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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Mechatronic Design and Control of
Hybrid Electric Vehicles
Bernd M. Baumann, Gregory Washington, Bradley C. Glenn, and Giorgio Rizzoni, Member, IEEE
Abstract—The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV’s).
Toward these ends, four issues are explored. First, the development of HEV’s is presented. This synopsis includes a novel definition of degree of hybridization for automotive vehicles. Second,
a load-leveling vehicle operation strategy is developed. In order
to accomplish the strategy, a fuzzy logic controller is proposed.
Fuzzy logic control is chosen because of the need for a controller
for a nonlinear, multidomain, and time-varying plant with multiple uncertainties. Third, a novel technique for system integration
and component sizing is presented. Fourth, the system design and
control strategy is both simulated and then implemented in an actual vehicle. The controller examined in this study increased the
fuel economy of a conventional full-sized vehicle from 40 to 55.7
mi/h and increased the average efficiency over the Federal Urban
Driving Schedule from 23% to 35.4%. The paper concludes with
a discussion of the implications of intelligent control and mechatronic systems as they apply to automobiles.
Index Terms—Automotive control, hybrid vehicle control, intelligent control of automobiles.
I. INTRODUCTION
S
INCE the oil crises of the 1970’s, fuel economy has been
one of the dominant issues in automobile performance.
Achieving the lowest possible fuel consumption helps to save
natural resources and is more economical for consumers. It also
translates directly into lower emissions. Often in contrast with
these requirements, customers continue to demand increasing
comfort and performance. In general, there are two methodologies that can be employed to reduce the fuel consumption of an
automobile [1]:
1) reducing losses such as aerodynamic drag, rolling resistance, and braking losses due to the vehicle inertia;
2) increasing the efficiency of energy conversion.
While the first approach relates to design and structure of the
vehicle’s body and, therefore, to the vehicle concept, the second
approach relates to the power train. Three ways of developing
more fuel-efficient power trains can also be identified:
1) optimization of existing power-train components [e.g., direct injection (DI) technology for internal combustion engines (ICE’s)];
Manuscript received July 17, 1998; revised May 10, 1999 and December 1,
1999. Recommended by Technical Editor H. Peng. This work was supported by
the National Renewable Energy Laboratory.
B. M. Baumann is with DaimlerChrysler Research and Technology, D-70546
Stuttgart, Germany.
G. Washington, B. C. Glenn, and G. Rizzoni are with the Department of Mechanical Engineering, The Ohio State University, Columbus, OH 43210 USA.
Publisher Item Identifier S 1083-4435(00)02470-4.
2) development of new power train components (e.g., fuel
cell technology, flywheels, and ultracapacitors);
3) combination of existing power-train components into hybrid drivetrains.
The work presented in this paper focuses on control issues
arising from the implementation of hybrid drivetrains. In order
to accomplish this task, the basic concepts of hybrid vehicles
(HV’s) will be explained and a load-leveling strategy for a
parallel hybrid electric vehicle (HEV) will be presented. An
application-oriented overview of fuzzy logic control (FLC)
will demonstrate its suitability for the control of HV’s. The
implementation of a supervisory controller, which coordinates
an ICE and an electric machine (EM), will also be presented.
The system will then be compared to existing control strategies.
Finally, the strategy will be demonstrated on an actual vehicle.
The study of HV’s and their control is not new. Many researchers have engaged in the development of hybrids at various levels since the 1970’s [11]–[25]. While the development
of HEV’s is a rapidly advancing topic that has led to implementation and simulation, the development of advanced control algorithms (at least, that reported in the open literature) has not
kept pace with hybrid design [12]–[24]. Novelty in the work
presented in this paper is evident in three areas: 1) this study
represents the first reported usage of an intelligent controller,
FLC, in an HEV; 2) this study defines and quantifies a mechatronics based term for classifying HV’s. This new terminology
is called degree of hybridization (DOH); and 3) this study also
defines a novel mechatronics-based technique for initial sizing
of the ICE and the EM in a hybrid-electric power train.
II. HEV’S
The 1913 Webster Dictionary explains the word “hybrid” as
“the offspring of the union of two distinct species” and as being
“produced from the mixture of two species.” An HEV can, thus,
be seen as a mixture of an ICE-powered automobile and an electric vehicle (EV). When designing an HV, two major issues must
be resolved. The first is: how does one size the EM and ICE?
This is one of the most complicated issues in constructing the
hybrid drivetrain. Traditionally, simulations have been used to
find a good mix of ICE and EM. These simulations may be complex and time consuming. The second issue involves the choice
of a proper operation strategy. These issues represent components of ongoing research but one possible solution can be found
using the “synergy” principle of mechatronics. In other words,
the overall system configuration and control strategy should
make the final product better than just the addition of the individual components themselves.
1083–4435/00$10.00 © 2000 IEEE
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
59
One of the first choices that a designer must make before
sizing is whether the automobile will be ICE dominated or EM
dominated. An EM-dominated vehicle has an electric motor as
the principal propulsion system. The ICE in this case will be
considerably smaller and will probably be used to help charge
a very large battery pack along with supplying power to support vehicle movements during peak demand. An ICE-dominated HV will have a larger ICE, a small EM, and (possibly)
a small battery pack. The authors chose an ICE-dominated hybrid because of the significant weight savings associated with
a small battery pack, relative ease of implementation, and cost.
