Fuel Saving and Control for Hybrid Electric Powertrains

doi:10.4236/epe.2013.55035

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Energy and Power Engineering, 2013, 5, 343-351

http://dx.doi.org/10.4236/epe.2013.55035 Published Online July 2013 (http://www.scirp.org/journal/epe)

Fuel Saving and Control for Hybrid Electric Powertrains

Mohamed Zaher, Sabri Cetinkunt

Mechanical and Industrial Engineering Department, University of Illinois at Chicago, Chicago, USA

Email: [email protected], [email protected]

Received May 7, 2013; revised June 8, 2013; accepted June 15, 2013

tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

cited.

ABSTRACT

This paper focuses on comparing the performance of the embedded control of a hybrid powertrain with the original and

downsized engine. The main idea is to store the normally wasted mechanical regenerative energy in energy storage de-

vices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is

in the opposite direction to the applied force or torque. A rule based optimal robust control algorithm is developed and

is tuned for different work cycles. A comparison of the fuel savings using the hybrid system with the original and

downsized engines is performed.

Keywords: Hybrid Powertrain; Embedded Control; State of Charge; Downsized Engine

1. Introduction

Fuel efficiency and reduced emissions are two of the top

considerations in all organic fuel power generations, in-

cluding diesel engines [1]. Alternative powertrain con-

cepts including hybrid and fuel cell are developed to ful-

fill these considerations. The concept behind the hybrid

devices is to store the excess, potentially wasted, me-

chanical energy in energy storage devices and reuse that

energy to support future operations. In any condition

where the speed of motion is in opposite direction to the

applied force or torque, the regenerative energy recovery

opportunity exists (Figure 1 [2]). This condition is satis-

fied in various conditions such as (Figure 2):

1) vehicle braking, (i.e., slow down to a lower speed on

zero slope or vehicle is moving down a hill and brak-

ing must be applied to maintain a desired speed);

2) load is moved by gravitational (load) force.

The real time control challenge is to balance the ma-

chine power demands from both the engine and the hy-

brid storage device. The constraints faced in developing

the control strategy are:

1) minimize fuel consumption while meeting low emis-

sion requirements;

2) maintain or improve the work-machine productivity;

3) prevent the depletion of the energy storage device, and

maintain an acceptable state of charge (SOC).

Yafu, and Cheng [3] studied mild hybrid electric vehi-

cles (HEV) with integrated starter generator (ISG). By

using a parallel assist control strategy and modeling the

system in Simulink, they achieved their objective of re-

ducing the fuel consumption. Teratani et al. [4] installed

a new Toyota hybrid system (THS) which improved fuel

economy with 40%. The old THS had slow response,

high vibration, and noise during starting and stopping.

They managed to reduce the size of the THS. Also, they

presented the control logic currently used in most ISG

systems, as well as presented a sequence of steps through

which the vibration and noise can be minimized. Karden

et al. [5] studied the energy storage devices for HEVs

and concluded that for the foreseeable future the Lith-

ium-ion batteries and Nickel metal hydride will dominate

the electric hybrid market for their improved perform-

ance and smaller size compared to other storage devices.

He and Hodgson [6,7] modeled and simulated the

electric hybrid vehicle built by the University of Tennes-

see. Their research proposed using a Lithium-ion battery

as a modification from the original energy storage device

and proposed a control strategy based on the study of the

battery state of charge, power output of the engine and

the hybrid, and the acceleration capability of the vehicle.

Liu and Peng [8] studied the control of Toyota PSHEV

using two control algorithms: Stochastic dynamic pro-

gramming and equivalent consumptions minimization

strategies (ECMS) [9]. They used deterministic dynamic

programming solutions as a benchmark for comparisons

rather than implementable solutions to assess the per-

formance of both algorithms. They concluded that with

stochastic dynamic programming (SDP) an extra input

operating gear mode is needed beside the engine speed

M. ZAHER, S. CETINKUNT

344

Quadrant I (Motoring)

Speed +

Torque +

Quadrant II (Generating)

Speed -

Torque +

Quadrant III (Motoring)

Speed -

Torque -

Quadrant IV (Generating)

Speed +

Torque -

Torque (T)

max

Speed

peak

cont

Figure 1. Torque versus speed motoring and generating

quadrants (adopted from [2]).

Figure 2. Regeneration opportunities.

and the SOC, while with ECMS, frequent shifting should

be avoided by adding extra constraints between gears

switching decisions. Syed et al. [10] used a fuzzy logic

gain scheduling algorithm with proportional-integral (PI)

controllers in Power split HEV (PSHEV). The results of

testing the controller on a Ford Escape showed that a

minimum of four rules are needed to ensure smooth en-

gine speed.

