by General Motors, the

reduction ratio was found to be 10.946. The third input to this block is the torque from

the rear axle . After applying the ratio to these inputs, gearbox rotational speed( )RGBT

( RGB ) and position ( )RGB , as well as torque, become outputs. This process is modeled

with the following equations. Note the rear gearbox is far less complex than the front

counterpart. This is because the gearbox attached to the EV1 motor has only one

reduction and no driver selectable gears to be considered. The EM is either motoring,

regenerating, or off depending on commands received by this device.

RGB GB RAxleT T= (22)

EMRGB

GB

= (23)

EMRGB

GB

= (24)

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Figure 11. Simulink Block Diagram of the Rear Gearbox.

2.3.3 Rear Axle

The rear axle is an exact replica of the front axle as seen in Figure 12. Assumed to

be mass- less, the axle only contributes a damping ( and stiffness to the

dynamics of the rear driveline. Inputs to the rear axle match that of the outputs of the gear

box. By implementing the appropriate damping and stiffness values, the rear gearbox

speed and position are converted to an output torque .This torque is sent to the

rear brakes and fed back to the rear gearbox as previously stated. The values of stiffness

and damping have yet to be determined. Expression (25) mathematically represents the

process seen in Figure 12.

)RAxleb ( )RAxlek

( )RAxleT

( ) ( )RAxle RAxle RGB RBrake RAxle RGB RBrakesT b k = + (25)

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Figure 12. Simulink Diagram of the Rear Axle.

2.3.4 Rear Brakes

The rear brakes behave exactly as the front brakes. They are considered to have

no mass, and only impose a torque that will resist the torque driving the vehicle.

There are differences in this model when compared to the front counterpart as seen in

Figure 13. For modeling purposes, the actual damping coefficient of the brakes is a very

difficult number to estimate. Keeping this in mind, the brake torque is determined by

multiplying the brake command from the driver by a proportional gain (

( )RBrakeT

RBrakes). This

result is subtracted from the incoming torque to the brakes. This effectively reduces the

torque flowing between the rear axle and the wheels. The value of the gain is simply the

percentage of the total braking power that is provided by the rear brakes. In this case the

value equals 40%, or 0.4. The incoming brake command, determined by the control

strategy, accurately provides a value that is manipulated by this gain. The simple

expression representing this process is:

RBrakes RBrakes RAxleT T= (26)

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Figure 13. Simulink Diagram of the Rear Brakes.

2.3.5 Rear Wheels

The model of the rear wheels provides semblance to that of the model of the front

wheels. However, no differential is included here since this does not exist on the

Chevrolet Equinox. The only reduction of the rear driveline occurs at the gearbox

attached to the EM. This model uses the inputs of brake torque( , vehicle

speed( , and vehicle position(

)RBrakeT

)VEH

v )VEH

, and creates the three outputs using the wheel

radius as seen in Figure 14. The outputs include wheel rotational speed (( )RWheelr )RWheel

and position ( )RWheel , as well as, the tractive force to be given to the vehicle.

The wheel radius is determined from the specifications of the Equinox released by

General Motors for this competition. The dynamics of the wheels are relatively small

compared to the other dynamic phenomena present in the rear driveline. Thus, the

stiffness and damping of the wheels are not considered in the following model. The

effects of the wheel are mathematically represented in the equations below.

( )RWheelF

RBrakesRWheel

RWheel

TF

r= (27)

RWheel RWheel VEHr x = (28)

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RWheel RWheel VEHr v = (29)

Figure 14. Simulink Diagram of the Rear Wheels.

The individual components of the driveline have been represented both

mathematically and by Simulinkdiagrams. Each of these can now be connected together

to create a model of the entire vehicle powertrain. Completing this task results in the

creation of two HEV simulators that will be extensively used to evaluate the performance

of the hybrid Equinox.

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Chapter 3

Simulation Results

With all of the modeling complete, two comprehensive HEV simulators are

created. One is a quasi-static simulator that is effective for monitoring energy

consumption. The other is a dynamic simulator for the evaluation of the drivability of the

HEV. Both work together in conjunction with a graphics program that is developed here.

