Energy management strategies for parallel hybrid vehicles using fuzzy logic

Control Engineering Practice 11 (2003) 171–177

Energy management strategies for parallel hybrid vehicles using fuzzy logic Niels J. Schoutena, Mutasim A. Salmanb, Naim A. Kheira,* a

Department of Electrical and Systems Engineering, Oakland University, School of Engineering and Computer Science, 102A SEB, Rochester, MI 48309, USA b General Motors Research and Development Center, 30500 Mound Road, Warren, MI 48090, USA Received 19 October 2001; accepted 17 January 2002

Abstract This paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optimize the fuel economy of the PHV, by optimizing the operational efficiency of all its components. The controller optimizes the power output of the electric motor/generator and the internal combustion engine by using vehicle speed, driver commands from accelerator and braking pedals, state of charge (SOC) of the battery, and the electric motor/generator speed. Separate controllers optimize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine. r 2003 Published by Elsevier Science Ltd. Keywords: Hybrid vehicles; Energy management systems; Fuzzy control; Optimal power flow; Automotive control

1. Introduction The environmental challenges of the 21st century will require tremendous advancements in the automotive industry. The next generation of vehicles has to be much cleaner and more efficient than the current ‘‘conventional’’ vehicles. Ultimately, zero emission vehicles (ZEV) will be required, pushing the development of electric and fuel cell vehicles. However, battery and fuel cell technology still limit the potential of both developments. For the short-to-mid-term hybrid vehicles (using a combination of an internal combustion engine and electric motor) are a cleaner and more efficient alternative for conventional vehicles, while providing the same range and performance. Hybrid technology is one of the most promising research topics for the partnership for a new generation of vehicles (PNGV). PNGV is a partnership between the United States government and the automotive industry, with the objective to develop a new generation of vehicles. These future vehicles should achieve up to three times today’s average of fuel economy (80 mpg= *Corresponding author. Tel.: +1-248-370-2177; fax: +1-248-3704633. E-mail address: [email protected] (N.A. Kheir). 0967-0661/03/$ - see front matter r 2003 Published by Elsevier Science Ltd. PII: S 0 9 6 7 - 0 6 6 1 ( 0 2 ) 0 0 0 7 2 - 2

34 km/l), without compromising consumer expectations with respect to performance, comfort, safety, quality and cost of ownership. In order to achieve these goals, it is very important to optimize the architecture and components of the hybrid vehicle, but as important is the energy management strategy that is used to control the complete system. The objective of this paper is to develop a power controller for a parallel hybrid vehicle (PHV) that optimizes the fuel economy. See Section 2 for a description of PHVs and Powell, Bailey, and Cikanek (1998) for a description of several other hybrid vehicle configurations. PHV power controllers presented in the past (Powell et al., 1998; Ehsani, Gao, & Butler, 1999) only optimize internal combustion engine (ICE) operation, thus not using the full potential of hybrid technology. The power controller presented here will optimize the operation of all major PHV components: ICE, electric motor (EM), and battery. For the implementation of the controller, fuzzy logic has been used. Previous research at Oakland University (Jalil and Kheir, 1998) and Ohio State University (Kono, 1998) already indicated that fuzzy logic is very suitable for hybrid vehicle control. It is a good method for realizing an optimal trade-off between the efficiencies of all components of the PHV. It is also very robust, because

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it is tolerant to imprecise measurements and to component variability. Section 2 explains the basics of PHVs, and briefly describes the simulation model. The energy management strategy is given in Section 3, followed by the description of the controller in Section 4. Finally, the simulation results and a brief analysis of these results are presented in Section 5.

* *

automated manual transmission: 5 speed; total test vehicle mass: 1100 kg.

The size of the components was chosen to achieve the PNGV vehicle performance requirements given in Table 1.

