Energy and Power Engineering, 2013, 5, 343-351 Published Online July 2013 (
Fuel Saving and Control for Hybrid Electric Powertrains
Mohamed Zaher, Sabri Cetinkunt
Mechanical and Industrial Engineering Department, University of Illinois at Chicago, Chicago, USA
Received May 7, 2013; revised June 8, 2013; accepted June 15, 2013
Copyright © 2013 Mohamed Zaher, Sabri Cetinkunt. This is an open access article distributed under the Creative Commons Attribu-
tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
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
opyright © 2013 SciRes. EPE
Quadrant I (Motoring)
Speed +
Torque +
Quadrant II (Generating)
Speed -
Torque +
Quadrant III (Motoring)
Speed -
Torque -
Quadrant IV (Generating)
Speed +
Torque -
Torque (T)
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
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
Copyright © 2013 SciRes. EPE
Copyright © 2013 SciRes. EPE
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-
12001300 1400 1500 1600 17001800 1900 2000 2100 2200
P ow e r (kW)
Engine Speed (rpm)
12001300 1400 1500 1600 17001800 1900 2000
2100 2200
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
P ow e r (kW)
En gine Speed (rpm)
8001000 1200 1400 1600 1800 20002200
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-
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
The battery SOC can be calculated using Equation (2)
it t
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]).
Copyright © 2013 SciRes. EPE
Copyright © 2013 SciRes. EPE
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
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.
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
Machine Displacement (m)
Machine Velocity (m/s)
Real Machine
050100 150 200
Engine Speed (rpm)
Machine Dis
050 100 150 200
Machine Displacement (m )
Consumed Fuel (Liters)
Real Machine
Simu lation
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
Copyright © 2013 SciRes. EPE
05 10 1520 25 30
Tim e (sec)
Disp lacem en t (m )
05 10 1520 25 30
Tim e (sec)
05 10 1520 25 30
Tim e (sec)
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
Tim e (sec)
05 10 15 20 25 30 35 40
Tim e (sec)
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.
Copyright © 2013 SciRes. EPE
Table 1. Aggressive truck loading baseline and hybrid com-
Machine Cycle Time
(sec) Fuel
(%) 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-
Machine Cycle Time
(sec) Fuel
(%) 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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