Energy and Battery Management of a Plug-In ... - Semantic Scholar

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011

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Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic S. G. Li, S. M. Sharkh, F. C. Walsh, and C. N. Zhang

Abstract—Fuzzy logic is used to define a new quantity called the battery working state (BWS), which is based on both battery terminal voltage and state of charge (SOC), to overcome the problem of battery over-discharge and associated damage resulting from inaccurate estimates of the SOC. The BWS is used by a fuzzy logic energy-management system of a plug-in series hybrid electric vehicle (HEV) to make a decision on the power split between the battery and the engine, based on the BWS and vehicle power demand, while controlling the engine to work in its fuel economic region. The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real battery in the loop. Simulation results are presented to demonstrate the performance of the proposed fuzzy logic energy-management system under different driving conditions and battery SOCs. The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging. Index Terms—Battery in the loop test bench, battery management, energy management, fuzzy logic, plug-in electric vehicle (EV).

N OMENCLATURE A Aα C CD D DG E FB FD FR

Vehicle frontal area. Accelerate and brake pedal value. Battery capacity. Drag coefficient. Vehicle transmission gear ratio ωM /ωD . Engine-generator gearbox drive ratio ωG /ωE . Battery open circuit voltage (OCV) or electromotive force. Brake force. Drive force. Rolling resistance force.

Manuscript received February 23, 2011; revised June 6, 2011; accepted July 26, 2011. Date of publication August 22, 2011; date of current version October 20, 2011. This work was supported in part by the Joint Training Ph.D. program of the China Scholarship Council. The review of this paper was coordinated by Mr. D. Diallo. S. G. Li was with the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China. He is now with Shaanxi Automobile Group Co., Ltd., Xi’an 710200, China (e-mail: [email protected]). S. M. Sharkh is with the Electro-Mechanical Research Group, University of Southampton, SO17 1BJ Southampton, U.K., and also with HiT Systems Ltd., SO17 1UA Southampton, U.K. (e-mail: [email protected]). F. C. Walsh is with the Energy Technologies Research Group, University of Southampton, SO17 1BJ Southampton, U.K. (e-mail: electro@ chemeng.fsnet.co.uk). C. N. Zhang is with the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TVT.2011.2165571

FW g I1 I2 IB J KE KT m PB PE PE∗ PG PM RW TD TE TG TM TMAX TMAXB u U1 U2 V VB η ηD ηM µ ρ ωE ωG ∗ ωG ωM

Wind resistance force. Gravitational acceleration. DC–DC converter input side current. DC–DC converter output side current. Battery current. Moment of inertia. Back-electromagnetic-force constant. Torque constant. Vehicle mass. Battery power. Engine power. Engine power demand. Generator power. Motor power. Wheel radius. Drive torque. Engine torque. Generator torque. Motor torque. Motor maximum torque. Maximum breaking torque that can be produced by the mechanical breaking system. Diesel engine serrated rod position. DC–DC converter input side voltage. DC–DC converter output side voltage. Vehicle velocity. Battery voltage. Battery Coulombic efficiency. DC–DC converter efficiency. Motor efficiency. Friction coefficient. Air density. Engine speed. Generator speed. Generator speed demand. Angular velocity of motor. I. I NTRODUCTION

I

N A plug-in hybrid electric vehicle (PHEV), the battery is directly charged when plugged into an electric power source, thus sharing the characteristics of both hybrid and battery electric vehicles (EV). Like the EV, it has the advantage of using greener off-peak electricity with the additional advantage of having a backup, quick-refueling engine to extend its range. There are many studies that demonstrate the advantage of a PHEV, compared with a conventional hybrid electric vehicle (HEV) in terms of emissions, fuel economy, and running cost [1], [2]. However, this is achieved at the expense of the extra