These three items give the Ohio State Challenge X team extensive capability to study of

all aspects of HEV operation.

3.1 cX-SIM

The first simulator is called cX-SIM and the top layer of this Simulinkmodel can

be seen in Figure 15. This is a quasi-static HEV simulator, meaning the dominant

dynamics of the system are solely the vehicle. The vehicle reacts much slower than every

other component in the system thus the time constant of the vehicle is quite large. This

fact allows for the time constants of all other components to be assumed zero [6].

However, there is one caveat; the inertia of the ICE is included to ensure proper operation

of the dynamic model of the torque converter. With only these two dynamic

characteristics included, this simulator is computationally inexpensive and the results are

acquired in a rather timely fashion. This is ideal for monitoring fuel consumption and

energy usage. Since the models of the previous chapter result in a convoluted system

when integrated together, they are each converted into a subsystem. This collects of the

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various parts of the aforementioned models into one block. These are the subsystems seen

in cX-SIM.

Figure 15. cX-SIM Top Layer.

3.1.1 Driver

As shown in Figure 15, cX-SIM is divided into three main parts. The first

represents the Driver. Contained within this subsystem are all of the components that

mimic the behavior of an actual vehicle operator. There is an input velocity profile that

can be set to match that of the Federal Urban Driving Cycle (FUDS) or Federal Highway

Driving Cycle (FHDS). This input enters a PID controller, which receives a feedback

signal of the actual velocity from the vehicle. The controller minimizes the discrepancy

between the desired and actual vehicle velocity. Not only is the difference in velocity

rectified, but also two signals are generated and sent from the driver subsystem. The first

signal, alpha (), represents the accelerator pedal position. The second represents the

brake pedal position, beta (). These commands are then sent to the HEV Powertrain

subsystem.

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3.1.2 HEV Powertrain

The diagram of the HEV Powertrain subsystem is shown in Figure 16. Here, the

models seen in the previous chapter are integrated together to effectively represent the

driveline of the Ohio State Challenge X Equinox. The driver commands, and , enter

the controller bock. Within this subsystem lies the control strategy. The inputs from the

driver are manipulated to create torque requests for each of the actuators, as well as, state

commands, the gear command, and the brake command. The torque request enters the

appropriate component and the conversion of torque and speed according to the

characteristic of the device begins. Note that the axles of the driveline have been removed

because these dynamics are being ignored for this particular simulator. Moreover, the

inertias of the EM and the AT have also been ignored for the reasons previously

described. A look-up table containing the appropriate data is included in place of the

inertia. The result from this subsystem, as mentioned in Chapter 2, is a tractive force from

both the front and rear driveline, which are summed together and sent on to the vehicle.

Figure 16. cX-SIM Powertrain Subsystem.

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3.1.3 Vehicle

The final subsystem in cX-SIM is the Vehicle. The contents of this subsystem are

merely the Simulink block representations of equations 6-9. Forces due to air drag,

vehicle rolling resistance, and road grade are subtracted from the total tractive force given

to the vehicle from the powertrain. Also, the inertia of the vehicle is included to give cX-

SIM the quasi-static classification previously defined. Once all of the force effects on the

vehicle have been calculated, and output vehicle speed is sent from this subsystem back

to driver. This entire process then repeats throughout the predetermined driving cycle.

3.2 cX-DYN

A quasi-static simulator cannot effectively analyze vehicle dynamics, particularly

drivability. So a second HEV simulator is created to accomplish this. The second

simulator is called cX-DYN and is used primarily to analyze the drivability of the

vehicle. Drivability refers to the vibrations felt by the operator. In order to maintain

consumer favorability and fulfill one of the competition goals, this detail must be

precisely controlled. cX-DYN allows for this type of control, as well as the ability to

monitor all dynamic responses of the vehicle.