This is an important step and it determines the vehicle configuration (i.e., parallel or series) and the control strategy. To quantify the level of domination of an HEV, the concept of DOH has
been developed. This number permits the distinction among different HV’s using the same drivetrain configuration, yet with
different components. The DOH, a number between 0–1, represents the ratio of the maximum power output of the two energy conversion machines. Although it is possible to define this
number for any number of energy conversion machines included
in the power train, the case of an HV operating in two different
energy domains results in the following definition for the DOH,
and
representing two different
with the subscripts
energy conversion devices:
(1)
DOH
To be more specific, for an HEV using one ICE with a nominal
and one EM with the nominal power
power output
, the DOH can be written as
output
(2)
DOH
Application of (2) to a conventional vehicle (CV) powered
only by an ICE results in
DOH
(3)
Similarly, the result for an EV is
DOH
(4)
Fig. 1 shows the graphical representation of (2). Strictly
,
speaking, a CV from today’s production has a DOH
because the alternator and starter are EM’s with power flows to
or from the ICE. The DOH is an important mechatronic design
tool because it provides a quantitative measure of where power
is flowing in a hybrid. This helps the designer decide what
type of control strategy to use and what component (i.e., the
ICE, EM, or both) will be targeted for control. For instance, if
the HV is significantly ICE dominated (0.48 or lower on the
ICE side), the designer may want to emphasize control of the
variables associated with the ICE. The rationale for this choice
stems from the fact that the payout for control of the variables
associated with the ICE has a greater potential, in terms of their
effect of the total amount of power consumed or stored, than
the variables associated with the EM. This measure provides
Fig. 1.
DOH for HEV.
the designer with quantifiable justification for focusing most of
his/her energy on using the EM to maximize ICE performance.
A 10% increase in performance of a 66-kW ICE will eclipse
the same percentage increase in performance of a 20-kW EM.
This strategy does not tell the designer to neglect the EM, but
it does offer a quantifiable metric that the designer can use to
determine where control efforts should be emphasized. This
highlights the “synergy” principle of mechatronic systems
because it helps ensure that the control strategy can actually
take advantage of the vehicle configuration. In addition, when
cost concerns are considered, the DOH can be used as part of a
cost-benefit analysis.
Once the size and type of ICE (or dominant component) is
chosen, the EM (or nondominant component) can be chosen.
A simplified, but quite accurate, method for HEV design based
on an ICE-dominated system can be shown by examining the
efficiency map of the ICE (or dominant component) at various
operating points. An efficiency map is shown in Fig. 2. The solid
circular contours on Fig. 2 represent lines of constant efficiency.
The dotted lines represent lines of constant power. The (×’s)
on the plot indicates the operating points of an ICE (66-kW
DI Diesel engine) at various points in the driving cycle. The
dashed lines in the plot represent lines of constant power. Upon
examining the peak point of efficiency and its relationship to
the (×’s) one can see that the center of mass of these points can
be shifted closer to the optimum operating point of the ICE if
about 20 kW of power is added or removed. Upon adding 20 kW
of power to all the (×’s), a set of new values represented by the
( ’s) can be determined. Based on the efficiency map, these new
points reveal a set of more efficient operating points. The same
design strategy can be used for fuel use or other metrics. Using
this procedure, one can conclude that a 20-kW peak EM should
be used. This is not meant to replace modeling and simulation,
but it does give the designer an opportunity to narrow choices
quickly.
As an example, the actual automobile used in this study was
a production 1997 Chevrolet Lumina that was modified with a
66-kW direct injection diesel engine and a 20-kW EM [1]. The
with a bias on the ICE
automobile has a DOH
side. The significant bias on the ICE side means that the “overall
system efficiency and fuel economy will be dominated by the
ICE.” Employing the DOH-based strategy expressed earlier, any
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Fig. 2. Efficiency map with operating points.
control strategy used should first pay specific attention to the
fuel use and efficiency of the ICE.
An HEV can have many configurations. The vehicle used
in this study has a parallel configuration because both the ICE
and the EM are directly coupled to the transmission, as shown
in Fig. 3. In this case, the EM charges the battery pack when
working as a generator and discharges the battery pack while
assisting the ICE in powering the vehicle when working as a
motor.
III. VEHICLE OPERATION STRATEGY
The operation strategy represents how the individual components of the drivetrain will interact with one another. It is normally a function of the size of the power-train components, (i.e.,
nominal and peak power output of the ICE and the EM), the
means for controlling the power flow, such as transmissions or
clutches, and dependency of the components on each other. The
automobile developed in this study uses a methodology known
as load leveling to force the ICE to act at or near either its peak
point of efficiency or its best fuel use at all times [20], [21]. The
idea behind load leveling an “IC-dominated hybrid” (i.e., one
with a DOH less than 0.48 and a bias on the ICE) is to move
the actual ICE operating point as close as possible to some predetermined value for every instant in time during the vehicle
operation. If the best efficiency is needed, the vehicle operation
points will be forced in the vicinity of the best point of efficiency
at a particular engine speed. If the best fuel economy is needed,
then vehicle operation points will be forced in the vicinity of that
point. The resulting power difference will be used or contributed
by the EM (or nondominating component). Since the EM has a
considerably smaller power rating than the ICE and the DOH is
on the ICE side, it can be reasonably assumed that the efficiencies of the EM and the battery pack will have a smaller influence
on the overall efficiency. This highlights the effectiveness of the
DOH. The operation strategy can be explained in detail by examining the efficiency map in Fig. 4 [10].
The four points shown in Fig. 4 are all characterized by the
property that they correspond to a lower efficiency than theoretically achievable by the particular ICE. To perform load leveling
based on the ICE efficiency, one has to control the ICE so that
the operating points are shifted closer to the point of best efficiency. This will also change the power output of the ICE since it
is proportional to the product of engine speed and engine torque.
Changing the location of the actual operating point on the efficiency map will, in general, require a change of engine speed
and engine torque. The actual gear ratio and the vehicle speed
will determine the engine speed. However, one has much more
control authority over engine torque than over engine speed. The
ICE used in this study comes equipped from the manufacturer
with drive by wire (typical for DI diesel engines), i.e., the accelerator pedal is attached to a potentiometer which delivers an
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
61
Fig. 3. Power-train configuration of the HEV.
