Fuzzy Logic Energy Management Strategy for Plug-in Hybrid Electric Vehicles

Vehicle Engineering (VE) Volume 2, 2014

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Fuzzy Logic Energy Management Strategy for Plug-in Hybrid Electric Vehicles Yu Zhang*1, Meilan Zhou2, Dengke Lu3 Department of Electrical Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China zhangyu8419@163.com;zhoumeilan001@163.com; 1q2w3e4rqq@163.com Received 30 June 2013; Accepted 28 July 2013; Published 12 March 2014 Š 2014 Science and Engineering Publishing Company

Abstract The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking.

The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, there will be a need to recharge the battery using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, fuzzy logic energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for several number of drive cycles. The proposed PHEV algorithm results are compared with the determinacy rulebased energy management strategy for HEV with similar battery capacity as PHEV. Keywords Plug-in Hybrid Electric Vehicle (PHEV); Fuzzy Logic Controller; Energy Management Strategy (EMS); State of Charge (SOC)

Introduction Nowadays, the major problem of a Hybrid Electric Vehicle (HEV) is to solve the increasing cost problem due to the rising gasoline prices. A HEV is powered from two sources of energy: an electric motor via battery and an internal combustion engine (ICE). In the Hybrid Electric Vehicles, the battery is charged through the ICE and the regenerative braking while decelerating the vehicle, but during this procedure, ICE is used to charge the battery at first, then battery is used to drive the motor, consequently this causes large

energy losses in the whole process. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, the battery will need to be recharged using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack.

So theoretically it needs another form to charge the battery, at the same time, this method must be able to reduce exhaust emissions and improve fuel economy to reduce cost.

Compared to the traditional HEV, Plug-in Hybrid Electric Vehicle has large capacity energy storage device. The energy storage device of PHEV used by an electric grid to charge increases the pure electric mode, reduces exhaust emissions and improves fuel economy. At present, in many other countries, PHEV has become an important technology for energy saving and emissions reduction in the new generation of automobile development plan.

The energy management strategy of HEV in order to achieve the best fuel economy is by reducing maximum exhaust emissions and to keep the battery’s health by distributing power and output torque between the motor and ICE. Currently, a lot of literature has been researched on HEV. However, as PHEV is a relatively new concept, so it lacks the feasibility of energy management strategy in these literatures. Banvait has researched on the determinacy rule-based energy management strategy for the PHEV. However, this energy management strategy belongs to static torque distribution; and it cannot obtain an optimal performance. On the basis of Banvait, Wu Jian used Particle Swarm Optimization Algorithm (PSO) to optimize the threshold parameters. This algorithm

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improves the performance of static logical threshold energy management strategy. On the basis of Pontryagin minimum principle and according to the global optimization strategy proposed by PHEV, S.Stockar was able to get the global optimal solution of control variables. However, as global optimization strategy requires an accurate condition of the whole information of the cycle, it has a lot of practical limitation and the result can only be used as energy management strategy quality reference. In this paper, first of all, a feasible PHEV energy management strategy based on fuzzy logic control has been proposed. Then a simulation on the electric vehicle simulation software platform ADVISOR, was implemented to compare and analyse several drive cycle. Finally the results will be compared with the determinacy rule-based energy management strategy.

Powertrain System Plug-in Hybrid Electric Vehicles combine the power from an electric motor with that from an ICE to propel the vehicle; and they can either work independently or together at the same time. When the battery is plentiful, battery power is preferred to drive motor, achieving pure electric mode, so as to reduce emissions and fuel consumption. While the battery power is not enough, the ICE works. And while the vehicle needs higher power, both the ICE and electric motor work together at the same time. By using automatic clutch connected between them, the vehicle has the idle stop, pure motor drive, pure ICE drive, motor and ICE united drive, light load charging and regenerative braking. Fuel converter FC-S141-emis is selected in simulation experiments, the displacement of which is 1.0L.when V = 5700r / min , Pmax = 41kW ;in the highest efficiency zone, when V = 3477r / min ,the result

Vehicle Engineering (VE) Volume 2, 2014

ICE

Motor Generator clutch DC/AC AC/DC

Auto-controlled clutch

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Transmission

Battery

FIG. 2 THE POWERTRAIN SYSTEM OF PHEV

Table 1 shows the parameters of powertrain system of PHEV. TABLE1 THE MAIN COMPONENT PARAMETERS OF MODEL