The construction of cX-DYN resembles that of cX-SIM. As can be seen in Figure

17, there are three main parts: Driver, HEV Powertrain, and Vehicle. The functions of thedriver and vehicle subsystems are exactly the same as those in the quasi-static

counterpart. However, the HEV Powertrain includes additional components to make the

simulator more dynamic. Mainly the dynamics of the axles and inertias of the EM and

AT are included. Figure 18 displays the HEV Powertrain of cX-DYN with these

additional components included.

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Figure 17. cX-DYN Top Layer.

Figure 18. cX-DY N Powertrain Subsystem.

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One main disadvantage to cX-DYN is that the included vehicle dynamics make

this simulator computationally more expensive. Therefore, this simulator must be run in

Simulinks Accelerator mode in order to obtain results in a more time efficient manner.

Both cX-SIM and cX-DYN have their advantages and disadvantages, but they are used

together to analyze all aspects of HEV operation.

3.3 cX Graphics

In order to obtain visual knowledge of the functions of the Challenge X Equinox

after running one of the aforementioned simulators, a graphical user interface program is

developed. The interface of this program is shown in Figure 19. Appropriately named cX

Graphics, this program can monitor every quantity of the HEV and visually represent

them. Moreover, the user has the option of selecting which exactly quantities to view.

This is done by dividing the data into the Set layout for plots categories as seen in

Figure 19: Driver, Vehicle, Acceleration, HEV Operation, Conventional Powertrain, and

Electric Powertrain. Statistical information of some of the quantities within these

categories can be viewed as well. A key feature of cX Graphics is that the user can select

to view their particular quantity is English, or SI units. This is done to increase the

versatility of this program; as well as, to enhance the Challenge X simulation analysis

experience by giving the users of cX-SIM, or cX-DYN, a detailed visual link between thesimulated operation of the HEV and the occurrences therein.

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Figure 19. cX Graphics Top Layer.

3.3.1 Set Layout for Plots Driver

The plot options for the Driver are seen in Figure 20. Here, the accelerator and

brake pedal positions can be viewed over the entire drive cycle. Also, the commands

from the PID controller within the driver block can be monitored. The pedal positionsreflect how an actual driver behaves in order to match the velocity profile of the set

driving cycle being simulated. The output of the PID controller will be continuously

adjusted to in order to meet the desired velocity command.

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Figure 20. Driver Plot Options Screen.

3.3.2 Set Layout for Plots Vehicle

Plot options for the vehicle are displayed in Figure 21. The actual velocity of the

vehicle can be generated, as well as the desired vehicle velocity according to the driver.

Both of the curves can be graphed together to analyze the error. The user also has the

option of setting a customized range for both the horizontal and vertical axis. The default

values are maximums and minimums of the particular simulation. Not only can the speed

be displayed, but also the desired and actual power at the wheels can be visualized.

Statistical data such as the rms, maximum and minimum deviations for the velocities and

powers can be also displayed.

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Figure 21. Vehicle Plot Options Screen.3.3.3 Set Layouts for Plots Acceleration Test

Prior to running the simulator, the user has the option of selecting an Acceleration

Test. After the simulation is complete, the user can then open the appropriate plot options

screen in cX Graphics (Figure 22) and define the parameters of this test. Once the cX

Graphics program is executed, the results appear in both a figure and table. The figure

shows the actual velocity and the table will contain the statistical information previously

aforementioned. The units for this can also be interchanged between SI and English.

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Figure 22. Acceleration Test Parameter Selection Screen.

3.3.4 Set Layout for Plots HEV Operation

Since the main output signal of the control strategy is the torque request for each

driveline component, the most important aspect of analyzing this HEV is monitoring

these requests and device output. Thus, every torque request and output torque for each

actuator in the driveline can be visualized with cX Graphics. Figure 23 shows the plot

options for HEV operation. Deviations of the actual torque and torque request, as well as,

the shifting sched ontrol strategy is

plemented, the options to see the potential for energy recuperation and Sankey

ake cX Graphics a vital tool for

ule can also be displayed if desired. Once the c

im

Diagrams will be fully developed. These options m

energy management analysis. Such an analysis leads to minimal fuel consumption,

fulfilling one of the primary goals of Challenge X.