TABLE I
QUALITATIVE DESCRIPTION OF A LOAD-LEVELING STRATEGY FOR A PARALLEL HEV.
input signal to the engine control unit (ECU). The ECU then
controls the amount of fuel injected into the cylinders. By modifying the input signal to the ECU, the torque production of the
ICE can be changed.
To compensate for the different power output of the ICE, one
has to adjust the contribution of the EM to the overall powertrain output. This means that sometimes the EM will function
as a motor assisting the ICE and at other times it will operate
as a generator storing electric energy in a battery pack. When
operating as a motor, the possible power contribution of the EM
to the overall drivetrain is limited by the state of charge (SOC)
of the battery pack.
Table I summarizes the necessary adjustments of gear ratio,
accelerator command to the ICE, and torque command to the
is equivalent to
EM. The change in accelerator command
the difference between the accelerator signal commanded by the
driver and the actual accelerator signal sent to the ECU. A posiis equivalent to operating the EM as a motor, whereas
tive
means operation as a generator. For instance, the
a negative
first case states that the engine speed and torque are too low. To
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Fig. 4.
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Generic efficiency map of an ICE, showing the locations of arbitrary operating points relative to the point of best efficiency.
remedy this situation, the gear ratio must be increased (which
means downshifting in a manual transmission), the accelerator
command must be increased, and to maintain a constant overall
power-train power output, the EM must be operated as a generator. This operation as a generator keeps the excess torque that is
generated by the ICE from being delivered to the drivetrain. The
implementation of the operation strategy of an HEV requires a
controller to command the adjustments as shown above.
IV. FLC OVERVIEW
Intelligent control has become more and more popular as the
capabilities of modern computers have dramatically increased.
The intelligent control technique employed in this paper is entitled FLC. FLC was chosen in this study because it can handle
both nonlinear data and linguistic knowledge [2]. In addition,
most hybrid systems in existence today use a rule base that is
implemented by programmable logic control (PLC). The fuzzy
logic controller can accomplish the same task more efficiently
and without the use of lookup tables or interpolation. In classical
set theory, an element of any universe can be either a member
of the set or not. Fuzzy sets, however, are characterized by the
fact that an element of the universe of discourse has a so-called
degree of membership, determined by a membership function
(MSF), i.e., an element cannot only belong or not belong to a
set, but also belong to more or less of that set. This fuzziness,
a characteristic of human thought and classification processes,
can be useful in describing control policies for systems that are
difficult to define simply in a precise mathematical fashion. FLC
has been the focus of many studies a complete overview can be
found in [2]–[5].
A fuzzy logic controller makes use of fuzzy sets and of the
methods of fuzzy logic to represent inputs and outputs. As is the
case with many other intelligent control techniques, fuzzy logic
is based on an input–output or black-box relationship. However,
the black box in this case is filled with rules created by humans.
Together, these rules form the rule base that represents the control laws. The main parts (i.e., procedures) of the controller are
as follows:
1) fuzzification interface converts the controller inputs to
data that the inference mechanism uses to activate and
apply rules;
2) rule base that contains the information that an expert
would use in controlling the car; inference mechanism,
which applies the experts knowledge in making control
decisions;
3) defuzzification interface, which is the transformation of
the results from the inference process to crisp (i.e., definite) outputs.
The way a FLC works and can be implemented is straightforward and intuitive. On the other hand, the mathematical formulation can be cumbersome. Therefore, the methodology of
an FLC will be explained using a highly simplified yet relevant
example. Consider a controller with two inputs and one output,
as shown in Fig. 5. Each input is represented by five triangular
MSF’s. The specific control task is to send a torque command
to an EM, based on a desired torque contribution
and
the SOC of the supply battery pack. All signals are normalized.
(meaning maxThe range of the torque signal is between
imum torque as a generator) and 1 (meaning maximum torque
as a motor). The SOC range is between 0 (meaning the battery
pack is completely discharged) and 1 (meaning the battery pack
is fully charged).
The rule base in our simplified example, which represents the
control law, has 25 rules (found by multiplying the total number
of MSF’s of each input). Let us suppose that the actual value of
and the actual SOC 0.50,
the desired torque
as shown in Fig. 6. The two rules that are on are given by the
member.
is high positive and SOC is medium, then
is
• If
positive.
is positive and SOC is medium, then
is neu• If
tral.
This rule base restricts the actual torque command sent to the
EM, considering the SOC of the battery pack. If the battery pack
is not completely charged, the torque command is lower than the
desired torque to prevent depleting the batteries and damaging
the battery pack.
The first step in this process is fuzzification.
is a member of both MSF’s representing “positive” and “high
positive,” yet the degree of membership is different. A torque
value of 0.875 is considered to be 25% “positive” and 75% “high
positive.” The value can also be defined as the certainty. In
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
63
Fig. 5. Simple FLC system with two inputs and one output.
other words, one is 75% certain that the torque value of 0.875
0.50 is a member of only one MSF,
is high positive. SOC
An SOC of 0.5 is considered to be 100% “medium.”
Because of how the MSF’s have been chosen, the degrees of
membership for each input must add up to 100%. However, it
is possible to define MSF’s of qualitatively and quantitatively
different shapes.
The next step is the inference process. Each rule is checked
for the degrees of membership for the participating inputs. Here,
is high positive (of which one is 75% cerrule 1 says that if
tain) and the SOC is medium (of which one is 100% certain), the
has to be positive. In order to determine the degree
output
is a member of the MSF “positive,”
or certainty with which
the minimum method is applied. That means the minimum of the
values of the inputs is going to be the value for the output.
