- Email: [email protected]
Energy Management Strategies for Parallel Hybrid Vehicles Using Fuzzy Logic
Copyright @ IFAC Mechatronic Systems. Darmstadt. Germany. 2000
ENERGY MANAGEMENT STRATEGIES FOR PARALLEL HYBRID VEHICLES USING FUZZY LOGIC
Niels J. Schouten X , Mutasim A. Salman·, and N aim A. Kheirx
xElectrical and Systems Engineering Department. Oakland University 102 Science and Engineering Building. Rochester. Michigan 48309, USA [email protected], [email protected] • General Motors Research and Development Center 30500 Mound Road, Warren, Michigan 48090-9055, USA [email protected]
Abstract: TIlis paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optiroize 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 optiroize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine. Copyright © 2000 IFAC Keywords: Hybrid Vehicles, Energy Management Systems, Fuzzy Control, Optimal Power Flow, Automotive Control
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 = 34 kmIl), 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 optiroize the architecture and components of the hybrid vehicle, but as important is the energy management strategy that is used to control the complete system.
1. INTRODUCTION The environmental challenges of the twenty-first 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.
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, et ai. , 1998) for a description of several other hybrid vehicle
Hybrid technology is one of the most proffilsmg research topics for the Partnership for a New
83
Table 1 Minimum Vehicle Performance Requirements Stated by PNGV 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
=:> Mechanical ;z::> Electrical Pov.er Fig. 1. Block Diagram of a Parallel Hybrid Vehicle configurations. PHV power controllers presented in the past (powell, et af., 1998; Ehsani, et al., 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 impiementation of the controller, fuzzy lo~ic ~ been used. Previous research at Oakland Uruverslty (lalil and Kheir, 1998) and Ohio State Unive.rsio/ (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 it is tolerant to imprecise measurements and to component variability.
12 s 23 s 5.3 s 140 ft (43 m) 17 ftls2 (5.2 m1s2 ) 6.5% 30% 100 mph (161 km/h)
controllers, such as the ICE, EM, battery, and transmission controllers. The specific PHV configuration, used throughout the paper, consists of tlIe following components: • Compression Ignition Direct Injection (CIDI) engine: 55 kW • Permanent Magnet motor: 20 kW continuous, 40 kW peak • Advanced battery: 40 kW, 2kWh • Automated manual transmission: 5 speed • Total test vehicle mass: 1100 kg The size of the components was chosen to achieve tlIe PNGV vehicle performance requirements given in Table l.
Section 2 of the paper, 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.
3. ENERGY MANAGEMENT STRATEGY This section describes the energy management strategy, which is the philosophy behind tlIe 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.
2. PARALLEL HYBRID VEHICLE BASICS Figure 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).
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 drivmg and charging tlIe battery. To determine tlIe optimal power split and how to optimize tlIe power generation/conversion of tlIe individual components, the efficiency maps of tlIe components were used.
For analysis and controller design, the PNGV Systems Analysis Toolkit (PSAT) mode.ls (McBroom, 1997) are used to simulate the PHV m the Matlab/Simulink environment (see (Wipke, et al., 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. !he driver' s objective is to track the speed versus tIme profile, by modulating the brake and ~ccel~rator pedals. The power controller uses tlIese driver mputs to compute tlIe commands for tlIe several local
3. J EffiCiency Maps Figure 2 presents a contour plot of tlIe efficiency of a generic CIDI engine in tlIe speed~torque p~e . Superimposed on the contour plot IS tlIe OptImal
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Fig. 3. Block Diagram of Power Flow: Using ICE Directly (path 1); Using EM (path 2)
O L10~O----~2~070----~3~OO~--~4~OO~~ ICE speed (rad/s]
3.2 Power Split Strategy Now that the efficiency characteristics of the components are known, it is possible to formulate the power-split strategy. It is possible to use the ICE directly to drive the wheels (Figure 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 bilttery during discharging, to the EM used as a motor, to the wheels has to be analyzed.
Stale 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.