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Figure 23. HEV Operation Plot Options Screen.

3.3.5 Set Layout Plots for Conventional Operation

The u conventional

owertrain with the options seen in Figure 24. These options include only those variables

uest and actual torque delivered by the

vering positive torque, hence why only

ser has the option of only viewing the results from the

p

that are related to the ICE. Again, the torque req

ICE can be examined. To enhance this comparison, statistical data between these two

values can also be shown. Not only can the speed and torque be viewed independently,

but they can also be displayed on the efficiency and fuel consumption map for the Fiat

1.9L diesel engine. Features like this, and others shown below, give cX Graphics the

capability to extensively audit the functions of the conventional powertrain. Unlike the

electric actuators, the ICE is only capable of deli

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the first quadrant of the torque, speed, and efficiency/fuel consumption map is shown

when this option is selected (see Figure 30).

Figure 24. Conventional Powertrain Plot Options Screen.

3.3.6 Set Layout Plots for Electric Powertrain

Not only can the conventional powertrain be independently viewed, but the

electric powertrain can be as well. Figure 25 displays the plot options screen for this task.

All aspects of the batteryvoltage, current, and state-of-chargecan be analyzed.

However, all of the limits for these quantities are still being investigated by other

members of the Ohio State Challenge X team. These limits will be properly implementedhere once conclusive values have been obtained.

The next component that can be monitored is the EM. Speed and torque can be

viewed independently, as well as on the efficiency map for the EV1 motor. Of course, the

statistical deviations between actual and desired torque can also be shown. Displaying the

power used by the EM is an option as well. It is important to note that the speed listed

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here is that of only the EM, the angular speed seen at the half shafts is this value divided

by the gearbox ratio of 10.946. As previously stated, the EM is capable motoring the

vehicle and absorbing kinetic energy from the vehicle to replenish the battery. Thus, both

positive and negative torque, as well as power, is seen here throughout any given driving

cycle (see Figure 31).

The final set of options includes presenting the quantities produced by the ISA.

However, the efficiency map of the ISA does not currently exist since the Ohio State

Challenge X team has not yet received the actual device. The operating data of this

component is a modified version of a similar ISA that has been analyzed by others. Once

the actual ISA is been received and examined, the appropriate changes will be made in

cX-SIM and cX-DYN, as well as cX Graphics, to reflect this. Nonetheless, the delivered

torque and torque request can be viewed along with the statistics that quantify their

differences. The efficiency and power of this component are also display options. Keep in

mind that this component, just like the EM, can deliver torque (positive power) and

absorb torque (negative power) to recharge the battery. Therefore, both positive and

negative torque and power values will appear here.

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Figure 25. Electric Powertrain Plot Options Screen.

The quasi-static cX-SIM is developed for the task of monitoring energy and fuel

consumption while the dynamic cX-DYN is used to the study drivability of the HEV. An

in-depth visual analysis is performed by cX Graphics once the simulators produce results.

Now that the vehicle can be actively simulated and analyzed, preliminary results are

obtained and verified to ensure the simulators are accurate to the designed vehicle.

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Chapter 4

Results & Model Verification

As discussed in the previous chapter, both a quasi-static and dynamic simulator

are being developed. At the present time, only cX-SIM is functional and only a few of the

mod ry.

dditional work on these simulators will continue until both are fully capable of

roviding the Ohio State Challenge X team with the appropriate analysis. Techniques to

verify of these simulators are developed and also presented.

4.1 cX-SIM

es of operation are available. Therefore, the results presented here are prelimina

A

p

Only the Idle, Launch, Engine Start and Deceleration modes of operation are

functional when the preliminary results from cX-SIM are obtained. cX Graphics is then

used to create the figures seen below. Using a Federal Urban Driving Cycle (FUDS), a

sim 6

ows the actual and desired velocity curves. In a perfect system, the two curves would

ulation was performed for approximately the first 3 minutes of the cycle. Figure 2

sh

lay precisely on top one another; however, since this system design mimics the behavior

of an actual driver, the discrepancy seen in Figure 26 is very realistic. Nonetheless, the

deviation between the desired and actual velocity is not that outlandish. Figure 27 shows

the screen shot of the statistical data created by cX Graphics. The rms deviation is only

~2.0 kph-- an acceptable value given the nature of this simulation.