This is defined mathematically as
to 0.25. The larger triangle on the right has the same values
as its corresponding (
positive), but its height has been
reduced to 0.75.
To get a crisp output value for use by the controller from the
two output triangles of Fig. 6, defuzzification is performed using
the center of gravity (COG) method. This method computes the
crisp control output based on the area under the fuzzy sets. The
location of the COG of the resulting triangles reveals the value
.
of the output, which, in this case, happens to be
Mathematically, this can be described to be
(6)
where
SOC
(5)
where
MSF of
;
MSF of
;
(SOC) MSF’s of
and SOC.
For rule 1 in this example, the output certainty
would be
which is 0.75. Applying the minimum
method to rule 2, we obtain a
for the output MSF neutral to be 0.25. For rule 1, this means
that there is a 75% certainty that the output will be part of the
MSF. For rule 2, there is a 25% certainty that the
positive
MSF. Once the certainty
output will be part of the neutral
for each premise or rule has been determined, the consequent
(or the implied fuzzy set) for each rule can be found. It is
is positive and
found from taking the MSF of the premise (
is neutral, respectively) and multiplying by the certainty
to quantify the “then” (or the consequent) operation. This is
called the product function. This leads to the two triangles at
the bottom of Fig. 6. Notice that the consequent triangle on
(
the left has the same values as its corresponding
neutral). The only difference is that the height has been reduced
area of the MSF ;
center (or the location of the apex) of the triangular
MSF;
number of rules;
crisp control output.
V. SYSTEM INTEGRATION AND CONTROLLER DEVELOPMENT
IMPLEMENTATION
A. System Integration
This section discusses the integration of the EM, which was
configured to operate as a motor or a generator, the ICE, and
the controller. The performance objective lies in the development of a control system that will allow the ICE to operate at
or near its peak point of efficiency or at or near its best fuel
line by employing the appropriate hardware, operation strategy,
and control devices. The design objectives are to integrate these
components “seamlessly.” In other words, the driver should be
minimally aware that these components are operating within the
vehicle. The following will discuss the development of a fuzzy
controller for the implementation of a load-leveling strategy
with a parallel HEV power train. The vehicle of interest is a
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Fig. 6. Fuzzification, inference, and defuzzification procedures for the presented example (system with two inputs, one output, and two rules).
modified 1997 Chevrolet Lumina. The power train includes the
following:
• 1.9-L four-cylinder turbo-charged compression-ignition
direct-injection (CIDI) ICE, with maximum power output
of 66 kW and a peak fuel efficiency of 43% (this ICE was
chosen due to its high efficiency);
• permanent-magnet (PM) brushless dc EM with power
rating of 10 kW continuous and 20 kW peak;
• mechanical coupling between the EM and the crankshaft
of the ICE;
• manual five-speed transmission.
• fuel tank which can store up to 7 gal Biodiesel (B20).
• battery pack which has a nominal voltage rating of 312 V
and an energy content of about 1.56 kWh.
Fig. 3 shows a schematic drawing of the power train. The individual controllers for the ICE and the EM have been unmodified.
The weight of the modified vehicle is 3371 lbs.
The given system was designed to permit a hierarchical
control structure. This means that the intelligent controller
sends signals to the original equipment manufacturer (OEM)
controllers of the EM and the ICE. The OEM controllers then
send a signal to their individual components. When working
with highly complex systems such as an HEV, it is important
to simplify the man–machine interface as much as possible.
Therefore, the upcoming considerations neglect lateral and
vertical vehicle dynamics, which is a reasonable assumption
given the fuel economy focus of the study. As far as the
longitudinal vehicle dynamics are concerned, the role of the
driver within the overall system can be described as shown in
Fig. 7(a). One goal of this controller development was to avoid
additional control actions by the driver. The application of this
principle to the specific power train results in a system like the
one shown in Fig. 7(b). The signals of interest are as follows:
, the desired vehicle velocity as given by speed
limits or decisions made by the driver;
• , the actual longitudinal vehicle velocity;
, the difference between the desired speed and the ac•
tual speed of the vehicle;
• , a signal proportional to the accelerator pedal angle;
• , a signal proportional to the brake pedal angle;
• #GR, the selected gear (in our case, an integer number
between 1–5);
• SOC of the battery pack, which is a signal that indicates
the ability of the battery pack to deliver or to store electric
energy;
, an accelerator or fuel command as seen by the en•
gine control unit;
, a torque command signal sent to the controller of the
•
EM.
In a vehicle with a conventional power train, the driver
chooses accelerator and brake pedal angles based on the dif. The driver also
ference between desired and actual speed,
selects a gear to coordinate engine speed and vehicle speed. In
an HEV, two accelerator pedals would be necessary in order to
control the operating points of both ICE and EM. In this study,
the fuzzy controller, which is part of the supervisory vehicle
control unit (VCU), gives the command signals to the EM
and the ICE. This means that the driver can operate just one
input, as he/she is accustomed to doing. The VCU, representing
the power-train management, splits the driver input into two
) and
signals, one going to the control unit of the ICE (
). Because of this
the other to the controller of the EM (
modification, it is possible to combine both the accelerator
pedal input and brake pedal input together to one single input,
, which expresses the driver’s desire to accelerate, decelerate,
or maintain the vehicle speed. The brake signal is also required
to control brake energy recuperation by using the EM as a
•
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
65
(a)
(b)
Fig. 7. Driver–vehicle feedback control systems.
Fig. 8.
VCU with inputs and outputs in the overall system.
generator. Provided with this information, the VCU fits into the
overall system as shown in Fig. 8.