3. Energy management strategy 2. Parallel hybrid vehicle basics Fig. 1 presents a block diagram of a PHV with an electric motor (EM) in parallel with an internal combustion engine (ICE) and transmission. There are 5 different ways to operate the system, depending on the flow of power: (1) provide power to the wheels with only the ICE; (2) only the EM; or (3) both the ICE and the EM simultaneously; (4) charge the battery, using part of the ICE power to drive the EM as a generator (the other part of ICE power is used to drive the wheels); (5) slow down the vehicle by letting the wheels drive the EM as a generator that provides power to the battery (regenerative braking). For analysis and controller design, the PNGV Systems Analysis Toolkit (PSAT) models (McBroom, 1997) are used to simulate the PHV in the Matlab/ Simulink environment (see Wipke, Cuddy, & Burch (1999) for more information on hybrid vehicle modeling). Apart from the model of the PHV, PSAT also includes a model of a driver, and a set of driving cycles given as speed versus time profiles. The driver’s objective is to track the speed versus time profile, by modulating the brake and accelerator pedals. The power controller uses these driver inputs to compute the commands for the several local controllers, such as the ICE, EM, battery, and transmission controllers. The specific PHV configuration, used throughout the paper, consists of the following components: *

*

*

compression ignition direct injection (CIDI) engine: 55 kW; permanent magnet motor: 20 kW continuous, 40 kW peak; advanced battery: 40 kW, 2 kWh;

This section describes the energy management strategy, which is the philosophy behind the power controller. The energy in the system should be managed in such a way that: the driver inputs (from brake and accelerating pedals) are satisfied consistently (driving the PHV should not ‘‘feel’’ different from driving a conventional vehicle), the battery is sufficiently charged at all times, and the overall system efficiency of the four basic components (ICE, EM, battery, and transmission) is optimal. The power controller is used to determine how much power is needed to drive the wheels, and how much to charge the battery. Then it should split the power between ICE and EM. If the battery needs to be charged, negative power will be assigned to the EM, and the ICE will provide the power for both driving and charging the battery. To determine the optimal power split and how to optimize the power generation/ conversion of the individual components, the efficiency maps of the components were used. 3.1. Efficiency maps Fig. 2 presents a contour plot of the efficiency of a generic CIDI engine in the speed–torque plane. Superimposed on the contour plot is the optimal efficiency curve. This curve defines the optimal speed and torque for a given power level. The ICE speed can be controlled by shifting gears of the automated manual transmission. The CIDI efficiency on the optimal curve is highest for engine speeds between 230 and 320 rad/s, corresponding to an engine power between 30 and 50 kW, with the absolute optimum at 47 kW. Therefore, the power-split

Table 1 Minimum vehicle performance requirements stated by PNGV

combining gear

clutch

ICE

Transmission Battery

Wheels

EM

Mechanical Electrical Power

Fig. 1. Block diagram of a parallel hybrid vehicle.

0–60 mph (0–97 km/h) 0–85 mph (0–137 km/h) 40–60 mph (64–97 km/h) in 5th gear Distance at 5 s Maximum acceleration Max. road grade at 55 mph (89 km/h) in 5th gear Maximum launch grade Maximum speed

12 s 23 s 5.3 s 140 ft (43 m) 17 ft/s2 (5.2 m/s2) 6.5% 30% 100 mph (161 km/h)

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1

ICE

173

Trans

Wheels

2

ICE torque [Nm]

EM 100

Battery 50

Fig. 3. Block diagram of power flow: using ICE directly (path 1); using EM (path 2).

0

100

200

300

400

3.2. Power-split strategy

ICE speed [ rad/s]

Power Flowing into Battery [kW]

40

20

0

-20

-40 0.3

0.5

0.7

0.9

State of Charge

Fig. 2. Efficiency maps. The arrows indicate increasing efficiency. (Top) efficiency map and optimal efficiency curve of combustion engine. (Bottom) efficiency map of battery.

strategy should preferably result in an engine power in this range. The efficiency on the optimal efficiency curve is lowest for very low and very high engine speeds. A similar efficiency map has been used to analyze the efficiency of the permanent magnet motor/generator. The EM speed is directly related to vehicle speed, because it cannot be controlled by gear shifting. Therefore, the EM efficiency can only be optimized, by optimizing the power at a given EM speed. The efficiency is optimal for speeds between 320 and 430 rad/s. The optimal power in this region is approximately 10 kW. Fig. 2 also presents an efficiency map of the advanced battery in the state of charge (SOC)-power plane. The battery operates most efficiently for high SOC and low (charging and discharging) power levels. Therefore, the SOC should be as high as possible by frequently charging at low power level. For our research the battery should not be charged when the SOC is over 0.9, and regenerative braking should not be used when the SOC is over 0.97.