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Figure 26. Actual & Desired Velocity from cX-SIM Preliminary Simulation.

Figure 27. Deviation of Actual & Desired Vehicle Speed of cX-SIM Simulation.

Using the Set Layout Plots HEV Operation option of cX Graphics the sum of the

total torque desired from the powertrain (ICE, ISA and EM combined) as well as the total

torque delivered from the powertrain is visually represented in Figure 28. The total torque

request can never be negative because the control strategy lacks the appropriate

algorithms for this case. In order for negative torque to be requested from the powertrain,

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the battery state-of-charge must be approaching the acceptable lower bound. Since no

state-of-charge control is effectively implemented to date, the control strategy never

requests torque to replenish the battery. However, this does not mean that the powertrain

cannot deliver negative torque. As the vehicle slows down, the EM and ISA, as seen by

the resulting output torque curve of Figure 28, absorb the kinetic energy of the vehicle.

This results in the rather high deviations between total torque request and total torque

output seen in Figure 29.

Figure 28. Total Output and Requested Torque during the cX-SIM PreliminarySimulation.

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Figure 29. Deviations Between Actual & Desired Torque of HEV Powertrain.

n be

iewed in Figure 30. Keep in mind that these are not necessarily sequential, and the

The operating points of the ICE as selected by the current control strategy ca

v

control strategy selects the operating points of the ICE based only one of the three control

strategy objectives (See Chapter 5). Further more, the appearance of this figure will be

different once the complete control strategy is integrated into cX-SIM. However, the Fiat

ICE proves to be an efficient choice. As long as the torque of the ICE remains above ~50

Nm, the efficiency of this device never decreases below 33%-- a favorable result for the

Ohio State Challenge X team. However, as the torque demand increases the efficiency

reaches values in the upwards of 41%-- a good value for ICEs.

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Figure 30. ICE Operating Points during cX-SIM Preliminary Simulation.

The components of the electrical powertrain are also analyzed after the

preliminary simulation. Figure 31 displays the operating points of the EM on the

efficiency map on the EV1 motor. Again, these points are not necessarily sequential, and

are selected by the control strategy according to Figure 43 (See Chapter 5). The operating

points do appear to lie more so in the negative torque (regeneration) region than the

positive torque (motoring) region. This is another result of the incomplete control

strategy. The operating points seen in Figure 31 are not optimal for minimal fuel

onsumption and SOC control as a completed control strategy forces them to be.

onetheless, the EM tends to operate in the 60-75% efficiency region throughout this

particular driving cycle.

c

N

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Figure 3 ulation.1. Operating Points of the EM during the cX-SIM Preliminary Sim

Since no efficiency map of the ISA exists to date, the power of the ISA is shown

below. Note that the ISA power is zero, until the control strategy moves from Launchmode into Engine Start mode. At this point, the power will spike as seen at ~15 seconds

into the drive cycle. Here, the ISA turns ON in order to start the ICE. The negative power

seen in Figure 32 represents the ISA being driven by the ICE in order to generate

electrical power. As previously aforementioned, this figure will also look differently once

the complete control strategy is implemented.

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F

l component analyzed during this preliminary simulation is that of the

igure 32. ISA Power during the cX-SIM Preliminary Simulation.

The fina

battery. Figure 33 displays the SOC of the battery throughout the driving cycle. Thisfigure does not accurately represent how the SOC behaves during a given driving cycle,

as no SOC control is provided. However, this figure does display how the SOC increases

during replenishment, and decreases during power demand. The current limits of 60%

and 80% are initial boundaries set with the knowledge of past HEV development research

at Ohio State [6]. These limits will be optimized to accommodate the batteries being used

in the Ohio State Challenge X Equinox.

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Figure 33. Battery SOC during cX-SIM Preliminary Simulation.

