Energy management strategy for a PHEV
Content
Energy Management Strategy for a PHEV
Vignesh Hegde Narashima, Anand Gali, Naresh Kumar Suresh Kumar
TU Kaiserslautern
Abstract—In this paper, a energy management controller is
developed for hybrid vehicles with parallel configuration. Using
the torque required at the gearbox, the state of charge(SOC)
of the battery, the speed and torques of the Electric machine
and the Internal combustion(IC) engines, a set of rules have
been developed, in a controller to effectively determine the split
between the different power plants powering the vehicle. The
underlying theme of this rule based logic is to optimize the
working of the components whilst maintaining a full charge
at the end of the driving cycle. Simulation results in overall
consumption and final battery charge which is used to assess the
performance of this energy management controller. The results
show a potential to improve the fuel economy and to maximize
the efficiency of the vehicle in general.
Keywords: Control strategies, Energy management, Fuel economy, Rule based management, Logic based, Hybrid vehicles,
PHEV, optimization.
I. INTRODUCTION
Hydrocarbon based fuels are the only sources of energy, used by conventional vehicles worldwide. Being nonrenewable and also a means of pollution, it is very important
to consider its impact on the environment. To offset the
degradation, large amounts of research are being carried out
to search for alternative sources to power the vehicle. The
search for improved fuel economy and reduced emission
without sacrificing vehicle performance has made the hybrid
technology one of the most promising and viable solutions out
in the market.
A. Hybrid Electric Vehicles
Hybrid Electric Vehicle ( HEV ) is a vehicle which for the
purpose of mechanical propulsion, draws energy from both of
the following vehicle sources of stored energy/power.
• a consumable fuel,
• an electrical energy/power storage device (e.g. battery,
capacitor, flywheel/generator, etc.).
In the last two decades, the Automotive industry has increasingly developed vehicles and concepts with these hybrid powertrains. Although there have been of different configurations,
all of the hybrid vehicles can be broadly classified in three
types,
1) Series Hybrid: Series HEVs is driven by an electric
motor, functioning as an electric vehicle while the
battery pack energy supply is sufficient, with an engine
tuned for running as a generator when the battery
pack is insufficient. There is no mechanical connection
between the engine and the wheels, and the purpose
of the range extender is to charge the battery. Opel
Ampera and BMW i3 are examples of series hybrids.
2) Parallel Hybrid: InParallel HEVs the IC Engine
and the Electric motor are both connected to the
mechanical transmission and can either individually
or simultaneously transmit power to drive the wheels,
usually through a conventional transmission. Honda’s
Integrated Motor Assist (IMA) system as found in
the Insight, Porsche Panamera S E-Hybrid Civic, and
Accord are examples of parallel hybrids.
3) Series Parallel Combined Hybrid Systems: Power-split
hybrids have the benefits of a combination of series
and parallel characteristics. As a result, they are more
efficient overall, because series hybrids tend to be more
efficient at lower speeds and parallel tend to be more
efficient at high speeds like Toyota Prius.
It is clearly visible, that the hybrid vehicles were made
just to achieve a goal, ’A better Fuel economy without
compromising on the consumer expectations with respect to
performance and other economic/comfort constraints’.
In order to achieve this, it is very important to optimize the
working of the components in the hybrid powertrain system.
This is not only possible through better efficient engines,
motors and generators but also with a very efficient Energy
management strategy. This Energy management strategy is
implemented using a energy management Controller, a similar
is which designed and presented here. The energy management
controller is expected to optimize the operation of all the
critical components in the PHEV: the IC Engine, EM( Electric
Machine) and the battery. For the implementation of the
strategy using the controller, a Rule based and logic based
approach is used. It has been selected as it provides an optimal
tradeoff between complexity and also effectiveness.
B. QSS Toolbox
The examples and the simulation calculation done in the
following paper has been done using QSS Toolbox. The
QSS Toolbox is a collection of Matlab/Simulink blocks and
the appropriate parameter files that can be run in any Matlab/Simulink environment. It allows for interconnection between relevant powertrain elements, scalability and integration
of elements with possibility to visualize and numerically
optimize them. Complex powertrains can be built using basic
blocks as shown in Figure 1.
