Fuzzy logic control for parallel hybrid vehicles - IEEE Xplore

460

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 10, NO. 3, MAY 2002

Fuzzy Logic Control for Parallel Hybrid Vehicles Niels J. Schouten, Mutasim A. Salman, and Naim A. Kheir

Abstract—In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results have been used to assess the performance of the controller. A forward-looking hybrid vehicle model was used for implementation and simulation of the controller. Potential fuel economy improvement has been shown by using fuzzy logic, relative to other controllers, which maximize only the efficiency of the engine. Index Terms—Control strategies, emissions, fuel economy, fuzzy logic, hybrid vehicles, optimization.

I. INTRODUCTION

T

HE search for improved fuel economy, reduced emission, and affordable vehicles, without sacrificing vehicle performance, safety, reliability, and other conventional vehicle attributes has made the hybrid technology (both thermal and electrical motorization) one of the challenges for the automotive industry. Hybrid systems, using a combination of an internal combustion engine (ICE) and electric motor (EM), have the potential of improving fuel economy by operating the ICE in the optimum efficiency range and by making use of regenerative braking during deceleration. Within the last two decades, the automotive industry has increasingly developed vehicles with hybrid power-trains. Many concept vehicles, with several hybrid configurations, have been presented by several auto manufacturers. Few manufacturers have actually developed or have plans to develop hybrid vehicles for mass production [1]–[3]. There are three different types of hybrid systems, (see [2], [4] for details): • Series Hybrid: In this configuration, an ICE-generator combination is used for providing electrical power to the EM and the battery. • Parallel Hybrid: The ICE in this scheme is mechanically connected to the wheels, and can therefore directly supply mechanical power to the wheels. The EM is added to the drivetrain in parallel to the ICE, so that it can supplement the ICE torque. Manuscript received February 23, 2000; revised September 14, 2001. Manuscript received in final form January 28, 2002. Recommended by Associate Editor M. Jankovic. This work was supported by Partnership for New Generation of Vehicles, under contract to Oakland University. N. J. Schouten and N. A. Kheir are with the Electrical and Systems Engineering Department, Oakland University, Rochester, MI 48309 USA. M. A. Salman is with General Motors Corporation, Warren, MI 48090 USA. Publisher Item Identifier S 1063-6536(02)03420-6.

• Series-Parallel Combined System: Toyota Prius is an example of this so-called dual system. Because of the importance and potential benefits of the hybrid technology, many of the national laboratories, research institutions, and universities are actively involved in this research area. Hybrid technology is also one of the research topics for a partnership between United States government and the automotive industry, i.e., Partnership for a New Generation of Vehicles (PNGV). PNGV was established with the objective to develop a new generation of vehicles. These future vehicles should achieve up to three times todays average fuel economy (80 mi/gal 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 energy management strategy is implemented by a power controller. It controls the energy flow between all components, and optimizes power generation and conversion in the individual components. The objective of this paper is to develop a power controller for a parallel hybrid vehicle (PHV) that will optimize the fuel economy. PHV power controllers presented in the past [2], [5] only optimize the 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, EM, and battery. For the implementation of the controller, fuzzy logic has been used. Previous research at Oakland University [6], [7] and Ohio State University [8], [9] already indicated that fuzzy logic control is very suitable for hybrid vehicle control. It is a good method for realizing an optimal tradeoff between the efficiencies of all components of the PHV. Fuzzy logic control is tolerant to imprecise measurements and to component variability. It also gives a systematic methodology for the development of a rule-based energy management strategy. Section II of the paper, explains the basics of parallel hybrid vehicles, and briefly describes the simulation model. The energy management strategy is presented in Section III, followed by the description of the fuzzy logic power controller in Section IV. Finally, the simulation results and a brief analysis of these results are presented in Section V.

II. BASICS OF PHVs Fig. 1 presents a block diagram of a PHV with an EM and an ICE. For this particular configuration, the ICE and EM power are combined downstream of the transmission. Alternatively, the power could also be combined upstream of the transmission.

1063-6536/02$17.00 © 2002 IEEE


461

TABLE I MINIMUM VEHICLE PERFORMANCE REQUIREMENTS STATED BY PNGV

Fig. 1.