4.2 cX-DYN

No preliminary results for cX-DYN have been acquired to date. Since cX-SIM is

a more efficient simulator, computationally speaking, for optimizing two of the main

parts of the control strategy, the primary focus has been directed toward completing the

quasi-static simulator. Key deadlines for the Challenge X competition (reports and Year 1

competition) have also shifted the focus on gaining full functionally of cX-SIM prior to

cX-DYN. However, once cX-SIM is fully operational, the changes to be made in order to

make cX-DYN operational will be trivial.

4.3 Rolling Chassis

In order to validate the modeling and simulations mentioned in Chapters 2 and 3,

the Ohio State Challenge X team has created a Rolling Chassis. One of the obsolete

future truck vehicles was taken and cut down to only the floor pan, firewall, frame, axles

and wheels. The driver seat and steering wheel were left as well. This serves as a

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platform on which all of the components described in Chapter 1 will be mounted for

initial testing. Once all of the actuators are assembled and the control strategy hardware

and software is installed, the complete verification process will begin. This will not only

verify the simulators, but also the ability for each of the aforementioned components to

be integrated together and behave according to the desired vehicle architecture.

4.3.1 Launch Test

Currently, only the EM is ready for testing and validation. The EM is attached to

the rolling chassis, and placed on a dynamometer as seen in Figure 34. Tests have been

conducted to analyze the effectiveness of the EM during electric launch. The figures that

follow show speed data from one such test. The pedals of the rolling chassis are used to

accelerate and decelerate the EM on the dynamometer. This approach accurately

resembles the accelerator pedal positions during the launch of the actual vehicle. With the

inertial and frictional values of the Equinox programmed into the dynamometer, the

accelerator pedal is pushed to the maximum position to generate a replication of a vehicle

launch.

In order to preserve the mechanical integrity of each device involved, there is a

torque request limit placed on the system. One of the tests has a limit of 40% torque

request. The second has a limit of 50%. This means that when the accelerator pedal is

completely depressed, only 40% of the total torque capacity of the EM is available for

delivery to the dynamometer. Figure 35 shows the EM motor speed during the 40%

limited test. Figure 36 displays the vehicle speed generated throughout the duration of

this test. The vehicle reaches a speed of ~24 kph (~15mph) in just under 10 seconds. The

launch is also performed with the torque output limit of 50%. Figure 37 and 38 show the

EM motor speed and vehicle speed, respectively, for this experiment. The vehicle reaches

a speed of approximately 37kph (~23 mph) in approximately 15 seconds. Recall that the

EM only provides motoring torque during launch for a short period of time (Launch

Mode), and then the ICE is started (Engine Start Mode) and then reigns as the primary

motoring device (Normal Mode). Considering the output of the EM is limited, initial

observations would suggest this actuator is capable of being a part of the system to meet

the goals specified in Table 1.

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There are a few factors that exist which alter n below. The most

prominent involves the experimental set-up. Doing a repeated launch test is very taxing to

the battery, so a power supply is used. However, the DC output of this power supply

fluctuates immensely. In order to stabilize this output, the power supply is connected in

parallel to a lead-acid battery pack. The pack serves as an electrical shock absorber, or

large capacitor. This configuration stabilizes the voltage going into the EM at rest;

however, when the launch is performed, the power oscillates slightly. This oscillation is

reflected in the speed figures seen below. The torque request is constant, but the speed

fluctuates due to a changing power input. Since the pack can respond more rapidly than

the power supply, as the launch initially begins the battery pack supplies the elec

energy until the power supply catches up. Once the power supply is up to speed, it

provides more than the requested power in order to charge the battery, as well as run the

EM. The battery abs shoot and the power supply then decreases output to

the point where the battery once again supplies power. The power supply detects this

drop in the battery his process would

repeat until a steady-state supply environment is reached between the two sources.

However, the launch test is not performed for this length of time.

the data see

trical

orbs this over

and once again provides an overshoot of energy. T

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Lead-Acid

Battery Pack

EV1 Motor

& Inverter

Chassis

Dynamometer

Rolling

Chassis

Figure 34. Rolling Chassis Experimental Set-Up.