Now that the efficiency characteristics of the components are known, it is possible to formulate the powersplit strategy. It is possible to use the ICE directly to drive the wheels (Fig. 3, path 1), or to use the EM to drive the wheels (path 2). When the EM is used, the energy comes from the battery, which was charged earlier with the ICE. Therefore, when analyzing the use of the EM, the efficiency of the power flow from the ICE to the EM used as a generator, to the battery during charging, and then from the battery during discharging, to the EM used as a motor, to the wheels has to be analyzed. To optimize the PHV efficiency the most efficient option (using ICE directly, or using EM) has to be chosen at all times. When the ICE is directly used to drive the wheels, mechanical power directly flows from the ICE to the wheels (path 1). When the EM is used (path 2), The mechanical power from the ICE is converted to other types of power, before the power is transferred to the wheels. Inherent to these additional power conversions are losses (at least 16% for our configuration). For optimal efficiency the ICE should only be used for charging the battery when the ICE efficiency is very close to the optimum. This way, the high ICE efficiency during charging can partly compensate for the losses due to power conversions. If this is the case, using the EM is more efficient when the efficiency for using the ICE directly is more than 16% below the optimum (using the optimal efficiency curve). This is the case for low and high power levels (below 6 and over 50 kW, respectively), and for low vehicle speed (below 7 mph=11 km/h). Therefore, when the power command is below 6 kW, only the EM is to be used to drive the wheels. Between 6 and 50 kW, only the ICE is to be used to drive the wheels. When the power command is over 50 kW, the EM is to be used to complement the ICE. When only the ICE is used to drive the wheels, the ICE can produce additional power to charge the battery. This generator power still needs to be optimized. The next section

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4. Power controller The general energy management strategy described in the previous section has been implemented using a Takagi–Sugeno fuzzy logic controller (Takagi and Sugeno, 1985). A fuzzy logic controller relates the controller outputs to the inputs using a list of if–then statements called rules (see Table 2 as an example of rules). The if-part of the rules refers to adjectives that describe regions (fuzzy sets) of the input variable. A particular input value belongs to these regions to a certain degree, represented by the degree of membership (see Fig. 5 for examples of membership functions that define the degree of membership). The then-part of the rules of a Takagi–Sugeno controller refers to values of the output variable. To obtain the output of the controller, the degrees of membership of the if-parts of all rules are evaluated, and the then-parts of all rules are averaged, weighted by these degrees of membership. The logical AND has been implemented with the minimum operator, the logical NOT with the simple complement. See (Kaymak, Babu$ska, & van Nauta Lemke, 1995) for more information on fuzzy control. Fig. 4 presents a simplified block diagram of the power controller. The first block converts the driver

inputs from the brake and accelerator pedals to a driver power command. The signals from the pedals are normalized to a value between zero and one (zero: pedal is not pressed, one: pedal fully pressed). The braking pedal signal is then subtracted from the accelerating pedal signal, so that the driver input takes a value between –1 and +1. The negative part of the driver input is sent to a separate brake controller that will compute the regenerative braking and the friction braking power required to decelerate the vehicle. The controller will always maximize the regenerative braking power, but this should not exceed 65% of the total braking power required, because regenerative braking can only be used for the front wheels. The positive part of the driver input is multiplied by the maximum available power at the current vehicle speed. This way the complete range of

Table 2 Rule base of the fuzzy logic controller

normal Degree of Membership

discusses the power controller that implements the energy management strategy and uses fuzzy logic to compute the optimal generator power.