Block diagram of the parallel hybrid vehicle.

For both the upstream and downstream configuration, there are five different ways to operate the system, depending on the flow of energy: 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). A power controller is needed to manage the flow of energy between all components, while taking into account the energy available in the battery. The power controller adds the capability for the components to work together in harmony, while at the same time optimizes the operating points of the individual components. This is clearly an added complexity not found in conventional vehicles. For controller analysis and design, the PNGV systems analysis toolkit (PSAT) models [10] are used to simulate the PHV in the Matlab/Simulink environment. These models were developed under direction and contributions of the three major United States car companies (DaimlerChrysler, Ford, and General Motors) and the United States government. The model architecture is “forward looking,” which is much more suitable for controller analysis and design than a backward looking architecture (see [11] for a discussion on forward and backward looking models, and see [4], [12] for a description of backward looking models). 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. The driver achieves his objective 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 fuel economy for composite urban/highway travel is computed by letting the driver track the profiles described in the SAE J1711 standard. The composite fuel economy (Composite_FE) is given as Composie FE

city FE

highway FE

The city-driving test standard starts from ambient initial conditions (also known as “cold start”). It is composed of two urban cycles separated by a 10-min soak. In our analysis, the engine is assumed to be warm and only the second urban cycle is used. For the highway cycle only one cycle with warmed-up engine is used. In order to have a meaningful fuel economy value, it is

important to have the battery SOC charge-neutral for each of the cycles. 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; • manual transmission: five speed; • total test vehicle mass: 1100 kg. The size of the components was chosen to achieve the PNGV vehicle performance requirements given in Table I. III. ENERGY MANAGEMENT STRATEGY 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: 1) The driver inputs (from brake and accelerating pedals) are satisfied consistently (driving the PHV should not “feel” different from driving a conventional vehicle). 2) The battery is sufficiently charged at all times. 3) The overall system efficiency of the four basic components (ICE, EM, battery, and transmission) is optimized. While the PHV is operated, the power controller should determine how much power is needed to drive the wheels based on the driver inputs, and how much is needed to charge the battery. Then it should split the power between ICE and EM. If the battery needs to be charged, negative power needs to be assigned to the EM, and the ICE should provide the power for both driving the wheels and charging the battery. The power-split strategy can be used to optimize the efficiency of all four basic components of the PHV, because it determines the operating points of the components. Once the power is split in an optimal way, the power generation/conversion of the individual components also has to be optimized. To identify the optimal operating points, the efficiency maps of the components were used. A. 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. For a given ICE power level, the optimal efficiency curve defines the optimal

462


Fig. 2. Efficiency map and optimal curve of the ICE. The arrows indicate increasing efficiency.

Fig. 4.

Efficiency map of the battery. The arrows indicate increasing efficiency.

Fig. 5. Block diagram of the energy flow: (1) mechanical path and (2) electrical path.

Fig. 3. Efficiency map, continuous and peak power curves of the EM. The arrows indicate increasing efficiency.

operating point in the speed-torque plane. Once the optimal speed and torque are known, the torque is controlled by varying the throttle angle and the speed by shifting gears of the automated manual transmission (note that for a given vehicle speed, a higher gear number will result in a lower engine speed). For the generic CIDI map used in this paper, the ICE efficiency on the optimal curve is highest for engine speeds between 230 and 320 rad/s, corresponding to an ICE power between 30 and 50 kW, with the absolute optimum at 47 kW. Therefore, the power-split strategy should preferably result in an ICE power level in this range. The efficiency on the curve is lowest for very low and very high engine speeds; therefore the ICE power should preferably not be below 6 kW or over 50 kW. Fig. 3 presents a contour plot of the efficiency of the permanent magnet motor also in the speed-torque plane. The efficiency plot for motoring and generating mode is the same, only the sign of the torque is reversed. Superimposed on the plot are the continuous and peak power curves.