Figure 35. Launch Test EM Motor Speed -40% Torque Limit.

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Figure 36. Launch Test Vehicle Speed- 40% Torque Limit.

Figure 37. Launch Test EM Motor Speed--50% Torque Limit.

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Figure 38. Launch Test Vehicle Speed-- 50% Torque Limit.

4.3.2 Model Verification & Mapping

The EM was ori algin ly the propulsion device for the EV1 electric car as

reviously mentioned. After deciding to use this component for Challenge X, the Ohio

tate team sent this drive unit to be rebuilt. When th

However, there is no method of requesting an exact torque from the EM, but merely a

aximum torque capacity. Therefore, a series of tests will be

peration. Once this extensive torque mapping exercise is completed, a modified

ersion of cX-SIM will investigated for simulator a

The mapping process will be quite extensive. Unfortunately, the chassis

p

S e rebuilt unit arrived testing began.

rough percentage of the m

conducted to create a map, so that known torque requests can be sent to the EM during

o

v ccuracy.

dynamometer is not calibrated correctly to yield an accurate power reading. Thus, the

torque mapping procedure involves varying the friction coefficient of the dynamometer

and running the EM at certain torque requests. Once the chassis reaches steady-state

speed, the torque being provided by the EM can be found. Effectively, the result of this

procedure will be the torque-speed map of the EM. The dynamometer specifies 3

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coefficients that make up the total load that is placed on the vehicle. However, these

coefficients are represented by the horsepower at 50 miles per hour and require some

manipulation to be used for this mapping. The general expression of the dynamometer

load is:

Friction A B C= + + (30)

where A is the rolling resistance, B is the intrinsic friction, and C is the air drag. The

variable of interest is the friction coefficient, B. Each of these variables has units of

HP@50 mph [10]. Knowing this fact we can relate this horsepower at a rated speed to

the force of the dynamometer,Fdyno(N):

dyno

SS

BF

v

= (31)

In this expression, represents all of the conversion factors necessary and vSSis the found

steady-state speed (mph). This force, along with the wheel radius, rwheel, is used to find

the torque at

the wheel, Twheel:

wheel dyno wheel T F r= (32)

Using the gear ratio, GB, the torque of the EM (TEM:) can be found.

wheelEM

GB

TT

= (33)

Figure 39 represents the vehicle speed of one such test. Notice how the vehicle reaches a

steady-state speed of approximately 8 kph (~5mph). To ensure this is in fact the steady-

state speed the derivative of this curve, acceleration, is found. In order to do this, a

second order polynomial was fit to the speed data and is shown in Figure 39. The

derivative of this curve is shown in Figure 40. The derivative approaches zero toward the

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end of the test, meaning the vehicle speed has reached a quasi-steady state value that can

be used in the aforementioned calculations. The result of this particular test is an EM

torque of 313.9 Nm.

Figure 39. EM Torque Mapping Test: Vehicle Speed (B=30).

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Figure 40. EM Torque Mapping Test: Acceleration (B=30).

This process will be repeated over a large range of friction coefficients and torques to

create a very detailed map. These output torques will be entered, with their corresponding

percentage torque request, into a look-up table. This will allow for an input torque value

in standard units (e.g. Nm) to be sent to the EM for operation. Once this table is

plemented, the verification odified form of cX-

M.

im of the model will be done with the m

SI

4.3.3 cX Test

This version of cX-SIM is called cX-Test and can be seen in Figure 41. Basically,

this model is the rear driveline and vehicle cut from the parent simulator. The vehicle is

included because the dynamometer simulates vehicle drag, rolling resistance, and grade

resistance. Here, a torque request is sent to the EM, and the model reacts accordingly.This simulation is analyzed with the condensed version of cX Graphics seen in Figure

2cX-Test Graphics. The same torque request is given to the actual EM on the rolling

hassis, and the resulting data is compared to the cX-Test data to ensure the model is

alid.

4

c

v

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Figure 41. cX Test Top Layer.