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
Figure 1.
QSS Toolbox- Overview
C. Driving Cycle
Driving Cycle is a series of data points representing the
speed of a vehicle versus time. Driving cycles are produced by
different countries and organizations to assess the performance
of vehicles in various ways, as for example fuel consumption
and polluting emissions. In the paper two of such driving
cycles are considered for the design and implementation.
• NEDC: The New European Driving Cycle (NEDC) is a
driving cycle, last updated in 1997, designed to assess
the emission levels of car engines and fuel economy
in passenger cars (which excludes light trucks and
commercial vehicles).
•
FTP-75: The EPA Federal Test Procedure, commonly
known as FTP-75 for the city driving cycle, are a series
of tests defined by the US Environmental Protection
Agency (EPA) to measure tailpipe emissions and fuel
economy of passenger cars (excluding light trucks and
heavy-duty vehicles).
II. PARALLEL H YBRID E LECTRIC V EHICLE
Parallel Hybrid Electric Vehicles, which are most
commonly produced at present, have both an internal
combustion engine and an electric motor coupled
together. When only one of the two sources is being
used, the other must also rotate in an idling manner,
which would be connected by a one-way clutch, or
freewheel. The two sources may be applied to the same
shaft joined at an axis in parallel with the electric motor
lying between the engine and transmission.
And therefore speeds at this axis must be identical
and the supplied torques adds together with the electric
motor adding or subtracting torque to the system as
necessary.Parallel hybrids can be further categorized
depending upon how balanced the IC engine and the
Electric Motor are at providing the motive power. In
some cases, the combustion engine is dominant when
the electric motor turns on only when a boost is needed.
Whereas in other case, the vehicle can run with just the
electric system operating. But because current parallel
hybrids are unable to provide all-electric (ICE=OFF)
propulsion, they are often categorized as mild hybrids.
A. PHEV Configuration
The specific PHEV configuration used throughout the
paper, consists of the following components.
– Mercedes-Benz A 170 CDI (W168).
– Electric motor (12 kW / 60 Nm)
– Battery (16.38 kW / 0.468 kWh / 48 V)
– FTP 75 and NEDC as the test cycles to be optimized
on
III. ENERGY MANAGEMENT STRATEGY
Before we start with the description of the energy management strategy, which is the working design behind the
controller, the energy in the system is to be managed taking
the following conditions into concern.
1) The driver requirements according to the driving cycle
are satisfied consistently (the driver requirements being
the required torque at the gearbox, angular velocity and
anglular acceleration at the gearbox).
2
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
2) The battery satisfies Charge sustainment. Requirement
being that the battery charge at the end of the drive
cycle to be the same or equivalent to the quantity with
which we started, ensuring charge sustainability.
3) The overall powerplant system (IC Engine and EM) is
optimized for its efficiency.
While the PHEV is being operated, the controller should
determine how much power is to be supplied at the wheels
and how is it being distributed by the synergistic combination
of the IC Engine and the EM. The task is not only to ensure
the required torque at the gearbox is being provided but also
to determine how much of the same is needed to charge the
battery such that charge sustainment at the battery is ensured.
This involves optimal distribution of the power between IC
Engine and EM. If the battery is to be charged, it is performed
either via Regeneration during braking or through negative
torque supplied to the EM from the IC Engine. It is to be noted
that during this case the latter should provide power for both
driving the wheels and charging the battery. The power split
strategy is used to optimize the efficiency of the IC Engine
and Electric motor. To ensure optimal operating points it is
necessary to analyze the efficiency maps of the components
in use.
Regeneration Mode
Figure 4. Load Point Shifting Mode
B. Controller Basics
A. Power Split Strategy
This Power split energy management strategy is based on
the status of charge, the power demand. The strategy uses
a controller which essentially is a switch to select between
various operating modes depending upon the SOC value.