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 EM speeds between 320 and 430 rad/s. The optimal power for this region is approximately 10 kW. Fig. 4 presents a generic efficiency plot of an advanced battery in the state of charge (SOC)-power plane. It can easily be seen that the battery operates most efficiently for high SOC and low power levels. For power-split control, this indicates that the SOC should be as high as possible by frequently charging at low power level. In this study, it is assumed that the highest admissible SOC is 0.9 for charging, and 0.97 for regenerative braking. B. Power Split Strategy Now that the efficiency characteristics of the components are known, it is possible to formulate the power-split strategy. The difference between using the ICE or the EM to drive the wheels is explained in Fig. 5. When the ICE is used (path 1), the energy flows directly from the ICE through the transmission to the wheels. When the EM is used (path 2), energy first flows from the ICE through the transmission to the EM, operated as a generator, for charging the battery; later the energy will flow from the battery to the EM, operated as motor, to the wheels.


Fig. 6.

463

Simplified block diagram of the power controller.

In path 1 the mechanical power produced by the ICE will immediately be used to drive the wheels. In path 2 the same mechanical power will be converted to electrical, chemical, electrical, and back to mechanical. Inherent to these power conversions are losses. These additional losses considerably lower the efficiency of using the EM for driving the wheels. Therefore, using the EM (path 2) will only be efficient in a few (extreme) situations, when directly using the ICE (path 1) is very inefficient. The minimum losses due to power conversions are determined by the efficiency of the EM, first used as a generator and then as a motor, and of the battery, first used to store and then to release energy. The maximum round-trip efficiency of the battery used in our simulations is 0.95 and of the EM (motor/generator) 0.88, so that the maximum efficiency of the power conversions in path 2 is 84% (minimum additional losses 16%). Therefore, it is only justified to use the EM (path 2), in the cases that directly using the ICE (path 1) is at least 16% less efficient than using the ICE in path 2 (for charging the battery). For a good energy management strategy the average ICE efficiency in path 2 (ICE used for charging the battery), will be close to the optimal ICE efficiency. Using the ICE efficiency map (Fig. 2), it was determined that the efficiency on the optimal curve only drops below 16% of the absolute optimum for power levels below 6 kW and over 50 kW. 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, and the ICE can also produce additional power to charge the battery. When the power command is over 50 kW, the EM is to be used to complement the ICE, because in this case the ICE is again very inefficient and close to its maximum (55 kW). The EM power for driving the wheels is computed using this strategy. When the driver power command is between 6 and 50 kW, the EM power for charging the battery still needs to be optimized. The next section discusses how the driver power command is computed and how fuzzy logic is used to compute the optimal generator power. IV. FUZZY LOGIC POWER CONTROLLER This section first discusses the basics of the fuzzy logic controller that was used to implement the power controller, and then the power controller itself will be presented in more detail. A. Fuzzy Logic Control Basics The general energy management strategy described in the previous section has been implemented using fuzzy logic

TABLE II RULE BASE OF THE FUZZY LOGIC CONTROLLER

[13]–[16]. Fig. 6 presents a block diagram of the power controller, the fuzzy logic controller (FLC) being the main part. The basic idea of an FLC is to formulate human knowledge and reasoning, which can be represented as a collection of if–then rules, in a way tractable for computers. Table II presents a list of if–then rules that represent the energy management strategy described in the previous section (a description of the rule base will follow in the next section). The variables in Table II , and , correspond to the preceding “then”: SOC, inputs of the FLC (Fig. 6): battery state of charge, driver power command, and EM speed, respectively. The variables following (generator power), and scaling factor correspond “then”: to the outputs. The first part of a rule (preceding “then”), called the antecedent, specifies the condition (i.e., the combination of inputs) for which a rule holds. The second part (following “then”), called the consequent, is the corresponding control action, i.e., the controller output. The antecedents in Table II contain linguistic terms (low, high, optimal, etc.) that reflect human knowledge of the PHV. The antecedents are defined as a combination of individual conditions, using the logical AND and NOT operators (in general, it is also possible to use the logical OR). The linguistic terms, the connectives, and the if–then relations need to be defined in a way tractable for computers. The linguistic terms are represented by fuzzy sets. Consider the variable “driver power command” in Fig. 7. This variable is represented by the linguistic terms “normal” and “high.” These linguistic terms are represented by two fuzzy sets that are defined by the two membership functions in Fig. 7. The memberof the variship functions define the degree of membership