To optimize the PRY 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).
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 powersplit 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 optirnizing 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.
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 kW and over 50 kW, respectively), and for low vehicle speed (below 7 mph = 11 km/h).
Figure 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.
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 optirnized. The next section discusses the power controller that implements the energy management
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Table 2
1 2 3 4
5 6 7
8 9 Vehicle Speed
Rule Base of the Fuzzy Logic Controller
H SOC is too high then Pgen is 0 kW H SOC is nonnal and Pdriver is nonnal and O)EM is optimal then Pgen is 10 kW H SOC is nonnal and O)EM is not optimal then P gcn is 0 kW H SOC is low and Pdrivcr is nonnal and O)EM is low then P gen is 5 kW H SOC is low and P drivcr is nonnal and O)EM is not low then Pgen is 15 kW H SOC is too low then P gcn is Pgcn.max H SOC is too low then scale factor is 0 H SOC is not too low and P drivcr is high then Pgcn is 0 kW H SOC is not too low then scale factor is 1
Drive r Power Drive r Commandl Drive r Command Command • Interpreter SCC
•
Generator Power Fuzzy Logic Controller
Scaling Factor
ICE Power ICE and EM Power
EM Power
EM Speed
Fig. 4. Simplified Block Diagram of Power Controller strategy and uses fuzzy logic to compute the optimal generator power.
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 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 EMlICE speed and EMlICE 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.
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 Figure 5 for examples of membership functions that defme the degree of membership). The then-part of the rules of a TakagiSugeno 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, et al., 1995) for more infonnation on fuzzy control.
Once the driver power command is computed, the fuzzy logic controller (Figure 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.
Figure 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 three inputs of the fuzzy logic controller are driver power command (Pdriver), SOC and EM speed (O)EW . The membership functions (MF) for driver power command are presented in Figure 4 (top). Fuzzy set 'nonnal' represents the power range for
86
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Fig. 5. Membership Functions
0.5
' 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. MFs for SOC (Figure 5, middle): 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' . MFs for EM speed (Figure 5, bottom): 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.
0 .7
Slate of Charge
O.g
Fig. 6. Operating Points. Top: efficiency map, optimal curve, and operating points of the combustion engine. Bottom: efficiency map and operating points of battery. is to be the driver power command added to the desired generator power (PlCE = Pdnver + Pgen ), and P EM is to be minus the desired generator power (PEM = -Pgen ) . There are two exceptions: 1. When P dnver + PEM,gen is smaller than the threshold value (s.f. * 6 kW), then PlCE = 0 kW, and PEM = Pdnver. 2. When P dnver + PEM,gen is greater than the maximum ICE power at current speed (PlCE,max@speed), then P lCE = PlCE,max@speed and PEM = P dnver - PlCE,max@speed. Finally, when P EM is positive (EM used as motor), P EM has to be multiplied by the scaling factor (PEM = PEM * s.f.) .
The rule base is presented in Table 2. If the SOC is too high (rule 1) the desired generator power (Pge.,) 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 (30-50 kW). Finally, when the SOC is not too low (rule 9), the scaling factor is to be one.
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.
5. SIMULATION RESULTS The controller has been simulated with PSAT using the test procedure described in the SAE J 1711 standard. Figure 6 presents the operating points of the
The third block in Figure 4 computes the final values for the ICE power (PlCE) and EM power (PEW, using Pdn vcr, Pgen, and the scaling factor (s.f.). PlCE normally
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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 Figure 6) was operated at a relatively high SOC (between 0.77 and the maximum 0 .9), and at relatively low power, both resulting in high efficiency.