According to the strategy apart from the IC engine driven
mode, the other possible paths in which power can flow is
shown in the figures 2. 3. and 4. respectively
Figure 2.
Figure 3.
Electric Driving Mode
The above mentioned paths shall be further referred to as
Electric driving, IC driving, Regeneration, Load Point Shifting
1 (IC + motor mode; LPS1) and Load Point Shifting 2 (IC+
generator mode; LPS2) all throughout this paper. For a good
energy management strategy, the required torque demand must
be satisfied whilst ensuring the IC Engine is being operated at
the most efficient point for the required torque demand. Thus
the controller must decide how the IC Engine is to be utilized
in tandem with the EM to ensure a lower fuel consumption
compared to a conventional vehicle.
The general rule based logic of the controller is explained
in this section. The controller divides the operating conditions
of the PHEV into 4 basic zones depending upon the value of
the SOC the zones are named as
1) Excess zone ( SOC > 0.98)
2) Normal zone ( 0.7 < SOC < 0.98 )
3) Low zone ( 0.6 < SOC < 0.7 )
4) Very Low zone ( SOC < 0.6 )
These operating zones were determined on analyzing the
recharge potential of the battery through regeneration mode.
When the SOC went southward from 0.6 the tendency of the
charge being closer to the start was decreasing, i.e. it was
not possible to charge back without compromising on the efficiency. Each zone has its own sub strategies for implementing
the power split, the onus of the strategy being to get the SOC
as close as possible to the zone above it. For example at SOC
= 0.63, the strategy aims to bring the SOC closer to 0.7 as fast
as possible, thus ensuring a full charge at the end of the cycle.
This is realised ensuring the Torque provided by the EM as
both motor and generator (not simultaneously) are capped and
is proportional to the state of charge. This involves disabling
one or more operating modes depending upon the operating
zones. Based on this idea, LPS2 mode is disabled in Excess
zone and ED mode is disabled in Very low zone. Similarly
ED is permitted till 30 Nm, whilst in Normal and Low it is
permissible till 28Nm, 20Nm respectively.
C. Controller Overview
The controller overview is illustrated using a simple table
in Table below
3
Seminar Electromobility SS 2015
Mode/Zone
ED
LPS1
LPS2
IC
Regeneration
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
Excess
0<T<40
T>=150
Disabled
40<=T<150
T<0
Normal
0<T<30
T>=120
30<=T<=100
100<=T<150
T<0
Low
0<T<25
T>=170
25<=T<=100
100<=T<150
T<0
VeryLow
Disabled
Disabled
0<=T<80
T>=80
T<0
The implementation of the controller block in Simulink along with the operating zones are shown in Figure 5 and 6
respectively.
Figure 5.
Controller Block - Overview
Figure 6. Operating Zones - Illustration
4
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
IV. RESULTS
The results for the controller is illustrated in this section.
A. Result -FTP 75 Cycle
B. Result -NEDC Cycle
Results for FTP-75 test Cycle is shown below
Results for NEDC test Cycle is shown below
T_MGB_FTP-75
T_MGB_NEDC
150
Torque (Nm)
Torque (Nm)
150
100
50
0
-50
-100
-150
0
200
400
600
800
1000
1200
1400
1600
1800
Time (seconds)
Figure 7.
100
50
0
-50
-100
-150
0
200
400
Torque Requirement at Gearbox
Figure 11.
800
1000
1200
1000
1200
1000
1200
Torque Requirement at Gearbox
T_CE_FTP-75
TCE_NEDC
150
120
100
Torque ( Nm )
Torque ( Nm )
600
Time (seconds)
100
50
0
0
200
400
600
800
1000
1200
1400
1600
80
60
40
20
1800
Time (seconds)
0
0
200
400
600
800
Time (seconds)
Figure 8. Torque provided by IC engine
Figure 12. Torque provided by IC engine
T_EM_FTP-75
T_EM_NEDC
40
Torque ( Nm )
Torque ( Nm )
40
20
0
-20
-40
-60
0
200
400
600
800
1000
1200
1400
1600
20
0
-20
-40
-60
0
1800
Time (seconds)
200
1.6
1.4
1.2
200
400
600
800
1000
1200
1400
1600
1800
Battery Charge ( As )
Battery Charge ( As )
1.8
1
0
Q_BT_NEDC
4
2.5
x 10
2
1.5
1
0
200
Time (seconds)
Figure 10.