464


Inference and Aggregation can be combined into a single equation

Fig. 7. Membership functions for driver power command.

able in the two fuzzy sets. For driver power command larger than 50 kW the degree of membership in the fuzzy set “high.” equals 1 and the degree of membership in the fuzzy set “normal” equals 0. For normal driver power command less than 30 kW, it is the other way around. One can imagine that there is a gradual transition from normal to high driver power command. This transition is represented by the overlapping interval in Fig. 7 (between 30 and 50 kW). The values in this interval belong to both fuzzy sets with various degrees of membership, e.g., 45 kW belongs to “normal” with membership 0.25 and to “high” with membership 0.75. In order to work with the fuzzy rules in Table II the fuzzy sets need to be combined using the logical connectives AND and NOT (and in general also OR). For this purpose, the logical connectives from conventional Boolean logic have been extended to their fuzzy equivalents. There are several ways to implement these fuzzy equivalents, but in general the minimum operator is ], the simple compleused for AND [e.g., ) for NOT, and the maximum operator ment (e.g., ]. for OR [e.g., Using the fuzzy sets and the fuzzy set operators, it is possible to design a fuzzy reasoning system (inference system) that can act as a fuzzy controller. The reasoning mechanism used in this paper can be separated in four main steps. 1) Fuzzification: The membership degrees of the three input values of the FLC are computed using the membership functions (Figs. 7–9). 2) Degree of Fulfillment: The degree of fulfillment for the antecedent of each rule (Table II) is computed using the fuzzy logic operators. This degree of fulfillment determines to which degree the th rule is valid. For (SOC), example: Table II, rule 3: . 1 3) Inference: This operation represents the if–then implication. The degree of fulfillment of the antecedent of each rule is used to modify the consequent of that rule accordingly. This is done by multiplying the degree of fulfillwith the consequent of ment of the antecedent kW. rule . For example Table II, rule 2: , the results 4) Aggregation: For each controller output of the inference step are combined into a single value. This is done by taking the average of the inference results weighted by the degrees of fulfillment of the rules.

with the number of rules. The fuzzy logic controller (FLC) described in this paper is of the Sugeno–Takagi type [17]. A conceptually different type of FLC is the Mandami FLC [18], for which the consequent part of the rules (the control actions) are also represented by fuzzy sets. For Mandami’s approach the inference and aggregation act on the fuzzy sets of the consequents, instead of single (“crisp”) values, and the result of both steps is again a fuzzy set. A fifth step (defuzzification) is needed to replace this fuzzy set by a single (“crisp”) value (such as the center of area of the fuzzy set) that acts as one of the controller outputs. B. Power Controller Fig. 6 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 send 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 it can never 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 all 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 two–dimensional 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. 6) computes the optimal generator power for the EM in case it is used for charging the battery and a scaling factor for the EM in case 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.


465

Fig. 8. Membership functions for state of charge.

Fig. 10. Operating points, efficiency map, and optimal curve of the internal combustion engine for the fuzzy logic controller.

Fig. 9.

Membership functions for electric motor (EM) speed.

The three inputs of the fuzzy logic controller are: driver power , SOC and EM speed . The membercommand ship functions (MFs) for driver power command are presented in Fig. 7. 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. Fig. 8 presents the MFs for SOC. 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.” Fig. 9 presents the MFs for EM speed. 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 (Fig. 3). The rule base is presented in Table II. If the SOC is too high will be zero, to pre(rule 1) the desired generator power vent 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 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 (30–50 kW). Finally, when the SOC is not too low (rule 9), the scaling factor is one. The third block in Fig. 6 computes the final values for the ICE and EM power , using , , and power normally is the driver power comthe scaling factor (SF). mand added to the desired generator power , and is minus the desired generator power . There are two exceptions. is smaller than the threshold 1) When kW, and value (SF 6 kW), then . is larger than the max2) When , imum ICE power at current speed and then . is positive (EM used as motor), has Finally, when SF . to be multiplied by the scaling factor C. Gear Shifting Control The desired ICE power level is used by the gear shifting 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 gear number closest to the desired gear ratio is chosen. V. SIMULATION RESULTS The power controller has been implemented and simulated with PSAT using the driving cycles described in the SAE J1711 standard. Figs. 10–12 present the operating points of the ICE, EM, and battery plotted on the efficiency maps. The operating points in Fig. 10 are close to the optimal curve, which indicates that the

466


Fig. 11. Operating points, efficiency map, continuous and peak power curves of the electric motor (EM) for the fuzzy logic controller.