Canova et al. [11] studied the engine start/stop dy-

namics which led to the development of an engine model

that was used in a linear quadratic regulator control algo-

rithm developed and optimized via design of experiments

(DOE) methods for HEVs with ISG. Lin et al. [12] studied

the dynamics of the Toyota Prius PHEV and developed

an optimal control energy management strategy and arti-

ficial neural networks that were modified to a suboptimal

controller. Atkins and Koch [13] compared several pow-

ertrain configurations including downsizing engines,

supercharging, fuel cell vehicles, electric vehicles and

HEVs and evaluated their performance and emissions.

Ogawa, et al. [14] described the work done on the de-

velopment of the integrated motor assist technology de-

veloped at Honda Co. and implemented in the Civic ve-

hicle. They reduced the emissions and fuel consumption

by reducing the engine displacement, implement an idle

stop strategy and recovery of regenerative energy during

deceleration that is used to assist through a brushless DC

motor. Evans, et al. [15] introduced the architecture of

General Motors Sierra pickup truck hybrid vehicle. They

used parallel electric hybrid powertrain with a rule based

control based on the vehicle functions to achieve their

objectives. Liang, et al. [16] developed a parametric de-

sign for HEVs that could be implemented on military

vehicles or public transit buses. They also used a rule-

based control algorithm based on the knowledge of the

functions of different systems to achieve optimal control.

The machine investigated in this work is a medium

wheel loader (MWL). However, the procedures used are

general such that they are applicable to other types of

work machines. This paper focuses on controlling hybrid

powertrain mobile vehicles via real time optimized ro-

bust control and reports on fuel saving opportunity due to

a smaller engine usage with the hybrid system.

2. Medium Wheel Loaders and Hybrid

Technology

The prototype medium wheel loader (MWL), subsystems,

and a detailed dynamic, machine model are described

below (Figure 3). For this purpose, a high fidelity virtual

model is needed to mimic the actual machine. However,

replicating every aspect of the machine in mathematical

models is unmanageable due to unpredicted factors and

physical inaccuracies. A high fidelity virtual dynamic

machine model is developed using C language and em-

bedded in S-functions in Simulink.

1) Nonlinear engine dynamic model, including the steady

state lug curve (torque-speed capability) at all fuel

injection rates, but not including the combustion

thermodynamics and chemistry model.

2) Nonlinear dynamics of the transmission including the

steady state torque converter speed and torque in-

put-output relationship, and multi stage planetary gear

box which includes the dynamics of the electro-hy-

draulically actuated clutches and brakes to engage and

disengage selected set of the for the desired gear ratio.

3) Nonlinear dynamics of the electro-hydraulic circuit for

implement, steering and brake system, along with the

nonlinear dynamics of the linkage mechanisms.

4) And finally the inertial rigid body dynamics front and

rear frame of the machine as well as the flexible tire

dynamics and ground interactions.

More details of the virtual machine dynamic model are

presented in the PhD thesis by Mohamed Zaher [17].

There are four general work cycles for this type of

M. ZAHER, S. CETINKUNT

345

Figure 3. Wheel loader systems [2].

machine: truck-loading, load-and-carry, pile dressing,

and roading. Both the truck-loading and the load-and-

carry are cycles that involve digging, moving earth from

one location to another and dumping it at the new loca-

tion. The truck-loading is loading a truck or a hopper

with earth and is categorized into two major cycles: ag-

gressive truck-loading (ATL), and moderate truck-load-

ing (MTL). ATL is characterized by its speed and the

machine is operated with the engine at full throttle at all

times. Moderate truck-loading (MTL) is similar to the

ATL cycle but the operator varies engine speed com-

mand and may never reach full throttle. The load-and-

carry cycles is moving dirt to a hopper far away from the

pile and is defined by the travel distance into short and

long. Pile dressing is moving the earth in the pile around

to make it ready for the previous cycles. Roading is

driving the loader from one location to another without

being involved in any of the previous cycles.