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
The results have been compared with the results for the default controller in PSAT, which optimizes ICE operation only without considering the optimum region 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 PHY.
default
FLC
63.9 3.6 0 .62 13 .3 5 .0 6 .9 1.3 5.4 100
61.5 2.7 0.43 9.0 5.0 6 .9 1.4 5.4 92 .3
REFERENCES Ehsani, Mehrdad, Yimin Gao, and Karen L. Butler (1999). Application of Electrically Peaking Hybrid (ELPH) Propulsion System to a Full-Size Passenger Car with Simulated Design Verification. IEEE Transactions on Vehicular Systems, Vol. 48 No. 6, pp. 1779-1787. Jalil, Nashat and Naim Kheir (1998). Energy Management Studies for a New Generation of Vehicles (Milestone # 6: Fuzzy Logic for the Parallel Hybrid) , Technical Report, Department of Electrical and Systems Engineering, Oakland University, Rochester, Michigan, USA. Kaymak, u., R. BabuSka, H.R. van Nauta Lemke (1995). Fuzzy Control - Theory and Design, JournalA , Vol. 36 No. 3 . Kono, Hiroshi (1998). Fuzzy Control for Hybrid Electric Vehicles, Master' s Thesis, Department of Electrical Engineering, The Ohio State University, Columbus, Ohio, USA. McBroom, Scot T. (1997). Toolkit for Tomorrow's Car. Technology Today, Southwest Research Institute Publications, Spring 1997 (also available at www.swri.org). Powell, B.K., K.E. Bailey, and S.R. Cikanek (1998). Dynamic Modeling and Control of Hybrid Electric Vehicle Powertrain Systems. IEEE Control Systems Magazine, October 1998, pp.
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
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 Jl711 standard, show potential improvement over other strategies that only optimize the ICE efficiency.
17-33 .
Takagi, T. and M. Sugeno (1985). Fuzzy Identification of Systems and its Application to Modeling and Control, IEEE Transactions on Systems, Man and Cybernetics, Vol. 15 No. 1, pp. 116-132. Wipke, Keith B., Matthew R. Cuddy, and Steven D. Burch (1999). ADVISOR 2.1: User-Friendly Advanced Powertrain Simulation Using a Combined BackwardIForward Approach. IEEE Transactions on Vehicular Systems, VoL 48 No. 6 , pp. 1751-1761.
ACKNOWLEDGEMENT This work was supported by the Partnership for a New Generation of Vehicles (pNGV) under contract to Oakland University.
88
ENERGY MANAGEMENT STRATEGIES FOR PARALLEL HYBRID VEHICLES USING FUZZY LOGIC
Niels J. Schouten X , Mutasim A. Salman·, and N aim A. Kheirx
xElectrical and Systems Engineering Department. Oakland University 102 Science and Engineering Building. Rochester. Michigan 48309, USA [email protected], [email protected] • General Motors Research and Development Center 30500 Mound Road, Warren, Michigan 48090-9055, USA [email protected]
Abstract: TIlis paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optiroize 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 optiroize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine. Copyright © 2000 IFAC Keywords: Hybrid Vehicles, Energy Management Systems, Fuzzy Control, Optimal Power Flow, Automotive Control
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 = 34 kmIl), 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 optiroize the architecture and components of the hybrid vehicle, but as important is the energy management strategy that is used to control the complete system.
1. INTRODUCTION The environmental challenges of the twenty-first 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.
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, et ai. , 1998) for a description of several other hybrid vehicle
Hybrid technology is one of the most proffilsmg research topics for the Partnership for a New
83
Table 1 Minimum Vehicle Performance Requirements Stated by PNGV 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
=:> Mechanical ;z::> Electrical Pov.er Fig. 1. Block Diagram of a Parallel Hybrid Vehicle configurations. PHV power controllers presented in the past (powell, et af., 1998; Ehsani, et al., 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 impiementation of the controller, fuzzy lo~ic ~ been used. Previous research at Oakland Uruverslty (lalil and Kheir, 1998) and Ohio State Unive.rsio/ (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 it is tolerant to imprecise measurements and to component variability.