PARAMETER No/full load vehicle mass M (kg) Coefficient of Drag Cd Frontal area A (m2) Radius R (m) Engine Maximum power (kW)/speed (r/min) Peak Torque (Nm)/speed (r/min) motor Rated power (kW) Battery Capacity (A∙h) Rated voltage (V)

VALUE 1276/1541 0.335 2 0.282 63/5500 145/2000 30 28 300

Design of Fuzzy Controller The control model of determinacy rule-based energy management strategy(strategy A) is the logic threshold control strategy based on the engineering experience, the engine is limited within the optimized domain and the SOC of energy storage is kept at reasonable range; the charging torque is changed as the change of SOC; However, the optimization zone is rough, control strategy depends on the change of SOC, however the SOC is very difficult to be estimated; in addition, the control model is showed in Fig.3.

Tmax = 81N .m . The governing performance curve is showed in Fig.1. Fig.2 shows the powertrain system of PHEV.

FIG. 3 THE CONTROL MODEL OF DETERMINACY RULE-BASED ENERGY MANAGEMENT STRATEGY

FIG.1 THE GOVERNING PERFORMANCE CURVE OF FC-S141-EMIS

2

The basic fuzzy control model in ADVISOR is developed based on the theory, the practicality of control rule is poor and general, and fuzzy control is only

Vehicle Engineering (VE) Volume 2, 2014

in view of basic vehicle, not including plug-in Hybrid Electric Vehicle, but the opening of the underlying code can be easily developed again. In the research, redeveloped model is imported to verify the control effect and new configuration model is set in the configuration subsystem; then the top control model of PHEV is structured, along with the modification of loading file PARALL_EV_ defaults_ in.m, adding of the model name defined in options structure variables; the followed by modifying the file block_diagram_ name and adjust_config_bds.m, and replacement of the image of car in gui_image.m.

Design Principle In this paper, the power splitting between motor and ICE is divided into three different kinds of cases:

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cover all of the cases. Since the range of vehicle torque requirement is wider, more words should be used to describe the vehicle torque requirement Treq .This paper chooses 9 words to describe Treq and the domain of the

Treq is[-6, 6], according to the performance requirement, in general, the change of battery state of charge was between 0.35 and 0.9 in the PHEV, so as to describe SOC it is set into three fuzzy subsets, and the motor torque output Tm is divided into 9 fuzzy subsets in the domain.

The input and output variables of the fuzzy set and its domain provisions are as follows: The fuzzy set of Treq is: {NBB, NB, NM, NS, O, PS, PM, PB, PBB}, and the domain is [-6, 6].

1) The battery power is plentiful, namely SOC ≼ 0.5 , and motor will act as the major power source to propel the vehicle;

The fuzzy set of SOC is: {L, M, H}, and the domain is [0, 1].

2) The battery power is not enough, namely

PB, PBB}, and the domain is [-1.5, 1.5].

SOC ≤ 0.35 , the motor will remain at low power maintain state, and the ICE will act as the main power source to propel the vehicle;

While NBB is negative great, NB is negative big, NM is negative middle, NS is negative small, O is zero, PS is positive small, PM is positive middle, PB is positive big, PBB is positive great, L is low, M is middle, H is high.

3) For other conditions, namely 0.35 < SOC < 0.5 , both motor and ICE will act as the power source to propel the vehicle.

According to the above design proposed, set input variables of fuzzy logic controller are the vehicle torque requirement Treq and the battery state of charge (SOC). Because the vehicle torque requirement Treq is the distribution between the ICE and motor, therefore as long as one of the outputs is known, another can be determined. In this paper, the output variable of the fuzzy controller is the output torque of motor Tm .

Design Fuzzy Rules

The fuzzy set of Tm is: {NBB, NB, NM, NS, O, PS, PM,

1.0

3) Establish corresponding fuzzy control rule library.