The regular MWL engine is a four stroke turbocharged

after cooled injection approximately 9 liters diesel engine,

with average power rating (Figure 4) of 224 - 261 kW at

1800 - 2200 rpm and compression ratio of 17:1. The

downsized engine is a four stroke turbocharged after

cooled injection approximately 7 liters diesel engine,

with average power rating (Figure 5) of 140 - 225 kW at

1800 - 2200 rpm and compression ratio of 16.5:1 The

output gross power from the engine is not the actual

available power to the wheel loader main systems due to

power consumptions (6% - 14%) from accessories such

as the alternator, muffler, emission control, and cooling

system [18]. Unlike automotive engines, the diesel en-

gine speed in construction equipment is limited to about

2300 rpm due to the need for higher torque values at

lower machine speeds, desire for longer engine life and

reduced fuel consumption. The engine dynamic model

allows for the calculation of the torque and speed along

the lug curve and calculating the engine fuel consump-

tion via the brake specific fuel consumption map. With

hybrid implementation this engine could be downsized to

a 7 liters diesel allowing the engine to run in a more effi-

M. ZAHER, S. CETINKUNT

346

12001300 1400 1500 1600 17001800 1900 2000 2100 2200

140

160

180

200

220

240

260

280

P ow e r (kW)

Engine Speed (rpm)

12001300 1400 1500 1600 17001800 1900 2000

1600

1500

1400

1300

1200

1100

2100 2200

900

1000

Torque (N.m )

Rating A

Rating B

Rating C

Figure 4. Rated 9 liters engine lug curve and power rating.

8001000 1200 1400 1600 1800 20002200

100

150

200

250

P ow e r (kW)

En gine Speed (rpm)

8001000 1200 1400 1600 1800 20002200

400

600

800

1000

1200

1400

T orque (N.m )

Rating A

Rating B

Rating C

Figure 5. Rated 7 liters engine lug curve and power rating.

cient zone and making the hybrid system more cost ef-

fective.

The primary function of the powertrain is to transfer

the torque from the engine to the wheels, thus, creating

the necessary rimpull for the motion through a series of

speed reductions and torque multiplications. The power-

train of a wheel loader consists of wheels, axle reduce-

tions (simple gear train), differentials, axles, a torque

converter (TC) and transmission. A TC is a hydro-dy-

namic coupling which transfers torque between its input

and output while absorbing the difference in speed, hence,

it is the evolution from clutches. The difference in speed

is absorbed and dissipated in the oil inside the TC in the

form of heat. The TC is followed by a gearbox which

reduces the output speed to a desired range for the ma-

chine ground velocity. The gearbox is constituted of sev-

eral planetary gear trains connected together via brakes

and clutches that determine the final reduction ratio. The

explanation of how the mechanism works is available in

literature [18].

MWL hydraulics system is a closed center load sens-

ing hydraulics system for work-tool (implement) circuit

as it avoids dissipation of energy since it adapts the

amount of flow provided by the pump to the real needs of

the machine, minimizing the losses unlike open center

systems. The load sensing system compares the pump

pressure at the output to the cylinder pressures and ad-

justs the pump’s swash plate based on that feedback in

order to provide the correct amount of flow and maintain

a certain pressure differential. The chassis is the body

and linkages of the machine and it is governed by the

mechanical kinematic constraints and dynamics which

can be represented using multibody dynamics approach.

3. Electric Hybrid

The electric hybrid concept (Figure 6) converts the re-

generative mechanical energy to electrical energy via an

electric generator, and then stored in electro-chemical

batteries. The electric hybrid consists of a dual function

motor/generator (ISG) actuator, inverter that requires a

separate cooling system, and a Lithium-Ion battery pack.

The Li-I battery is selected as it is the most promising

rechargeable battery technology available according to

literature, and is widely used in electric hybrid technol-

ogy [19]. The change in SOC in a time interval, dt, with

discharging or charging current i may be expressed using

Equation (1) [20]:



SOC it t

Qit





(1)

The battery SOC can be calculated using Equation (2)

[20]:

SOC SOCd

it t

Qit



 (2)





where



Qit is the ampere-hour capacity of the bat-

tery at current rate i, SOC0 is the initial SOC and the





it is the ampere; positive during discharging and

negative during charging of the battery.

While the electric hybrid is the most expensive, it is

receiving the most investment in all mobile industries

due to its maturity. With the engine downsize, the ISG

system costs come to neutral. The diesel engine is the

primary power plant, electric hybrid constitute the energy

bumper. The ISG adds power to powertrain from the

battery when power assist is needed, and stores the en-

Figure 6. Electric hybrid concept (adopted from [5]).

M. ZAHER, S. CETINKUNT

347

ergy to the battery from the powertrain when there is

power-storage opportunity exists. The electric hybrid

cooling cycle passes through all the components where

the coolant fluid goes through the shunt tank into radiator,

through the pump, inverter, battery and ISG and then

back to radiator. The shunt tank is connected to all the

hybrid parts to allow air bubbles to escape and to the

radiator to allow coolant to expand and supply excess

fluid.

chine operations that will lead to completing the cycle.