Fig. 13. Operating points, efficiency map, and optimal curve of the ICE for the default PSAT controller.

Fig. 12. Operating points and efficiency map of the battery for the fuzzy logic controller.

ICE has been operated close to optimal efficiency. The operating points of the EM (Fig. 11) are mainly in the optimal speed range of 320–430 rad/s. The operating points of the battery (Fig. 12) are at a relatively high SOC (between 0.77 and the maximum 0.9), and the power level is relatively low, both resulting in high efficiency. These results have been compared with the results for the default controller in PSAT. This controller only optimizes the ICE efficiency, and does not optimize EM, battery or transmission efficiency. It optimizes the ICE efficiency by adjusting both the ICE speed (using the transmission gear ratio) and torque to make the operating point as close as possible to the optimal curve. For the default controller the efficiency of all components is lower, except for the ICE efficiency. The default controller is more effective in the optimization of the ICE, because there is no tradeoff between the ICE efficiency and the efficiency of the other components of the PHV [unlike the fuzzy logic controller

Fig. 14. Operating points, efficiency map, continuous and peak power curves of the EM for the default controller.

(FLC)]. Therefore, for the default controller the operating points of the ICE (Fig. 13) are closer to the optimal curve than for the FLC (see Fig. 10). However, the efficiency of the other components is better for the FLC than for the default controller, and therefore the overall efficiency is better for the FLC. Figs. 14 and 15 present the operating points of the EM, and battery for the default controller. The operating points of the EM are much more spread out over the map, and are not particularly in the optimal speed range of 320–430 rad/s (compare to Fig. 11), which explains the lower EM efficiency compared to the FLC. The operating points of the battery are in the desired SOC range, but the average charging and discharging power is higher (compare to Fig. 12), thus decreasing the battery efficiency.


467

in both tables). For the FLC the losses in the ICE, EM, battery, and drivetrain are smaller for both the urban and highway cycles. The vehicle losses, 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 higher for the default controller, the ICE losses for the FLC are smaller. This is because the ICE produces less energy for the FLC. Less energy is needed to complete the cycle, since the efficiency of the other components is higher than for the default controller. The overall improvement of the FLC for an urban cycle equals 6.8% and for a highway cycle 9.6%. VI. CONCLUSION

Fig. 15. Operating points and efficiency map of the battery for the default controller. TABLE III NORMALIZED LOSSES FOR THE DEFAULT CONTROLLER AND THE FLC FOR THE URBAN CYCLE

TABLE IV NORMALIZED LOSSES FOR THE DEFAULT CONTROLLER AND THE FLC FOR THE HIGHWAY CYCLE

In this paper, a fuzzy logic-based power controller for PHVs has been presented. This power controller optimizes the energy flow between the main components of the PHV and optimizes the energy generation and conversion in the individual components (ICE, EM, transmission, and battery). The efficiency maps of the components have been used to design the controller. The power controller first converts the accelerator and brake pedal inputs of the driver to a driver power command. The driver power command, state of charge of the battery, and electric motor speed are then used by a fuzzy logic controller to compute the optimal generator power and a scaling factor for the electric motor. The driver power command, optimal generator power, and scaling factor are used to compute the optimal ICE and EM power. Furthermore, the efficiency of the ICE for a given power level is optimized using an optimal speed-torque curve, and using gear shifting to control the speed of the ICE. The power controller ensures that the driver inputs (from brake and accelerator pedals) are satisfied consistently, the battery is sufficiently charged at all times, and the fuel economy of the PHV is optimized. Simulation results, using the driving cycles described in the SAE J1711 standard, show potential improvement by using fuzzy logic, over other strategies that optimize only the ICE efficiency. In future research, the robustness of the fuzzy logic controller will be investigated in more detail. The control structure will be used to design a controller that minimizes the emissions and maximizes the fuel economy. Adaptive/learning elements will be added to the controller, to enable on-line controller optimization. Combined component size and controller optimization will also be studied. REFERENCES