Figure 42. cX -Test Graphics.

Preliminary results from cX-SIM have been obtained and visually represented

using cX Graphics. Once cX-SIM is

fully functional, cX-DYN will be brought on-line as

well. Verifying both simulators is done via the rolling chassis; however, this task could

not be completed before the conclusion of this thesis. Not only will finishing the

simulators and verification be a task of the future, developing and integrating a robust

control strategy also needs to be completed.

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Chapter 5

Control Strategy Development

Future work includes performing the mapping and verification described in the

previous chapter, as well as developing a fully functional control strategy. With cX-SIM

nearing completion, and cX-DYN soon to follow, a suitable control strategy must be

implemented so the verification process can continue. The basics of the currently

considered control strategy are presented here. The ultimate design and fabrication of the

controllers necessary is left for future work on this project.

5.1 Overview

rimary focus of this research is modeling and simulation of the rearThe p

driveline of the Ohio State Challenge X Equinox. Development of the control strategy is

a task being pursued by other members of the OSU Challenge X team. However, the

literature has been investigated in regards to control of HEVs and the author has

participated in the development of the control strategy for the Hybrid Equinox. Therefore,

a brief summary of the control strategy is presented here.

The main objective of the control strategy is to reduce fuel consumption and

emissions while maximizing drivability by taking advantage of the multiple vehicle

modes. This is achieved by the potential energy recovery during braking, as well as the

availability of the additional degrees of freedom with respect to the power split to satisfy

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driver demand [7]. The control strategy being developed for the Ohio State Challenge X

Equinox is divided into two parts: primary control and secondary control. The primary

ontrol strategy commands the drivetrain components in real time as dependent on driver

requests, the state of each component, and of course the control strategy algorithm. The

secondary control monitors the state of each component, such as temperature, to ensure

fe operation [5]. The focus here is on the primary control strategy.

This con

State-of-charge, and Drivability [2, 5]. Each are considered independently and then

crea

c

sa

trol signal is decomposed into three decoupled signals: ECMS, Battery

summed together to te the final control signal. The process for obtaining the ultimate

control signal is described by Figure 43 below.

Figure 43. Control Strategy Schematic.

5.2 ECMS

The first factor considered by the control strategy is fuel consumption. Compliant

with the goals of Challenge X, the objective is to minimize this quantity. To accomplish

is, Ohio State chooses to use the Equivalent Consumption Minimization Strategy

CMS). The basis of ECMS is that all energy consumed by t

fuel tank [2]. In the case of HEVs, two fuel tanks are considered. The first is the

th

(E he vehicle comes from the

conventional fuel storage tank and the second is the battery, or some other energy storage

device. For ECMS purposes, the battery is modeled as a reversible fuel tank that can exist

in two states. First, the battery could be recharging, so fuel must be consumed, or kinetic

energy absorbed, to replenish this electrical energy. The second state is the battery could

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be using electrical energy to alleviate some of the load from the ICE to save fuel [4]. The

governing equation for ECMS is as follows:

( )* , * ,( )i j

i j

i j LHV

x u

m x u x u Q=& (34)

where ( )* ,i jm x u& is the mass flow of fuel for component *, ( )* ,i jx u is the efficiency of

component *, and LHVQ is known as the low-heating value of fuel. The quantity xi is a

state vector that includes the speeds of the ICE, AT, EM, and vehicle, as well as, the

positions of the AT, EM and vehicle. The control vector iu contains the torque of each of

the three actuators [2]. At any time during vehicle operation, the entire range of operating

points for ICE, ISA, and EM will be examined and equivalent flows will be found [4].

The point which minimizes the total equivalent fuel consumptionthe sum of each

* ,( )

i jm x u& -- will be chosen as the ECMS control input.

5.3 Battery State-of-Charge

One of the most important aspects to consider for effective HEV operation is

battery state-of-charge. The capacity of the battery as stated by the manufacturer cannot

reflect the change in this quantity due to usage. By maintaining the SOC, the degradation

of the battery capacity as a resu

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Arnett Thesis