800
Figure 13. Torque provided by Electric Machine
Q_BT_FTP-75
4
x 10
600
Time (seconds)
Figure 9. Torque provided by Electric Machine
2
400
Battery Charge variation
The graphs above depict the power split and the battery
usage for the control strategy for the FTP 75 cycle, it is to
be noted that the fuel consumption is 3.311 l/100km with a
SOC of 1.005 whereas for the conventional engine, it is 4.675
l/100 km. This is a reduced fuel consumption of 29.17%.
400
600
800
1000
1200
Time (seconds)
Figure 14.
Battery Charge variation
The graphs above depict the power split and the battery
usage for the control strategy for the NEDC cycle, it is to be
noted that the fuel consumption is 3.599 l/100km with a SOC
of 1.275 whereas for the conventional engine, it is 4.897 l/100
km. This is a reduced fuel consumption of 26.50%.
5
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
C. Result - Combined Average
This gives us a average combined fuel consumption of
3.455 l/100km with an average SOC of 1.14 and a reduction
in the fuel consumption of 27.81%.
V. L IMITATIONS
The effeciency of a model lies in its level of complexity.
More the complexity of the model, more realistic the results
are. But constructing a model is always striking a good
balance between complexity and realization potential. Hence
the first limitation lies in the modelling concept- Quasistatic
modelling, being a backward tracking method does not respect
the physical casuality and the driving profile must be known in
advance. Therefore this method is not able to handle feedback
control problems or deal with state events.
The next limitation lies in the nature of the controller.
Being designed with a rule based logic, the controller was
designed keeping only NEDC and FTP-75 cycles in mind
does not vouch for a optimal performance in other driving
cycles.
VIII. R EFERENCES
[1] A.Sciarretta, and L.Guzzella, Control of Hybrid Electric
Vehicles, IEEE Control Systems Magazine, 2007.
[2] L.Serra, G.Rizzoni and S.Onori, A Comparative Analysis
of Energy Management Strategies for Hybrid Electric Vehicles,
Journal of Dynamic Systems, Measurement, and Control,
2011.
[3] Niels J. Schouten, Mutasim A. Salman, and Naim A.
Kheir, Fuzzy Logic Control for Parallel Hybrid Vehicles, IEEE
Transactions On Control Systems Technology., vol. 10, no. 3,
May 2002.
[4] N.Jalil, Naim A. Kheir and Mutasim Salman, A Rule-Based
Energy Management Strategy for a Series Hybrid Vehicle,
Proceedings of the American Control Conference, June 1997.
[5] Farzad Rajaei Salmasi, Control Strategies for Hybrid
Electric Vehicles: Evolution, Classification, Comparison, and
Future Trends, IEEE Transactions On Vehicular Technology,
vol. 56, no. 5, September 2007.
VI. C ONCLUSION
The study shows that a proper energy management
controller can be the biggest difference between a effecient
PHEV and a non effecient PHEV. The implementation of
QSS toolbox made the realization and testing of the strategy
straightforward and quick. Based on the results that we have
obtained it is clear that, Energy Management in Electric and
Hybrid Vehicles is a complex task and requires a combined
knowledge from Mechanical, Electronics and Computer
Science. It ultimately shows that PHEVs can be viable
alternative to conventional vehicles but they still need to be
perfected for a full scale phasing out of conventional IC
engine powered vehicles.
VII. F UTURE W ORK
The future work would be improving the robustness of the
controller by employing fuzzy logics to the rule based strategy
base to make it effective on a wider range of drive cycles. A
more advanced improvement would be to employ dynamic
modelling to have more realistic results.