Plug-in Hybrid Electric Vehicle has many kinds of operation mode, so this requires the fuzzy controller to

NM

NS

0

PS

PM

PB

PBB

-4.8

-3.6

-2.4

-1.2

0

1.2

2.4

3.6

4.8

6

1

0 -6

Vehicle torque requirement Treq L

1.0

M

H

0.5

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

PM

PB

PBB

0.6

0.9

1.2

State of Charge 1.0

2) Confirm the membership functions of various kinds of fuzzy languages;

NB

0.5

The fuzzy rules design process can be divided into three parts: 1) Choose the proper fuzzy language of the input variables and output variable;

NBB

NBB

NB

NM

NS

0

PS

-0.3

0

0.3

0.5

0 -1.5

-1.2

-0.9

-0.6

1.5

Motor output torque Tm FIG. 4 INPUT AND OUTPUT VARIABLES OF MEMBERSHIP FUNCTIONS IN THE FUZZY CONTROLLER

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Vehicle Engineering (VE) Volume 2, 2014

slowly, leading to the torque that is negative and small, at the same time the battery energy is not enough, so in this case the torque will be given to generator and the generator will charge the battery, and the energy is from the regenerative braking.

E) It is shown the vehicle requires a little of torque, and the battery energy is not enough, so in this case the motor will not output torque, only ICE will provide the torque.

FIG. 5 SURFACE OF OBSERVATION OF FUZZY CONTROLLER

According to the experience and the simulation analysis in this paper, input variable uses triangle membership function, SOC uses trapezoid membership function, and the output variable Tm uses triangle membership function. Figure 4 shows the two inputs and one output variable of membership functions in the fuzzy controller, figure 5 shows the surface of observation of fuzzy controller.

The control of the fuzzy controller rule library is based on the understanding, intuition and the experience about control of the physical characteristics of the controlled object. And the fuzzy logic control rule is the core of the control links. The specific format is: if (provided) so (conclusion). In This paper, 27 rules to control the fuzzy controller have been made, here are a few of the control rules used to illustrate the function of fuzzy rules:

In order to improve the vehicle driving stability, the rules need to cover all of the operation modes of the powertrain system.

Simulation and Results Model In order to validate the design and the energy management strategy, MATLAB/Simulink was used to build the PHEV model for simulation. Figure 6 is the model of the proposed energy management strategy of the electric vehicle simulation software platform ADVISOR.

A.if ( Treq is PB) and (SOC is H) so ( Tm is PBB) B. if ( Treq is PS) and (SOC is H) so ( Tm is PM) C. if ( Treq is PM) and (SOC is M) so ( Tm is PM)

FIG. 6 FUZZY LOGIC CONTROLLER MODEL

D. if ( Treq is NB) and (SOC is L) so ( Tm is NM) E. if ( Treq is PS) and (SOC is L) so ( Tm is 0) In the above control rules: A) It is shown that vehicle requires much torque and the battery energy is plentiful, so in this case the motor outputs maximum torque and rest of the torque from ICE. B) It is shown the vehicle requires a little of torque, and battery energy is plentiful, so in this case the torque will be provided all by motor.

C) It is shown the vehicle requires the torque between A and B, and battery energy is enough, in this case the torque will be jointly provided by motor and ICE. D) It is shown in this time the vehicle decelerates

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FIG. 7 A PART OF THE VEHICLE TOP ADVISOR INTERFACE MODEL

Simulation for Several Drive Cycles On the platform of ADVISOR, the determinacy rulebased energy management strategy (strategy A) and the energy management strategy based on fuzzy logic

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control (strategy B) are tested respectively. The fuel economy shows the fuel consumption per 100 km, the smaller the value is, the better the fuel economy will be.

km/h

Strategy A Strategy B

FIG. 11 THE CHANGES OF SOC WITH UDDS DRIVE CYCLE

km/h

FIG. 8 INFORMATION OF UDDS ROAD CONDITION

t/s

FIG. 9 INFORMATION OF NEDC ROAD CONDITION

In this paper, first of all, the simulation with NEDC and UDDS road conditions was made, then comparison was made between the value of SOC and the ICE output torque Te (= Te Treq − Tm ) by five cycles.

The information of UDDS and NEDC road conditions is showed in Fig.8 and Fig.9.

The initial value of SOC in this paper is set to be 0.9; and the minimum value of SOC is 0.35. In the following picture, the decreasing speed of the value of SOC from strategy B is more slowly than that of SOC from strategy A, but both the strategies were able to finally let the SOC keep in between 0.35.