Fig. 8 shows the final state of development. It includes a
total of three estimators. The ICE optimum estimator determines
the optimal operating point of the ICE, given by engine speed
(which translates into the actual vehicle speed) and the engine
. This value is determined based on the
torque output
control strategy chosen. In order to assure the necessary engine speed, the proper gear ratio must be chosen. In this system,
the driver is given the proper gear selection #GR by an audible
signal. Future studies will showcase an automated transmission
for this purpose. The optimum estimator is based on the efficiency map or the fuel use map of the ICE, or dominant component as determined by the DOH, and is stored in a lookup table
in the computer.
The road load estimator determines the actual load of the vehicle as seen by the power train. It has to be provided with vehicle information such as frontal area of the vehicle, drag coeffi-
cient, coefficient of rolling resistance, wheel radius, gear ratios,
and total vehicle mass. It employs the standard power equations
as used in vehicle dynamics. At a given speed, the power equathat can be computed according
tion relates to load torque
to
(7)
where
torque caused by the rolling resistance;
torque caused by the aerodynamic drag;
torque caused by the acceleration of the vehicle;
torque caused by the vehicle going uphill or downhill;
torque caused by a tow load [6].
The difference between the torque which is necessary to overcome the road load and the desired torque output of the ICE (a
value determined by the efficiency map discussed earlier) is the
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
required torque contribution from the EM. This can be positive
(motor) or negative (generator).
Finally, the SOC of the battery pack has to be considered in
order to decide whether the required torque contribution of the
EM is possible or not. If the batteries are completely charged,
the EM cannot be allowed to operate as a generator. If they are
totally discharged, a positive torque contribution will not be possible. An SOC estimator is, therefore, of special interest for this
system. It is difficult, if not impossible, to monitor the SOC of
every individual cell of the battery pack. Therefore, the Peukert
equation (8) and measurements of battery pack voltage and current flowing between the EM and the battery pack are used to
determine an estimate of the SOC [7]
rules, a fuzzy logic controller with three inputs (
, and
and
), and a total of 847 rules will
SOC), two outputs (
is the desired or optimal
be used. In this particular setup
EM torque as calculated from (10). The controller employs 11
) and
triangle-shaped MSF’s for the first two inputs ( ,
seven MSF’s for the SOC. In order to facilitate computer imple, SOC) are
mentation, the MSF’s of the first two inputs (
numbered from 1 to 11 (where 1 represents negative large and
11 represents positive large). The accelerator command input
( ) MSF is numbered from 1 to 7 (where 1 represents negative
large and 7 represents positive large). These are shown in Fig. 9.
In this instance, 1 corresponds to the application of full braking
and 7 corresponds to a fully depressed accelerator pedal with no
braking. The outputs use 11 triangular MSF’s each. These are
. The minnumbered in a manner similar to SOC and
imum method is used to determine each individual rule’s contribution and the center of gravity method is employed for defuzzification.
(8)
is the Peukert capacity and is the Peukert constant. Both
values can be derived by curve fitting experimental data from the
battery pack being used in the vehicle. Knowing the current flow
and the time during which it occurs, it is possible to determine
the capacity of the battery pack. Using the battery voltage and
current, the relative change in capacity of the battery pack is
also computed. Using information from the estimators and the
procedure discussed in Section III, the operating point of the
to
ICE is commanded by adding or subtracting an amount
the driver’s accelerator pedal input (9). The torque command
sent to the EM is then computed according to (10)
(9)
(10)
Equations (9) and (10) describe the basic control laws of the
VCU. One can explain the operation of the control law for the
efficiency mode using the following scenario. Suppose that the
) is given
torque and speed needed overcome the road load (
by operation point II in Fig. 4. In this case, the engine speed
is too low and the torque is too high for best efficiency of the
ICE. To remedy this situation, the gear ratio to the ICE must be
increased (which means downshifting in a manual transmission)
and the accelerator command must be decreased by an amount
. The actual accelerator command
that goes to the ICE
is determined by the control law in (9). It becomes immediately
apparent that once this control move is exercised on the ICE,
there will not be enough torque to overcome the road load. The
EM must, therefore, be adjusted to meet the deficit. In order to
overcome the road load and maintain a constant overall powertrain power output, the EM must be operated as a motor. The
) that will be sent to the
value of the required EM torque (
EM controller is given by the control law in (10).
At first glance, (9) and (10) seem to be basic linear equations. The problem is that, in actual implementation, some of
) are nonlinear. In addithe terms in the road load torque (
tion, the SOC of the battery pack (another nonlinear equation)
must be utilized because of the fact that the battery pack is the
energy source needed for the EM. Because of the event-based
nature of the vehicle operation strategies, successful implementation of the control laws in (9) and (10) hinge on the application
of a rule-based controller. In order to efficiently implement the
B. Fuzzy Controller Implementation
The fuzzy controller works by using the values of the three
, and SOC) and incorporating the expert’s
inputs ( ,
knowledge of the system to calculate the change in accelerator
, and the actual EM torque,
. The
command to the ICE,
first input is ( ), which determines if the vehicle is accelerating,
) which is
decelerating, or neither. The second input is (
the optimal desired EM torque. This value is calculated from
the control strategy and (10). The third input is the SOC, which
is calculated from (8) and battery measurements. The controller
uses these inputs and its rule base to calculate the change in ac, and the required torque of
celerator command to the ICE,
.
the EM,
The controller was designed to be a general purpose fuzzy
logic controller because the same controller can work with multiple control strategies. This is accomplished because the requested control moves are actually defined by (9) and (10) and
the particular strategy being used. For example, let us assume
that, to overcome a particular load torque, the ICE is required to
operate at point IV in Fig. 4. Since point IV is not very efficient,
one would like to shift it as close to the peak efficiency point as
possible. This will be done by decreasing the engine speed (upshifting) and decreasing the accelerator command (which dedecreases torque). Decreasing the accelerator command
defined by (9). This command
fines the engine command
is incorporated in the input to the FLC. We have now shifted
point IV closer to the peak efficiency point. Because of this shift,
we are now not meeting the load torque requirement with the
ICE. The deficiency is compensated for by the torque generated
from the EM and is calculated by (10). This is the second input
). The third input is the SOC of the battery pack. Since
(
the control objective is met in (9) and (10), the fuzzy logic controller can be used based on the individual values themselves. A
subset of typical rules for the fuzzy logic controller is given by
the following list.
is positive large and the
1) If is positive large and
is zero and
SOC is positive medium large, then
is positive large.