6
Vignesh Hegde Narashima, Anand Gali, Naresh Kumar Suresh Kumar
TU Kaiserslautern
Abstract—In this paper, a energy management controller is
developed for hybrid vehicles with parallel configuration. Using
the torque required at the gearbox, the state of charge(SOC)
of the battery, the speed and torques of the Electric machine
and the Internal combustion(IC) engines, a set of rules have
been developed, in a controller to effectively determine the split
between the different power plants powering the vehicle. The
underlying theme of this rule based logic is to optimize the
working of the components whilst maintaining a full charge
at the end of the driving cycle. Simulation results in overall
consumption and final battery charge which is used to assess the
performance of this energy management controller. The results
show a potential to improve the fuel economy and to maximize
the efficiency of the vehicle in general.
Keywords: Control strategies, Energy management, Fuel economy, Rule based management, Logic based, Hybrid vehicles,
PHEV, optimization.
I. INTRODUCTION
Hydrocarbon based fuels are the only sources of energy, used by conventional vehicles worldwide. Being nonrenewable and also a means of pollution, it is very important
to consider its impact on the environment. To offset the
degradation, large amounts of research are being carried out
to search for alternative sources to power the vehicle. The
search for improved fuel economy and reduced emission
without sacrificing vehicle performance has made the hybrid
technology one of the most promising and viable solutions out
in the market.
A. Hybrid Electric Vehicles
Hybrid Electric Vehicle ( HEV ) is a vehicle which for the
purpose of mechanical propulsion, draws energy from both of
the following vehicle sources of stored energy/power.
• a consumable fuel,
• an electrical energy/power storage device (e.g. battery,
capacitor, flywheel/generator, etc.).
In the last two decades, the Automotive industry has increasingly developed vehicles and concepts with these hybrid powertrains. Although there have been of different configurations,
all of the hybrid vehicles can be broadly classified in three
types,
1) Series Hybrid: Series HEVs is driven by an electric
motor, functioning as an electric vehicle while the
battery pack energy supply is sufficient, with an engine
tuned for running as a generator when the battery
pack is insufficient. There is no mechanical connection
between the engine and the wheels, and the purpose
of the range extender is to charge the battery. Opel
Ampera and BMW i3 are examples of series hybrids.
2) Parallel Hybrid: InParallel HEVs the IC Engine
and the Electric motor are both connected to the
mechanical transmission and can either individually
or simultaneously transmit power to drive the wheels,
usually through a conventional transmission. Honda’s
Integrated Motor Assist (IMA) system as found in
the Insight, Porsche Panamera S E-Hybrid Civic, and
Accord are examples of parallel hybrids.
3) Series Parallel Combined Hybrid Systems: Power-split
hybrids have the benefits of a combination of series
and parallel characteristics. As a result, they are more
efficient overall, because series hybrids tend to be more
efficient at lower speeds and parallel tend to be more
efficient at high speeds like Toyota Prius.
It is clearly visible, that the hybrid vehicles were made
just to achieve a goal, ’A better Fuel economy without
compromising on the consumer expectations with respect to
performance and other economic/comfort constraints’.
In order to achieve this, it is very important to optimize the
working of the components in the hybrid powertrain system.
This is not only possible through better efficient engines,
motors and generators but also with a very efficient Energy
management strategy. This Energy management strategy is
implemented using a energy management Controller, a similar
is which designed and presented here. The energy management
controller is expected to optimize the operation of all the
critical components in the PHEV: the IC Engine, EM( Electric
Machine) and the battery. For the implementation of the
strategy using the controller, a Rule based and logic based
approach is used. It has been selected as it provides an optimal
tradeoff between complexity and also effectiveness.
B. QSS Toolbox
The examples and the simulation calculation done in the
following paper has been done using QSS Toolbox. The
QSS Toolbox is a collection of Matlab/Simulink blocks and
the appropriate parameter files that can be run in any Matlab/Simulink environment. It allows for interconnection between relevant powertrain elements, scalability and integration
of elements with possibility to visualize and numerically
optimize them. Complex powertrains can be built using basic
blocks as shown in Figure 1.