4.2. Rule-Based Logic

The rule-based control (Figure 7) is designed based on

knowledge of machine functions and system of the ma-

chine. It is a torque based control that will send out de-

sired torque to the ISG, thus, the generator-motor action

will be determined. To decide whether to charge or assist

multiple factors are considered. These factors are repre-

sented by parameters and thresholds that will enable

reaching the decision. These parameters and thresholds

are tuned for different cycle to achieve optimum robust

results based on the criteria listed earlier. The parameters

tuned in this logic are: delay timer, assist threshold,

charge threshold, low SOC threshold, idle charge torque,

ISG torque required threshold, engine speed factor, as-

sist/charge threshold offset, and the idle charge SOC

threshold. The first step in the decision process is to mul-

tiply the engine torque limit by three of the parameters

and compare the results to the engine load torque. These

parameters are the assist threshold, charge threshold, and

hysteresis factor. Another gain exists to assist with that

decision is the low SOC threshold which is biased to

charging when the SOC drops below it. For low SOC

mode, the more aggressive (charge bias) assist/charge

thresholds should be arrived at by taking the standard

values and offsetting them by assist/charge threshold

offset. These calculations along with comparing the en-

gine speed and torque demand with the actual will de-

termine the torque demand out of the engine. This step

determines if the engine is capable of supplying the de-

manded power on its own, need assistance from the hy-

brid, or has excess power to be used for charging. If

4. Control Strategies

In this section, the proposed control strategy will be in-

troduced. The control strategy under investigation is a

rule-based control. The rule-based control (Figure 7) is

easy to implement on the machine. The parameters of

this controller are tuned to different cycles to minimize

the fuel consumption, bring the final SOC closest to the

initial SOC, minimize the cycle time, and regulate the

minimum engine speed to be close to 1000 rpm or higher.

The high level Simulink diagram is shown (Figure 7).

4.1. Cycle Model

The cycle model is built to mimic the commands of the

real operator sent to the different machine systems as

well as the work area. Two different cycles and hence

two different operator models are used in this investiga-

tion: ATL, and MTL. The cycle model is constructed via

if statements with multiple possible output scenarios

based on time and distance along the cycle. Each output

from each if statement corresponds to a group of time

and distance based operator commands to the machine.

The sequence of these commands will result in the ma-

Figure 7. Rule-based control high level Simulink diagram.

M. ZAHER, S. CETINKUNT

348

the engine is idle and the SOC is less than the idle SOC

charge threshold then a charge command is issued.

Charging is also enforced when the retard engine speed

threshold is exceeded. With any gear up-shift or if the

engine deceleration rate exceeds the engine deceleration

rate threshold, assist is triggered. The gain tuning process

is an iterative and time consuming process. The target

from this process is to obtain an optimal set of parame-

ters for robust design such that with inaccuracies or slight

changes in the gain values do not affect the performance

and in the same time achieve the optimal possible solu-

tion. The orthogonal array experiments only requires a

fraction of the full factorial combinations through which

the same result can be achieved by using the analysis of

the variance (ANOVA) thus making it the most suitable

for tuning the nine parameters allowing for an estimate of

the optimal set of interactions. By repeating this process

over time, the desired optimal robust solution can be ob-

tained for each cycle [21].

5. Results

In this section, the machine model validation as well as

the results obtained during this work will be presented.

The validation results will show that the used machine

model is in good correlation with the real life machine.

The results obtained will show that when the engine is

downsized, and the hybrid system is implemented, the

engine achieves more fuel savings than with the original

engine. The model validation is performed by obtaining

machine data from the real life wheel loader and giving

the same operator commands to the machine model. If

the model is in good correlation, the machine model be-

havior will be in good correlation with the real machine

behavior. Figure 8(a) shows both the real and simulated

machine velocities. The general speed pattern is the same

and is in good correlation. The differences between then

could be attributed to the use of approximate dynamics in

the different machine systems. Figure 8(b) shows the

desired engine speed sent by the operator to the machine

and implemented in the cycle model, the real machine

engine speed and the simulated engine speed. From the

Figure 8(b) it is clear that the modeled engine speed

response to the desired engine speed command matches

to a great extend the actual engine speed response.