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
Fig. 9.
67
Fuzzy logic controller inputs.
2) If is positive medium and
is negative low and
is zero and
is
the SOC is positive large, then
zero.
3) If is positive medium and SOC is positive large and
is negative low, then
is zero and
is zero.
4) If is negative large and SOC is positive large and
is negative low, then
is zero and
is zero.
5) If is positive low and SOC is positive medium and
is positive low, then
is positive low and
is negative low.
In rule 1, the driver wants full acceleration and there is sufficient SOC in the battery, thus the EM will operate as a motor.
that is requested is zero. This is because the
Notice that
driver is already putting the pedal to the floor in his/her request
for full acceleration. There is no need to change his/her request.
based on (10).
This optimum estimator gives the value of
) in (10) is calculated using the various
The load torque (
loads that the automobile encounters as explained in Section
) is calV-A. The torque and speed needed from the ICE (
culated using one of the control strategies described in Section
V-A. This torque may be more or less than what the vehicle
needs to overcome the load torque. The difference between the
and
is compensated for by the EM torque
. Rule
2 is explained by the following. If the driver wants moderate acceleration and the desired EM torque is slightly negative and the
SOC of the battery pack is at its highest allowable SOC, then do
not increase the accelerator command and do not apply a negative torque with the EM. Rule 3 is explained by the following.
If the driver is braking heavily and the SOC is below its highest
allowable limit, then apply as much negative torque as the EM
can supply and do not adjust the accelerator command. This is
the regenerative braking scenario in which the application of the
brake is used in charging the batteries. Rule 4 is explained by the
following. If the driver is braking heavily and the SOC is at its
highest allowable limit, then apply no negative torque with the
EM and do not adjust the accelerator command. This keeps one
from overcharging the batteries. Rule 5 is explained by the following. If slight acceleration is being requested and the desired
EM torque is negative and the battery SOC is below its maximum limit, then add a positive change to the accelerator and
supply negative torque with the EM. In this final case, we add
extra torque to the ICE and use that extra torque to charge the
EM’s batteries. These particular rules give an idea of how the
FLC will respond to various scenarios.
As one can see by Fig. 10, this control scheme forces the
majority of operating points to be in the vicinity of the highest
point of efficiency. When compared to a parallel HEV with a
speed-control-based control scheme that utilizes lookup tables
[13], the average efficiency is increased from 30% to 35.4%.
The same rule base can be used with the best fuel consumption strategy. In this strategy, one forces the ICE to operate at or
below a certain fuel use line (Fig. 11). One must remember that
, based on
(10) gives the desired value of the EM torque,
. In general, the best fuel economy for
the load torque and
this system is found at a lower torque and a lower engine speed
than the best point of efficiency. From an intuitive point of view,
this means that better fuel economy will be attained by having
, from the ICE. As one can
smaller accelerator commands,
see in Fig. 12, the best fuel use control scheme forces the majority of operating points to be in the vicinity of the point of the
best fuel economy. When compared to the parallel HEV with a
speed-control-based control scheme that utilizes lookup tables
68
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Fig. 10.
Efficiency map for HEV using the efficiency strategy.
Fig. 11.
Fuel-use mode map for ICE.
[12], the average fuel economy was increased from 48 to 55.7
mi/gal. The comparisons were gathered through the use of an
experimentally verified hybrid simulator developed by the National Renewable Energy Laboratory (NREL) [12].
The rule base is the result of examining the results of the
movement of operating points of an ICE and from basic
knowledge of hybrid engine operation [1]. There are two
modes of operation, an efficiency mode and a fuel use mode.
In the efficiency case, the controller aims to maximize the
overall vehicle’s efficiency by optimizing the efficiency of the
dominant power source, the ICE. For the fuel-use case, the
controller strives to minimize the overall vehicle’s fuel use by
optimizing the fuel economy of the dominant power source. In
case the driver demands additional power during acceleration,
however, the system aims to maximize the overall drivetrain’s
power output. Upon examining the control scheme, it becomes
evident that the best that one can do in either case is limited by
slightly better than the optimal operation of the dominant power
source. This means that a more efficient dominant source, the
ICE in this case, is paramount to good design.
The controller was physically realized using a PC to download instructions to a digital signal processor (DSP)-based microcontroller. The microcontroller sent signals to the EM controller and the engine control unit.
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
Fig. 12.
69
Efficiency map for HEV using the fuel-use strategy.
VI. DISCUSSION
Fig. 13 shows real data from a slow 0–65-mi/h acceleration
acquired during normal vehicle operation. This slow cycle is
used to allow the reader to visualize what is happening in the
vehicle. The actual 0 to–60-mi/h vehicle acceleration is 12.7 s.