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
Figure 1.
QSS Toolbox- Overview
C. Driving Cycle
Driving Cycle is a series of data points representing the
speed of a vehicle versus time. Driving cycles are produced by
different countries and organizations to assess the performance
of vehicles in various ways, as for example fuel consumption
and polluting emissions. In the paper two of such driving
cycles are considered for the design and implementation.
• NEDC: The New European Driving Cycle (NEDC) is a
driving cycle, last updated in 1997, designed to assess
the emission levels of car engines and fuel economy
in passenger cars (which excludes light trucks and
commercial vehicles).
•
FTP-75: The EPA Federal Test Procedure, commonly
known as FTP-75 for the city driving cycle, are a series
of tests defined by the US Environmental Protection
Agency (EPA) to measure tailpipe emissions and fuel
economy of passenger cars (excluding light trucks and
heavy-duty vehicles).
II. PARALLEL H YBRID E LECTRIC V EHICLE
Parallel Hybrid Electric Vehicles, which are most
commonly produced at present, have both an internal
combustion engine and an electric motor coupled
together. When only one of the two sources is being
used, the other must also rotate in an idling manner,
which would be connected by a one-way clutch, or
freewheel. The two sources may be applied to the same
shaft joined at an axis in parallel with the electric motor
lying between the engine and transmission.
And therefore speeds at this axis must be identical
and the supplied torques adds together with the electric
motor adding or subtracting torque to the system as
necessary.Parallel hybrids can be further categorized
depending upon how balanced the IC engine and the
Electric Motor are at providing the motive power. In
some cases, the combustion engine is dominant when
the electric motor turns on only when a boost is needed.
Whereas in other case, the vehicle can run with just the
electric system operating. But because current parallel
hybrids are unable to provide all-electric (ICE=OFF)
propulsion, they are often categorized as mild hybrids.
A. PHEV Configuration
The specific PHEV configuration used throughout the
paper, consists of the following components.
– Mercedes-Benz A 170 CDI (W168).
– Electric motor (12 kW / 60 Nm)
– Battery (16.38 kW / 0.468 kWh / 48 V)
– FTP 75 and NEDC as the test cycles to be optimized
on
III. ENERGY MANAGEMENT STRATEGY
Before we start with the description of the energy management strategy, which is the working design behind the
controller, the energy in the system is to be managed taking
the following conditions into concern.
1) The driver requirements according to the driving cycle
are satisfied consistently (the driver requirements being
the required torque at the gearbox, angular velocity and
anglular acceleration at the gearbox).
2
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
2) The battery satisfies Charge sustainment. Requirement
being that the battery charge at the end of the drive
cycle to be the same or equivalent to the quantity with
which we started, ensuring charge sustainability.
3) The overall powerplant system (IC Engine and EM) is
optimized for its efficiency.
While the PHEV is being operated, the controller should
determine how much power is to be supplied at the wheels
and how is it being distributed by the synergistic combination
of the IC Engine and the EM. The task is not only to ensure
the required torque at the gearbox is being provided but also
to determine how much of the same is needed to charge the
battery such that charge sustainment at the battery is ensured.
This involves optimal distribution of the power between IC
Engine and EM. If the battery is to be charged, it is performed
either via Regeneration during braking or through negative
torque supplied to the EM from the IC Engine. It is to be noted
that during this case the latter should provide power for both
driving the wheels and charging the battery. The power split
strategy is used to optimize the efficiency of the IC Engine
and Electric motor. To ensure optimal operating points it is
necessary to analyze the efficiency maps of the components
in use.
Regeneration Mode
Figure 4. Load Point Shifting Mode
B. Controller Basics
A. Power Split Strategy
This Power split energy management strategy is based on
the status of charge, the power demand. The strategy uses
a controller which essentially is a switch to select between
various operating modes depending upon the SOC value.
According to the strategy apart from the IC engine driven
mode, the other possible paths in which power can flow is
shown in the figures 2. 3. and 4. respectively
Figure 2.