The differences could be attributed to the use of ap-

proximation and estimated numbers in various points in

the model including the load on the machine. Figure 8(c)

shows the amount of fuel consumed by the machine ver-

sus that estimated by the machine model. The general

trend of the fuel consumption is the same and the differ-

ence in the end point is minimal. Thus, it can be con-

cluded that the machine model has a very good correla-

tion to the real machine. In order to determine the benefit

050100 150 200

-4

-2

Simulation

Machine Displacement (m)

Machine Velocity (m/s)

Real Machine

(a)

2500

050100 150 200

500

1000

1500

2000

Desired

Engine Speed (rpm)

Simulation

Machine

Machine Dis

lacement

(

)

(b)

0.8

050 100 150 200

0.2

0.4

0.6

Machine Displacement (m )

Consumed Fuel (Liters)

Real Machine

Simu lation

(c)

Figure 8. Results for validation the accuracy of virtual

machine dynamic mode. (a) Validation machine velocity; (b)

Validation engine speed; (c) Consumed fuel.

behind the use of hybrid technology in MWL, a com-

parison between the baseline machine, the hybrid with

the regular 9 liters engine, and the hybrid with the 7 liters

engine is conducted. Figure 9 and Table 1 show the

comparison in case of ATL cycle. With the 9 liters en-

gine, the hybrid system increases productivity by about

1.4% and decreases the fuel consumption by 6.65%. On

the other hand, the hybrid system with the downsized

seven liters engine maintains the productivity as it is but

decreases the fuel consumption by about 20%. Figure 10

and Table 2 show the comparison in case of MTL cycle.

With the 9 liters engine, the hybrid system decreases

productivity by about 2.83% and decreases the fuel con-

sumption by 6.14%. On the other hand, the hybrid sys-

tem with the downsized seven liters engine maintains the

productivity as it is but decreases the fuel consumption

y about 26.5%. The disadvantage of using a downsized b

M. ZAHER, S. CETINKUNT 349

05 10 1520 25 30

Tim e (sec)

Disp lacem en t (m )

05 10 1520 25 30

100

200

300

400

Tim e (sec)

Grams

05 10 1520 25 30

Tim e (sec)

Percent

Bas eline

9 Li ters Hybrid

7 Li ters Hybrid

Baseline Displacement

9 Li ters Hybrid Displacem ent

Baseline Fuel Consumption

9 Li ters Hybri d Fuel C ons umption

7 Li ters Hybrid SO C

9 Li ters Hybri d SO C

7 Liters Hybrid Fuel Cons umption

7 Li ters Hybri d D isplacem ent

Figure 9. ATL hybrid verses baseline.

05 10 15 20 25 30 35 40

Tim e (sec)

D isplacem en t (m )

05 10 15 20 25 30 35 40

100

200

300

400

500

Tim e (sec)

Grams

05 10 15 20 25 30 35 40

Tim e (sec)

Percent

Base line

9 Liter s Hybrid

7 Liter s Hybrid

9 Li ters Hybrid S OC

Baseline Fuel Consumption

9 Liters Hybrid Fuel Cons um ption7 Liters Hybrid Fu el Cons um p ti o n

7 Li ters Hyb rid S OC

Base line

7 Li ters Hybrid Displ ac em ent

9 Li ters Hybrid D ispl ac em ent

Figure 10. MTL hybrid verses baseline.

M. ZAHER, S. CETINKUNT

350

Table 1. Aggressive truck loading baseline and hybrid com-

parison.

Machine Cycle Time

(sec) Fuel

(grams)

Productivity

(%) Fuel consumption

(%)

Baseline 32.15 344.6

9 Liters

Hybrid 31.7 321.7 1.4 −6.65

7 Liters

Hybrid 32.1 275.7 0.16 −20

Table 2. Moderate truck loading baseline and hybrid com-

parison.

Machine Cycle Time

(sec) Fuel

(grams)

Productivity

(%) Fuel c onsumption

(%)

Baseline 37.7 413.17

9 Liters

Hybrid 38.8 387.8 −2.83 −6.14

7 Liters

Hybrid 37.5 303.9 0.53 −26.45

engine is that if the battery runs out of charge, the engine

may not be able to support the machine functions. On the

other hand, the hybrid with the original engine doesn’t

provide the desired fuel consumption reduction.

6. Conclusion

The implementation of the hybrid system on the medium

wheel loader is expected to maintain the productivity of

the machine within acceptable range. The usage of the

hybrid system with a 7 liters engine is expected to reduce

the fuel consumption on the machine by 20% - 27% at

the simulated cycles. The usage of the hybrid system

with the 9 liters engine is expected to reduce the fuel

consumption on the machine by 6% - 7% at the simu-

lated cycles.

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