This artificial driving cycle, which could be interpreted as entering a highway, can be divided in four parts: idle, acceleration,
cruise, and coast down. At the beginning, the vehicle starts with
a fairly discharged battery which can be seen from the battery
voltage (approximately 300 V). Since the following acceleration is quite slow, the controller gives priority to charge the battery after it tries to support the ICE in accelerating the vehicle
for a short moment. Charging is taking place, as can be seen
from the positive torque command sent to the EM controller
(commanding a negative torque contribution from the EM) and
the sharp increase in battery voltage. The spikes in the battery
voltage are caused by the back electromagnetic force from the
EM. After about 30 s, the controller detects a continuous acceleration. To support this effort, it uses the electric energy previously generated and runs the EM as a motor. However, the previous charging efforts have not charged the batteries enough, so
s, the conthat it reduces the support of the ICE. At
troller detects that the batteries are not charged enough to continue the motor operation. The VCU now commands again a
negative torque contribution of the EM to charge the batteries
and to prevent them from being damaged. This is possible, since
the acceleration continues on a small level.
Remember that the overall acceleration from 0 to 65 mi/h
takes almost 2 min. This slow rate of acceleration was used
in order to represent the full range of controller operation on
s, using the fact that slow coast down
one graph. At
is needed, the controller decides that the batteries have been
charged enough to deliver a small amount of energy to power
the vehicle during cruising and coast down. At the end of the
cycle shown in Fig. 13, the battery voltage is approximately on
the starting level. This shows that the SOC has been maintained.
Comparing the two outputs of the controller, accelerator
change
and EM torque command, one can clearly see
that a charging command goes along with an increase in the
accelerator signal as seen by the ECU. The mean value of the
accelerator change over the 150 s shown in this example is
positive, which can be explained by the performance mode
which is overlaid to the efficiency mode.
VII. CONCLUSIONS AND FUTURE WORK
This paper has demonstrated the suitability of fuzzy control
techniques for the power-train management of an HV. The use
of intelligent control is justified because HEV drivetrains represent highly nonlinear multidomain systems and are, therefore,
excellent examples of mechatronic systems. The DOH concept
described in this paper allowed us to employ a control strategy
directly on the 66-kW ICE. This is because the benefit of employing a control strategy on the 20-kW electric motor produces relatively small results. A particular advantage for automotive applications is the possibility to allow the implementation of available linguistic knowledge and the inclusion of
available components that already have highly developed controllers. Shorter development cycles for automotive systems will
demand the type of supervisory control presented in this paper.
Achieving reasonable results without exploiting cumbersome
simulation methods and using control algorithms that are not
computationally intensive make this approach highly desirable.
Future work in this field will include adaptive fuzzy controllers and combinations of fuzzy control and artificial neural
networks [8], [9]. In addition, control schemes that seek to optimize as many sources as possible will employed. In addition,
combining the efficiency and fuel economy strategies into one
comprehensive strategy is a future goal. Research and development of these technologies must go along with the continuous
70
Fig. 13.
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000
Typical vehicle operation.
BAUMANN et al.: MECHATRONIC DESIGN AND CONTROL OF HEV’S
collaboration between automotive engineers and control engineers. The understanding of intelligent control techniques and
their application to achieve the goals of future developments of
automobiles fulfilling both economical and ecological restrictions has to be supported by the proper education of future engineers. The practical ends of intelligent control combined with
a deep understanding of mechatronics will be more and more
important for future vehicle engineering.
REFERENCES
[1] B. M. Baumann, “Intelligent control strategies for hybrid vehicles using
neural networks and fuzzy logic,” Master’s thesis, Dep. Mech. Eng., The
Ohio State Univ., Columbus, 1997.
[2] J. M. Mendel, “Fuzzy logic systems for engineering,” Proc. IEEE, vol.
83, pp. 345–377, Mar. 1995.
[3] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice-Hall, 1997.
[4] C.-T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall,
1995.
[5] Y. F. Li and C. C. Lau, “Development of fuzzy algorithms for servo
systems,” IEEE Contr. Syst. Mag., vol. 9, pp. 65–71, Apr. 1989.
[6] T. D. Gillespie, Fundamentals of Vehicle Dynamics. Warrendale, PA:
Society of Automotive Engineers, 1992.
[7] T. R. Compton, Battery Reference Book. Warrendale, PA: Society of
Automotive Engineers, 1996.
[8] J. M. Zurada, Introduction to Artificial Neural Systems. New York:
West, 1992.
[9] S. Haykin, Neural Networks: A Comprehensive Foundation. New
York: Macmillan, 1994.
[10] J. B. Heywood, Internal Combustion Engine Fundamentals. New
York: McGraw-Hill, 1988.
[11] B. Bates, “On the road with a Ford HEV,” IEEE Spectrum, vol. 32, pp.
16–21, July 1995.
[12] M. R. Cuddy and K. B. Wipke, “Analysis of the fuel economy benefit
of drivetrain hybridization,” SAE, Technical Paper Series, no. 970289,
1997.
[13] K. B. Wipke, M. R. Cuddy, and S. D. Burch, “ADVISOR 2.1: A
user-friendly advanced powertrain simulation using a combined
backward/forward approach,” IEEE Trans. Veh. Technol., vol. 48, pp.
1751–1761, Nov. 1999.
[14] J. R. Brumby et al., “Computer modeling of the automotive energy requirements for internal combustion engine and battery powered vehicles,” IEEE Trans. Veh. Technol., vol. 33, pp. 849–853, Sept. 1992.
[15] B. Wasacz, N. Janes, Y. Guezennec, and G. Rizzoni, “The 1996 Ohio
State University future car,” SAE, Technical Paper Series, no. SP-1234,
1997.
[16] M. J. Riezenman, “Electric vehicles,” IEEE Spectrum, vol. 29, pp.
18–21, Nov. 1992.
[17] M.-C. Chang, “Computer simulation of an advanced hybrid electricpowered vehicle,” SAE, Technical Paper Series, no. 780217, 1978.