Figure 3.
Electric Driving Mode
The above mentioned paths shall be further referred to as
Electric driving, IC driving, Regeneration, Load Point Shifting
1 (IC + motor mode; LPS1) and Load Point Shifting 2 (IC+
generator mode; LPS2) all throughout this paper. For a good
energy management strategy, the required torque demand must
be satisfied whilst ensuring the IC Engine is being operated at
the most efficient point for the required torque demand. Thus
the controller must decide how the IC Engine is to be utilized
in tandem with the EM to ensure a lower fuel consumption
compared to a conventional vehicle.
The general rule based logic of the controller is explained
in this section. The controller divides the operating conditions
of the PHEV into 4 basic zones depending upon the value of
the SOC the zones are named as
1) Excess zone ( SOC > 0.98)
2) Normal zone ( 0.7 < SOC < 0.98 )
3) Low zone ( 0.6 < SOC < 0.7 )
4) Very Low zone ( SOC < 0.6 )
These operating zones were determined on analyzing the
recharge potential of the battery through regeneration mode.
When the SOC went southward from 0.6 the tendency of the
charge being closer to the start was decreasing, i.e. it was
not possible to charge back without compromising on the efficiency. Each zone has its own sub strategies for implementing
the power split, the onus of the strategy being to get the SOC
as close as possible to the zone above it. For example at SOC
= 0.63, the strategy aims to bring the SOC closer to 0.7 as fast
as possible, thus ensuring a full charge at the end of the cycle.
This is realised ensuring the Torque provided by the EM as
both motor and generator (not simultaneously) are capped and
is proportional to the state of charge. This involves disabling
one or more operating modes depending upon the operating
zones. Based on this idea, LPS2 mode is disabled in Excess
zone and ED mode is disabled in Very low zone. Similarly
ED is permitted till 30 Nm, whilst in Normal and Low it is
permissible till 28Nm, 20Nm respectively.
C. Controller Overview
The controller overview is illustrated using a simple table
in Table below
3
Seminar Electromobility SS 2015
Mode/Zone
ED
LPS1
LPS2
IC
Regeneration
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
Excess
0<T<40
T>=150
Disabled
40<=T<150
T<0
Normal
0<T<30
T>=120
30<=T<=100
100<=T<150
T<0
Low
0<T<25
T>=170
25<=T<=100
100<=T<150
T<0
VeryLow
Disabled
Disabled
0<=T<80
T>=80
T<0
The implementation of the controller block in Simulink along with the operating zones are shown in Figure 5 and 6
respectively.
Figure 5.
Controller Block - Overview
Figure 6. Operating Zones - Illustration
4
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
IV. RESULTS
The results for the controller is illustrated in this section.
A. Result -FTP 75 Cycle
B. Result -NEDC Cycle
Results for FTP-75 test Cycle is shown below
Results for NEDC test Cycle is shown below
T_MGB_FTP-75
T_MGB_NEDC
150
Torque (Nm)
Torque (Nm)
150
100
50
0
-50
-100
-150
0
200
400
600
800
1000
1200
1400
1600
1800
Time (seconds)
Figure 7.
100
50
0
-50
-100
-150
0
200
400
Torque Requirement at Gearbox
Figure 11.
800
1000
1200
1000
1200
1000
1200
Torque Requirement at Gearbox
T_CE_FTP-75
TCE_NEDC
150
120
100
Torque ( Nm )
Torque ( Nm )
600
Time (seconds)
100
50
0
0
200
400
600
800
1000
1200
1400
1600
80
60
40
20
1800
Time (seconds)
0
0
200
400
600
800
Time (seconds)
Figure 8. Torque provided by IC engine
Figure 12. Torque provided by IC engine
T_EM_FTP-75
T_EM_NEDC
40
Torque ( Nm )
Torque ( Nm )
40
20
0
-20
-40
-60
0
200
400
600
800
1000
1200
1400
1600
20
0
-20
-40
-60
0
1800
Time (seconds)
200
1.6
1.4
1.2
200
400
600
800
1000
1200
1400
1600
1800
Battery Charge ( As )
Battery Charge ( As )
1.8
1
0
Q_BT_NEDC
4
2.5
x 10
2
1.5
1
0
200
Time (seconds)
Figure 10.