12 s 23 s 5.3 s 140 ft (43 m) 17 ftls2 (5.2 m1s2 ) 6.5% 30% 100 mph (161 km/h)
controllers, such as the ICE, EM, battery, and transmission controllers. The specific PHV configuration, used throughout the paper, consists of tlIe following components: • Compression Ignition Direct Injection (CIDI) engine: 55 kW • Permanent Magnet motor: 20 kW continuous, 40 kW peak • Advanced battery: 40 kW, 2kWh • Automated manual transmission: 5 speed • Total test vehicle mass: 1100 kg The size of the components was chosen to achieve tlIe PNGV vehicle performance requirements given in Table l.
Section 2 of the paper, 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.
3. ENERGY MANAGEMENT STRATEGY This section describes the energy management strategy, which is the philosophy behind tlIe 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.
2. PARALLEL HYBRID VEHICLE BASICS Figure 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).
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 drivmg and charging tlIe battery. To determine tlIe optimal power split and how to optimize tlIe power generation/conversion of tlIe individual components, the efficiency maps of tlIe components were used.
For analysis and controller design, the PNGV Systems Analysis Toolkit (PSAT) mode.ls (McBroom, 1997) are used to simulate the PHV m the Matlab/Simulink environment (see (Wipke, et al., 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. !he driver' s objective is to track the speed versus tIme profile, by modulating the brake and ~ccel~rator pedals. The power controller uses tlIese driver mputs to compute tlIe commands for tlIe several local
3. J EffiCiency Maps Figure 2 presents a contour plot of tlIe efficiency of a generic CIDI engine in tlIe speed~torque p~e . Superimposed on the contour plot IS tlIe OptImal
84
Fig. 3. Block Diagram of Power Flow: Using ICE Directly (path 1); Using EM (path 2)
O L10~O----~2~070----~3~OO~--~4~OO~~ ICE speed (rad/s]
3.2 Power Split Strategy Now that the efficiency characteristics of the components are known, it is possible to formulate the power-split strategy. It is possible to use the ICE directly to drive the wheels (Figure 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 bilttery during discharging, to the EM used as a motor, to the wheels has to be analyzed.
Stale 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.
To optimize the PRY 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).
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 powersplit 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 optirnizing 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.
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 kW and over 50 kW, respectively), and for low vehicle speed (below 7 mph = 11 km/h).
Figure 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.
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 optirnized. The next section discusses the power controller that implements the energy management
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Table 2
1 2 3 4
5 6 7
8 9 Vehicle Speed
Rule Base of the Fuzzy Logic Controller
H SOC is too high then Pgen is 0 kW H SOC is nonnal and Pdriver is nonnal and O)EM is optimal then Pgen is 10 kW H SOC is nonnal and O)EM is not optimal then P gcn is 0 kW H SOC is low and Pdrivcr is nonnal and O)EM is low then P gen is 5 kW H SOC is low and P drivcr is nonnal and O)EM is not low then Pgen is 15 kW H SOC is too low then P gcn is Pgcn.max H SOC is too low then scale factor is 0 H SOC is not too low and P drivcr is high then Pgcn is 0 kW H SOC is not too low then scale factor is 1
Drive r Power Drive r Commandl Drive r Command Command • Interpreter SCC
•
Generator Power Fuzzy Logic Controller
Scaling Factor
ICE Power ICE and EM Power
EM Power
EM Speed
Fig. 4. Simplified Block Diagram of Power Controller strategy and uses fuzzy logic to compute the optimal generator power.
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 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 EMlICE speed and EMlICE 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.
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 Figure 5 for examples of membership functions that defme the degree of membership). The then-part of the rules of a TakagiSugeno 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, et al., 1995) for more infonnation on fuzzy control.
Once the driver power command is computed, the fuzzy logic controller (Figure 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.
Figure 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 three inputs of the fuzzy logic controller are driver power command (Pdriver), SOC and EM speed (O)EW . The membership functions (MF) for driver power command are presented in Figure 4 (top). Fuzzy set 'nonnal' represents the power range for
86
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Fig. 5. Membership Functions
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' 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. MFs for SOC (Figure 5, middle): 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' . MFs for EM speed (Figure 5, bottom): 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.