0 .5

0 0

20

5 6 7 8 9

Vehicle Speed Driver Command

Driver Command Interpreter

Driver Power Command SOC

Fuzzy Logic Controller

60

80

low

normal too high

0 .5

0 0 .2

0 .4

0 .6

0 .8

1

800

1000

State of Charge

Degree of Membership

3 4

If SOC is too high then Pgen is 0 kW If SOC is normal and Pdriver is normal and oEM is optimal then Pgen is 10 kW If SOC is normal and oEM is not optimal then Pgen is 0 kW If SOC is low and Pdriver is normal and oEM is low then Pgen is 5 kW If SOC is low and Pdriver is normal and oEM is not low then Pgen is 15 kW If SOC is too low then Pgen is Pgen;max If SOC is too low then scale factor is 0 If SOC is not too low and Pdriver is high then Pgen is 0 kW If SOC is not too low then scale factor is 1

40

too low

1

0

1 2

high

1

Driver Power Command (kW)

Degree of Membership

174

1

optimal

low

high

0 .5

0 0

200

400

600

EM Speed (rad/s)

Fig. 5. Membership functions.

Generator Power Scaling Factor

ICE Power ICE and EM Power

EM Speed

Fig. 4. Simplified block diagram of power controller.

EM Power

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power is available to the driver at all times. The maximum available power is computed by adding the maximum available ICE and EM power. The maximum available EM and ICE power depends on EM/ICE speed and EM/ICE temperature, and is computed using a 2D look-up table with speed and temperature as inputs. However, for a given vehicle speed, the ICE speed has one out of five possible values (one for each gear number of the transmission). To obtain the maximum ICE power, first the maximum ICE power levels for those five speeds are computed, and then the maximum of these values is selected. Once the driver power command is computed, the fuzzy logic controller (Fig. 4) computes the optimal generator power for the EM and a scaling factor for the EM when it is used as a motor. This scaling factor is (close to) zero when the SOC of the battery is too low. In that case, the EM should not be used to drive the wheels, in order to prevent battery damage. When the SOC is high enough, the scaling factor equals one. The three inputs of the fuzzy logic controller are driver power command (Pdriver ), SOC and EM speed (oEM ). The membership functions (MF) for driver power command are presented at the top of Fig. 5. Fuzzy set ‘‘normal’’ represents the power range for ‘‘normal’’ driving conditions, ‘‘high’’ represents the range only used during high acceleration and high speed. The power range for the transition between normal and high (30–50 kW) is the optimal range for the ICE. The MFs for SOC are presented in the middle of Fig. 5. Fuzzy sets ‘‘too low’’ and ‘‘too high’’ represent the ranges where the SOC should not be. Fuzzy set ‘‘normal’’ represents the range where the SOC should be and ‘‘low’’ acts as a buffer between ‘‘normal’’ and ‘‘too low’’. The MFs for EM speed are presented at the bottom of Fig. 5. Fuzzy set ‘‘optimal’’ represents the optimal speed range. The MF drops relatively fast, since the efficiency also drops fast for speeds outside the optimal range. The rule base is presented in Table 2. If the SOC is too high (rule 1) the desired generator power (Pgen ) will be zero, to prevent overcharging the battery. If the SOC is normal (rules 2 and 3), the battery will only be charged when both the EM speed is optimal and the driver power is normal. If the SOC drops to low, the battery will be charged at a higher power level. This will result in a relatively fast return of the SOC to normal. If the SOC drops to too low (rules 6 and 7), the SOC has to be increased as fast as possible to prevent battery damage. To achieve this, the desired generator power is to be the maximum available generator power and the scaling factor is decreased from one to zero. Rule 8 prevents battery charging when the driver power demand is high and the SOC is not too low. Charging in this situation will shift the ICE power level outside the optimum range