To be able to compare the efficiency of the default controller and the FLC, the battery energy at the beginning and the end of each cycle has to be the same. This is done by using an algorithm that iteratively adapts the initial SOC until the SOC at the end of the cycle equals the initial SOC. This is the basis for the charge sustaining part of the SAE J1711 test procedure. Table s III and IV present the normalized losses for the urban and highway cycle, respectively. The losses are normalized with respect to the total loss for the default controller (which is 100%

[1] N. Hattori, S. Aoyama, S. Kitada, I. Matsuo, and K. Hamai, “Configuration and operation of a newly developed parallel hybrid propulsion system,” in Proc. Global Powertrain Congr., Detroit, MI, Oct. 6–8, 1998. [2] B. K. Powell, K. E. Bailey, and S. R. Cikanek, “Dynamic modeling and control of hybrid electric vehicle powertrain systems,” IEEE Contr. Syst. Mag., pp. 17–33, Oct. 1998. [3] J. M. Miller, A. R. Gale, and A. Sankaran, “Electric drive subsystem for a low-storage requirement hybrid electric vehicle,” IEEE Trans. Veh. Syst., vol. 48, pp. 1788–1796, Nov. 1999. [4] Z. Rahman, K. L. Butler, and M. Ehsani, “Designing parallel hybrid electric vehicles using V-ELPS 2.01,” in Proc. Amer. Contr. Conf., San Diego, CA, June 1999, pp. 2693–2697.

468


[5] M. Ehsani, Y. Gao, and K. L. Butler, “Application of Electrically Peaking Hybrid (ELPH) propulsion system to a full-size passenger car with simulated design verification,” IEEE Trans. Veh. Syst., vol. 48, pp. 1779–1787, Nov. 1999. [6] N. Jalil and N. Kheir, “Energy management studies for a new generation of vehicles (Milestone #5: Fuzzy logic for the series hybrid),” Tech. Rep., Dept. Elect. Syst. Eng., School Eng. Comput. Sci., Oakland Univ., Rochester, MI, Mar. 1998. , “Energy management studies for a new generation of vehicles [7] (Milestone #6: Fuzzy logic for the parallel hybrid),” Tech. Rep., Dept. Elect. Syst. Eng., School Eng. Comput. Sci., Oakland Univ., Rochester, MI, Mar. 1998. [8] B. M. Baumann, “Intelligent control strategies for hybrid vehicles using neural networks and fuzzy logic,” Master’s thesis, Dept. Elect. Eng., Ohio State Univ., Columbus, 1997. [9] H. Kono, “Fuzzy control for hybrid electric vehicles,” Master’s thesis, Department of Electrical Engineering, The Ohio State University, 1998. [10] S. T. McBroom, “Toolkit for tomorrow’s car,” in Technology Today: Southwest Research Institute Publications, Spring 1997. [Online]. Available: www.swri.org.

[11] K. B. Wipke, M. R. Cuddy, and S. D. Burch, “ADVISOR 2.1: User-friendly advanced powertrain simulation using a combined backward/forward approach,” IEEE Trans. Veh. Technol., vol. 48, pp. 1751–1761, Nov. 1999. [12] L. Guzzella and A. Amstutz, “CAE tools for quasistatic modeling and optimization of hybrid powertrains,” IEEE Trans. Veh. Technol, vol. 48, pp. 1762–1770, Nov. 1999. [13] U. Kaymak, R. Babuˇska, and H. R. van Nauta Lemke, “Fuzzy control—Theory and design,” J. A, vol. 36, no. 3, 1995. [14] K. Hirota, Ed., Industrial Applications of Fuzzy Technology. Tokyo, Japan: Springer-Verlag, 1993. [15] D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control. Berlin, Germany: Springer-Verlag, 1993. [16] C. C. Lee, “Fuzzy logic in control systems,” IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404–435, 1990. [17] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116–132, 1985. [18] E. H. Mandami, “Applications of fuzzy algorithms for control of simple dynamic plant,” Proc. IEE, pp. 1585–1588, 1974.