[18] K. L. Butler, K. M. Stevens, and M. Ehsani, “A versatile computer simulation tool for design and analysis of electric and hybrid drive trains,”
SAE, Technical Paper Series, no. 970199, 1997.
[19] F. Schluter and P. Waltermann, “Hierarchical control structures for hybrid vehicles—Modeling, simulation, and optimization. Advances in automotive control,” in Proc. 1st Int. Federation of Automatic Control
Workshop, Mar. 1995, pp. 115–120.
[20] C. G. Hochgraf, M. J. Ryan, and H. L. Wiegman, “Engine control
strategy for a series hybrid electric vehicle incorporating load leveling
and computer controlled energy management,” SAE J. SAE/SP-96/1156,
pp. 11–24.
71
[21] L. Guzzella, A. Amstutz, and F. Grob, “Optimal operation strategies for
hybrid powertrains,” in Proc. 1st Int. Federation of Automatic Control
Workshop, Mar. 1995, pp. 93–98.
[22] T.C. Moore, “Tools and strategies for hybrid-electric drivesystem optimization,” SAE, Technical Paper Series, no. SP-1189, 1996.
[23] C. W. Ellers, “Near term improvement of passenger car drive train efficiency,” SAE, Technical Paper Series, no. 961661, 1996.
[24] M. Ross and W. Wei, “Fuel economy analysis for a hybrid concept car
based on buffered fuel-engine operating at an optimum point,” SAE,
Technical Paper Series, no. 950958, 1995.
[25] B. K. Powell, K. E. Bailey, and S. R. Cihanek, “Dynamic modeling and
control of hybrid electric vehicle powertrain systems,” IEEE Contr. Syst.
Mag., vol. 18, pp. 17–33, Oct. 1998.
Bernd M. Baumann received the M.S. degree
from The Ohio State University, Columbus, and
the Dipl.-Ing. degree from Dresden University of
Technology, Dresden, Germany, in 1997 and 1998,
respectively, both in mechanical engineering.
He was a Graduate Research Assistant with the
Center for Automotive Research (CAR), The Ohio
State University, during 1996–1997 and actively involved in both The Ohio State University FutureCar
Challenge Team and The Ohio State University Formula Lightning Electric Race Car Team. In 1998, he
joined the Advanced Propulsion Systems Laboratory, Research and Technology,
DaimlerChrysler AG, Stuttgart-Untertürkheim, Germany, where he is currently
Project Manager and Chief Engineer of an HEV project. His research interests
involve design and control of advanced automotive power trains. He authored
or coauthored several technical papers on the control of advanced propulsion
systems.
Mr. Baumann was a Scholar of the German National Scholarship Foundation and the former Daimler-Benz Scholarship Program for Research and Technology. He is a member of the Society of Automotive Engineering and the
German Engineering Society (VDI).
Gregory Washington received the B.S. (magna cum
laude), M.S., and Ph.D. degrees in mechanical and
aerospace engineering from North Carolina State
University, Raleigh, NC, in 1989, 1991, and 1994,
respectively.
In 1995, he joined the Department of Mechanical
Engineering, The Ohio State University, Columbus,
where he is currently an Assistant Professor. His
areas of interest include modeling and control of
smart materials, intelligent control, and modeling
and control of mechatronic systems. He has authored
more than 30 published journal and conference papers related to smart materials
and the control of mechatronic systems.
Dr. Washington was the recipient of a National Science Foundation CAREER
Award in 1997.
Bradley C. Glenn received the B.S. and M.S.
degrees in mechanical engineering in 1997 and
1999, respectively, from The Ohio State University,
Columbus, where he is currently working toward
the Ph.D. degree in mechanical engineering with
emphasis in control theory.
He is also currently a Graduate Research Assistant
at The Ohio State University and is actively involved
in The Ohio State University FutureCar Challenge
Team.
72
Giorgio Rizzoni (S’83–M’85) received the B.S.,
M.S., and Ph.D. degrees in electrical engineering
from the University of Michigan, Ann Arbor, in
1980, 1982, and 1986, respectively.
Between 1986 and 1990, he served as the Assistant
Director of the Vehicular Electronics Laboratory
and Assistant Research Scientist and Lecturer with
the Electrical Engineering and Computer Science
Department, University of Michigan. He has been
a member of the faculty of the Department of
Mechanical Engineering, The Ohio State University,
Columbus, since 1990. He is the Director of the Powertrain Control and
Diagnostics Laboratory and of the DOE Graduate Automotive Technology
Education (GATE) Center on Hybrid Drivetrains and Controls, both affiliated
with The Ohio State University Center for Automotive Research. His research
activities focus on internal combustion engine modeling, control, and diagnostics and on hybrid and alternative powertrain concepts. He has contributed to
the development of a graduate course sequence entitled “Powertrain Modeling
and Control,” taught at The Ohio State University and at General Motors
through the GM Technical Education Program. He has published more than
100 technical papers in international publications. He is the author of two
textbooks and of the chapter “Electrical Engineering” in the CRC Handbook
of Mechanical Engineering (Boca Raton, FL: CRC Press, 1998). He is a past
Associate Editor of the ASME Journal of Dynamic Systems, Measurement, and
Control. He served as General Chair of the 1998 IFAC Symposium, “Advances
in Automotive Control.”
Dr. Rizzoni is a past Associate Editor of the IEEE TRANSACTIONS ON
VEHICULAR TECHNOLOGY. He has served as Guest Editor for the IEEE
TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY and IEEE Control
Systems Magazine. He is a member of the American Society of Mechanical
Engineers and the Society of Automotive Engineers (SAE). He was the recipient of the 1991 National Science Foundation Presidential Young Investigator
Award, the 1992 SAE Ralph R. Teetor Educational Award, and various other
teaching and technical awards.
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 5, NO. 1, MARCH 2000