From the figure 10 and figure 11, we can know, in strategy B when the battery energy is plentiful i.e. SOC ≥ 0.5 , the motor will act as the major power source to propel the vehicle, the ICE output torque Te is very small, but when the battery energy is not enough i.e. SOC ≤ 0.35 , the ICE will be provided much more torque and the motor will remain at low power maintain state. Compare figure 12 with figure 13, when the SOC ≤ 0.35 , the ICE output torque of strategy A will be significantly greater thanthat of strategy B. This is because in strategy A the ICE at this time, not only propels the vehicle, but also charges the battery, so the output torque is very big. This paper proposed strategy B, at this time the ICE is not used for the battery charging, because the energy that battery stores all coming from the electric grid and the regenerative braking, thus avoiding energy loss caused by the battery charging process in ICE.

e T /( N · m )

t/s

t/s Strategy A

Strategy A Strategy B

e T /( N · m ) t/s Strategy B FIG. 10 THE CHANGES OF SOC WITH NEDC DRIVE CYCLE

FIG. 12 THE ENGINE TORQUE OUTPUT WITH NEDC DRIVE CYCLE

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e T /( N ·m ) t/s Strategy A

Torque (N·m)

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e T /( N · m )

Speed (r/min) FIG. 15-2 THE OPERATION OF ENGINE UNDER UDDS ROAD CONDITION WITH STRATEGY B t/s Strategy B

FIG. 13 THE ENGINE TORQUE OUTPUT WITH UDDS DRIVE CYCLE

Table 2 is the data of two control strategies of the simulation results. In order to evaluate the fuel consumption reasonably, the consumption of power is converted into fuel consumption (Fuel Economy ( L / 100km )), which is used to be assessed.

The 97#gasoline is as the standard, the density of which is 0.737g/ml and the released energy during which is burned completely is 4.7 × 107 J / kg , so the energy of 1L gasoline is 3.46 × 107 J / L . The amount of gas can be defined according to the burning degree of fuel in ADVISOR. The block is showed in Fig.14, this block model--the exhaust system of ICE. Emphasis is on the catalytic converter after treatment of emissions.

Since converter performance is tied to operating temperature, the temperature and heat flows of the exhaust gas, converter, and engine manifold/ downpipe are predicted, and the control model is showed in Fig. 14.

Through Fig. 15-1 and Fig. 15-2, the operation points of engine under UDDS road condition with strategy B distribute more reasonably in the range of max torque curve than strategy A, and torque ripple is smaller.

Torque (N·m)

FIG. 14 THE EXHAUST SYSTEM OF ICE

By comparing proposed strategy B and strategy A, from table 2 we can see that the fuel consumption has significantly reduced and at the same time exhaust emissions have decreased correspondingly. In short, fuzzy logic control can reduce engine fuel consumption and reduce exhaust emissions. And no matter how much the value of SOC is in fuzzy controller, it can guarantee the battery in the optimal interval work, and the change of SOC is slow.

TABLE 2 RESULTS

Speed (r/min) FIG. 15-1 THE OPERATION OF ENGINE UNDER UDDS ROAD CONDITION WITH STRATEGY A

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UDDS

NEDC

Drive cycle

Control strategy

HC NOX Fuel CO Economy Emissions Emissions Emissions L/(100km) g/km g/km g/km

Strategy A

5.2

0.391

0.091

0.052

Strategy B

4.5

0.37

0.085

0.38

Improve

13.46%

5.37%

6.59%

26.92%

Strategy A

5.7

0.386

0.092

0.057

Strategy B

5

0.372

0.087

0.046

Improve

12.28%

3.23%

5.43%

19.30%

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Conclusions

Murphey, Y.L., Jungme Park, Kiliaris, L., Kuang, M.L. ,

Based on the characteristics of the PHEV, in this paper the author has designed the application in the PHEV fuzzy control energy management strategy. The simulation results in the cycle of road conditions show that the proposed energy management strategy can effectively reduce exhaust emissions and improve fuel economy between the reasonable distribution in the ICE and motor torque.

Hybrid

Vehicle

Power

Control—Part

II:

Online

Intelligent Energy Management[J]. Vehicular Technology 2013, 62(1): 69-79.

Khayyam, H. Stochastic Models of Road Geometry and Wind Condition for Vehicle Energy Management and Control[J]. Vehicular Technology, 2013, 62(1): 61-68.