800
Figure 13. Torque provided by Electric Machine
Q_BT_FTP-75
4
x 10
600
Time (seconds)
Figure 9. Torque provided by Electric Machine
2
400
Battery Charge variation
The graphs above depict the power split and the battery
usage for the control strategy for the FTP 75 cycle, it is to
be noted that the fuel consumption is 3.311 l/100km with a
SOC of 1.005 whereas for the conventional engine, it is 4.675
l/100 km. This is a reduced fuel consumption of 29.17%.
400
600
800
1000
1200
Time (seconds)
Figure 14.
Battery Charge variation
The graphs above depict the power split and the battery
usage for the control strategy for the NEDC cycle, it is to be
noted that the fuel consumption is 3.599 l/100km with a SOC
of 1.275 whereas for the conventional engine, it is 4.897 l/100
km. This is a reduced fuel consumption of 26.50%.
5
Seminar Electromobility SS 2015
Narashima, Gali, Suresh Kumar : Energy Management Strategy for PHEV
C. Result - Combined Average
This gives us a average combined fuel consumption of
3.455 l/100km with an average SOC of 1.14 and a reduction
in the fuel consumption of 27.81%.
V. L IMITATIONS
The effeciency of a model lies in its level of complexity.
More the complexity of the model, more realistic the results
are. But constructing a model is always striking a good
balance between complexity and realization potential. Hence
the first limitation lies in the modelling concept- Quasistatic
modelling, being a backward tracking method does not respect
the physical casuality and the driving profile must be known in
advance. Therefore this method is not able to handle feedback
control problems or deal with state events.
The next limitation lies in the nature of the controller.
Being designed with a rule based logic, the controller was
designed keeping only NEDC and FTP-75 cycles in mind
does not vouch for a optimal performance in other driving
cycles.
VIII. R EFERENCES
[1] A.Sciarretta, and L.Guzzella, Control of Hybrid Electric
Vehicles, IEEE Control Systems Magazine, 2007.
[2] L.Serra, G.Rizzoni and S.Onori, A Comparative Analysis
of Energy Management Strategies for Hybrid Electric Vehicles,
Journal of Dynamic Systems, Measurement, and Control,
2011.
[3] Niels J. Schouten, Mutasim A. Salman, and Naim A.
Kheir, Fuzzy Logic Control for Parallel Hybrid Vehicles, IEEE
Transactions On Control Systems Technology., vol. 10, no. 3,
May 2002.
[4] N.Jalil, Naim A. Kheir and Mutasim Salman, A Rule-Based
Energy Management Strategy for a Series Hybrid Vehicle,
Proceedings of the American Control Conference, June 1997.
[5] Farzad Rajaei Salmasi, Control Strategies for Hybrid
Electric Vehicles: Evolution, Classification, Comparison, and
Future Trends, IEEE Transactions On Vehicular Technology,
vol. 56, no. 5, September 2007.
VI. C ONCLUSION
The study shows that a proper energy management
controller can be the biggest difference between a effecient
PHEV and a non effecient PHEV. The implementation of
QSS toolbox made the realization and testing of the strategy
straightforward and quick. Based on the results that we have
obtained it is clear that, Energy Management in Electric and
Hybrid Vehicles is a complex task and requires a combined
knowledge from Mechanical, Electronics and Computer
Science. It ultimately shows that PHEVs can be viable
alternative to conventional vehicles but they still need to be
perfected for a full scale phasing out of conventional IC
engine powered vehicles.
VII. F UTURE W ORK
The future work would be improving the robustness of the
controller by employing fuzzy logics to the rule based strategy
base to make it effective on a wider range of drive cycles. A
more advanced improvement would be to employ dynamic
modelling to have more realistic results.
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