175

(30–50 kW). Finally, when the SOC is not too low (rule 9), the scaling factor is to be one. The third block in Fig. 4 computes the final values for the ICE power (PICE ) and EM power (PEM ), using Pdriver ; Pgen ; and the scaling factor (s.f.). PICE normally is to be the driver power command added to the desired generator power (PICE ¼ Pdriver þ Pgen ), and PEM is to be minus the desired generator power (PEM ¼ Pgen ). There are two exceptions: 1. When Pdriver þ PEM;gen is smaller than the threshold value (s.f. * 6 kW), then PICE ¼ 0 kW, and PEM ¼ Pdriver : 2. When Pdriver þ PEM;gen is greater than the maximum ICE power at current speed (PICE;max@speed ), then PICE ¼ PICE;max@speed and PEM ¼ Pdriver  PICE;max@speed : Finally, when PEM is positive (EM used as motor), PEM has to be multiplied by the scaling factor (PEM ¼ PEM * s:f:). The desired ICE power level is used by the gearshifting controller to compute the optimum gear number of the automated manual transmission. First, the optimal speed–torque curve is used to compute the optimal ICE speed and torque for the desired ICE power level. The optimal ICE speed is then divided by the vehicle speed to obtain the desired gear ratio. Finally, the transmission gear ratio closest to the desired gear ratio is chosen. Since this paper was first presented at the 2000 IFAC Conference on Mechatronic Systems, the following three extensions of the power controller are being developed: 1. A feature that enables users to specify a trade-off between the fuel economy and the emissions of the PHV. 2. A neural network for online fine-tuning. 3. A global optimization algorithm for offline optimization of the controller. Once new results will be available, we plan to publish them in a future paper.

5. Simulation results The controller has been simulated with PSAT using the test procedure described in the SAE J1711 standard. Fig. 6 presents the operating points of the ICE, plotted on the efficiency map. The operating points are close to the optimal curve, which indicates that the ICE has been operated close to optimal efficiency. The operating points of the EM were mainly in the optimal speed range of 320–430 rad/s. The battery (see also Fig. 6) was operated at a relatively high SOC (between 0.77 and the

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of the other components. The results showed that for the default controller the efficiency of all components is lower, except for the ICE. The default controller is more effective in the optimization of the ICE, because there is no trade-off between the ICE efficiency and the efficiency of the other components of the PHV. However, the overall efficiency of the FLC is better than for the default controller. Table 3 presents the normalized losses for a combined urban and highway simulation. The losses are normalized with respect to the total loss for the default controller (which is 100%). For the FLC the losses in the ICE, EM, battery, and drivetrain are smaller. The drag, rolling resistance, energy for accessories and friction braking are approximately the same, because both controllers are simulated with the same vehicle and the same driving cycles. Although the ICE efficiency is a little bigger for the default controller, the ICE losses for the FLC are smaller. This is because the total energy produced by the ICE is smaller for the FLC, since the efficiency of the other components of the PHV is bigger than for the default controller, thus less energy is needed to complete the cycle. The overall improvement of the FLC equals 7.7%.

6. Conclusions Fig. 6. Operating points. (Top) efficiency map, optimal curve, and operating points of the combustion engine. (Bottom) efficiency map and operating points of battery.

Table 3 Normalized losses for default controller and FLC; combined urban and highway simulation results Normalized losses Internal combustion engine (ICE) Electric motor (EM) Battery Drivetrain Rolling resistance Drag Friction braking Accessories Total

Default

FLC

63.9 3.6 0.62 13.3 5.0 6.9 1.3 5.4

61.5 2.7 0.43 9.0 5.0 6.9 1.4 5.4

100

92.3

In this paper, a fuzzy-logic-based power controller for parallel hybrid vehicles (PHV) has been presented. This power controller optimizes the power flow between the main components of the PHV and optimizes the energy generation and conversion in the individual components (internal combustion engine (ICE), electric motor (EM), transmission, and battery). The efficiency maps of the components have been used to design the controller. Simulation results, using the driving cycles described in the SAE J1711 standard, show potential improvement over other strategies that only optimize the ICE efficiency.

Acknowledgements This work was supported by the Partnership for a New Generation of Vehicles (PNGV) under contract to Oakland University.

References maximum 0.9), and at relatively low power, both resulting in high efficiency. The results have been compared with the results for the default controller in PSAT, which optimizes ICE operation only without considering the optimum region

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