Opila, Daniel F., Wang, Xiaoyong, McGee, Ryan, Gillespie, R. Brent, Cook, Jeffrey A., Grizzle, Jessy W. An Energy

REFERENCES

Management Controller to Optimally Trade Off Fuel

Gong Qiuming, Li Yaoyu, Peng Zhongren. "Trip based optimal power management of plug-in hybrid electric vehicle

Masrur, M.A., Phillips, A.M., Qing Wang.Intelligent

with

advanced

traffic

modeling”.

Economy and Drivability for Hybrid Vehicles[J]. Control Systems Technology,2012, 20(6):1490 - 1505.

SAE

Li, Siguang G.; Sharkh, Suleiman Abu; Walsh, Frank C.;

International Journal of Engines (S1946-3936), 2009, 1(1):

Zhang, Cheng-Ning N. Energy and Battery Management

861-872.

of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy

Banvait H, Anwar S, Chen Yaobin. "A rule-based energy

Logic[J]. Vehicular Technology, 2011, 60(8):3571-3585.

management strategy for plug-in hybrid electricvehicle

Tani, Abdallah; Camara, MamadouBaïlo; Dakyo, Brayima.

(PHEV)”. American Control Conference, St. Louis, MO,

Energy Management Based on Frequency Approach for

USA, 2009: 3938-3943.

Hybrid Electric Vehicle Applications: Fuel-Cell/Lithium-

Adhikari. "An Online Power-Balancing Strategy for a Parallel

Hybrid

Electric

Vehicle

Assisted

by

an

Integrated Starter Generator” Ieee Transactions on Vehicular Technology, 2689-2699.2010

Battery and Ultracapacitors[J]. Vehicular Technology, 2012 61(8):3375-3386.

Tani, A.; Camara, M.B.; Dakyo, B.; Azzouz, Y. DC/DC and DC/AC Converters Control for Hybrid Electric Vehicles

Hegazy. "PSO algorithm-based optimal power flow control of fuel cell/supercapacitor and fuel cell/battery hybrid

Energy Management-Ultracapacitors and Fuel Cell[J].

Industrial Informatics, 2013, 9(2): 686-696.

electric vehicles", the International Journal for Computat-

Borhan, Hoseinali; Vahidi, Ardalan; Phillips, Anthony M.;

ion and Mathematics in Electrical and Electronic

Kuang, Ming L.; Kolmanovsky, Ilya V.; Cairano, Stefano

Engineering, 86-107.2013.

Di.MPC-Based Energy Management of a Power-Split

Wu Jian, Cui Naxin, Zhang Chenghui, et al. "PSO algorithmbased optimization of plug-in hybrid electric vehicle energy management strategy". The 8th World Congress on Intelligent Control and Automation, 2010: 3997-4002.

Stephanie Stockar, Vincenzo Marano, "Giorgio Rizzoni, et al.

Hybrid Electric Vehicle [J]. Control Systems Technology, 2012, 20(3): 593-603.

Zhang, Chen; Vahidi, Ardalan. Route Preview in Energy Management of Plug-in Hybrid Vehicles[J]. Control Systems Technology, 2012, 20(2): 546-553.

Optimal control for plug-in hybrid electric vehicle

Kelouwani, Sousso; Henao, Nilson; Agbossou, Kodjo; Dubé,

applications". AmericanControl Conference, Marriott

Yves; Boulon, Löic. Two-Layer Energy-Management

Waterfront, Baltimore, MD, USA, 2010: 5024-5030.

Architecture for a Fuel Cell HEV Using Road Trip

Xia Meng; Langlois, N., "Optimized fuzzy logic control strategy of hybrid vehicles using ADVISOR", Computer, Mechatronics,

Control

and

Electronic

Information[J]. Vehicular Technology, 2012, 61(9):38513864.

Engineering

(CMCE), 2010 International Conference on, vol.4, no., pp.444-447, 24-26 Aug. 2010.

Millo. “Development of an optimal strategy for the energy management of a range-extended electric vehicle with additionalnoise,vibration and harshness constraints”, Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 4-16.2013.

Yu. Zhang received the Master of engineering degree in electronical and electronics from Harbin University of Science and Technology, Harbin, China, in 2010. She is currently a Ph.D candidate in electrical engineering and automation.

Her interests include fuzzy control on electric vehicle and automotive engineering.

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