0 .7
Slate of Charge
O.g
Fig. 6. Operating Points. Top: efficiency map, optimal curve, and operating points of the combustion engine. Bottom: efficiency map and operating points of battery. is to be the driver power command added to the desired generator power (PlCE = Pdnver + Pgen ), and P EM is to be minus the desired generator power (PEM = -Pgen ) . There are two exceptions: 1. When P dnver + PEM,gen is smaller than the threshold value (s.f. * 6 kW), then PlCE = 0 kW, and PEM = Pdnver. 2. When P dnver + PEM,gen is greater than the maximum ICE power at current speed (PlCE,max@speed), then P lCE = PlCE,max@speed and PEM = P dnver - PlCE,max@speed. Finally, when P EM is positive (EM used as motor), P EM has to be multiplied by the scaling factor (PEM = PEM * s.f.) .
The rule base is presented in Table 2. If the SOC is too high (rule 1) the desired generator power (Pge.,) 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 (30-50 kW). Finally, when the SOC is not too low (rule 9), the scaling factor is to be one.
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.
5. SIMULATION RESULTS The controller has been simulated with PSAT using the test procedure described in the SAE J 1711 standard. Figure 6 presents the operating points of the
The third block in Figure 4 computes the final values for the ICE power (PlCE) and EM power (PEW, using Pdn vcr, Pgen, and the scaling factor (s.f.). PlCE normally
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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 Figure 6) was operated at a relatively high SOC (between 0.77 and the maximum 0 .9), and at relatively low power, both resulting in high efficiency.
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
The results have been compared with the results for the default controller in PSAT, which optimizes ICE operation only without considering the optimum region 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 PHY.
default
FLC
63.9 3.6 0 .62 13 .3 5 .0 6 .9 1.3 5.4 100
61.5 2.7 0.43 9.0 5.0 6 .9 1.4 5.4 92 .3
REFERENCES Ehsani, Mehrdad, Yimin Gao, and Karen L. Butler (1999). Application of Electrically Peaking Hybrid (ELPH) Propulsion System to a Full-Size Passenger Car with Simulated Design Verification. IEEE Transactions on Vehicular Systems, Vol. 48 No. 6, pp. 1779-1787. Jalil, Nashat and Naim Kheir (1998). Energy Management Studies for a New Generation of Vehicles (Milestone # 6: Fuzzy Logic for the Parallel Hybrid) , Technical Report, Department of Electrical and Systems Engineering, Oakland University, Rochester, Michigan, USA. Kaymak, u., R. BabuSka, H.R. van Nauta Lemke (1995). Fuzzy Control - Theory and Design, JournalA , Vol. 36 No. 3 . Kono, Hiroshi (1998). Fuzzy Control for Hybrid Electric Vehicles, Master' s Thesis, Department of Electrical Engineering, The Ohio State University, Columbus, Ohio, USA. McBroom, Scot T. (1997). Toolkit for Tomorrow's Car. Technology Today, Southwest Research Institute Publications, Spring 1997 (also available at www.swri.org). Powell, B.K., K.E. Bailey, and S.R. Cikanek (1998). Dynamic Modeling and Control of Hybrid Electric Vehicle Powertrain Systems. IEEE Control Systems Magazine, October 1998, pp.
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
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 Jl711 standard, show potential improvement over other strategies that only optimize the ICE efficiency.
17-33 .
Takagi, T. and M. Sugeno (1985). Fuzzy Identification of Systems and its Application to Modeling and Control, IEEE Transactions on Systems, Man and Cybernetics, Vol. 15 No. 1, pp. 116-132. Wipke, Keith B., Matthew R. Cuddy, and Steven D. Burch (1999). ADVISOR 2.1: User-Friendly Advanced Powertrain Simulation Using a Combined BackwardIForward Approach. IEEE Transactions on Vehicular Systems, VoL 48 No. 6 , pp. 1751-1761.
ACKNOWLEDGEMENT This work was supported by the Partnership for a New Generation of Vehicles (pNGV) under contract to Oakland University.
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