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cost of a larger battery, power electronics, additional power train components, and extra complexity. The size and cost of the engine, battery, and electric motor of a PHEV are strongly dependent on its electric range, overall range between charging, driving cycle, and the energy-management strategy used [3]–[5]. In addition to charging infrastructure challenges, including increased electricity distribution losses and voltage variations, which may be alleviated by coordinated charging [6]–[10], a PHEV will face two technical problems: The first is battery energy management, and the second is vehicle energy management. For battery energy management, the state of charge (SOC) is considered to be the most important parameter, which a vehicle control strategy could use as a reference. Most HEV energy control strategies use the battery SOC to make decisions on power sharing between the engine and the electric motor and battery while aiming to maintain the SOC within a defined range. Different methods have been used to evaluate the SOC of a battery, including ampere-hour counting, open circuit voltage (OCV)-ampere-hour counting, neural-fuzzy-logic-based methods [11], and Kalman-filter-based methods [12]–[14]. However, because the battery is a very complex nonlinear time-variable system and its capacity changes with temperature and aging, accurate estimation of the SOC is still not possible. A review of the state of the art of energy-management strategies or power flow control of PHEV is presented in [15]. The control strategies are broadly classified into optimization- and rule-based strategies. Optimization-based controllers consider past and, if possible, future driving conditions to determine the operation mode of a plug-in vehicle and the power split between the electrical energy system and the engine [16]–[20]. They have been shown to be better than rule-based controllers in terms of fuel economy and emissions. However, they can be more complex to implement in real time, particularly in the case of global optimization controllers that require the details of the future driving cycle. Rule-based controllers are classified in [15] as deterministic and fuzzy controllers. Deterministic rule-based controllers use a set of rules that are usually implemented using state machine logic [21], [22]. Although they have been successfully used in commercial HEVs, with the Toyota Prius power follower controller being a good example, they lack flexibility and the ability to deal with uncertainty. Also, they do not optimize the energy usage over the whole driving cycle. Fuzzy logic controllers (FLCs), which are good at dealing with model uncertainty and complex decisions, have been proposed by many researchers for vehicle control and energy management. FLC were used to determine the power split between the internal combustion engine (ICE) and battery packs to ensure that the engine operates in the high-efficiency or lowemission region. For example, [23] and [24] described the application of a fuzzy logic energy-management control method in the CJY6470 off-road PHEV. A Takagi-Sugeno fuzzy model was used to design the energy-management system for the vehicle to minimize fuel consumption. Simulation results were provided to demonstrate the performance of the system. The potential of using an FLC to optimize the control and calibration of a Toyota Prius is discussed in [25]. The paper

reviewed competitive strategies and calibrated the FLC to optimize the fuel efficiency and emissions of the Toyota Prius. The results demonstrated that the FLC was effective in reducing fuel consumption. A rule-based acceleration control strategy for electric vehicles to reduce the complexity of existing vehicle controllers is studied in [26]. An FLC was used for making system-level decisions and speed control. The results suggest that the FLC has better performance than conventional controllers. A fuzzyrule-based control strategy for a PHEV is presented in [27] to control the amount of energy flow to satisfy driver demand, optimize energy consumption, and reduce polluting emissions. A comparison between a conventional proportional–integral– differential (PID) controller and an FLC method to control a PHEV powered by an induction motor and an ICE is provided in [28]. Simulation results for different road driving cycles were presented to demonstrate the robust fast dynamic performance of the FLC. In [29] and [30], an FLC was used to increase fuel economy and decrease emissions in a PHEV. The FLC was used to realize an electric assist control strategy, which was shown to result in improved fuel economy and decreased CO and NOx emissions. Reference [31] introduces an FLC method used in a fuel cell electric vehicle to determine the power split between the fuel cells, battery, and ultracapacitor. Real vehicle test data are provided. The data show that the FLC could achieve the target power split while maintaining the battery SOC within a specified range. Hajimiri and Salmasi [32] proposed using FLC in conjunction with road traffic information from Global Positioning System to develop a control strategy to improve vehicle performance, fuel consumption, and emissions is proposed. In [33], fuzzy logic was also used for power split management in a series HEV, and the controller was tested using the Auto BoxdSPACE platform. Wang and Yang [34], [35] introduced the so-called evolutionary fuzzy design method, which optimizes the fuzzy rules using a genetic algorithm. The method was used to design a parallel hybrid vehicle controller; genetic algorithms were used for fine-tuning the parameters of the FLC. A genetic-fuzzy control strategy for a PHEV was also used to optimize fuel efficiency and reduce emissions in [36]. The parameters of the FLC were optimized using a genetic algorithm. Similarly, a comparison between a nonoptimized FLC and an optimized FLC with a genetic algorithm was reported in [37] and [38], and the results showed that the optimized FLC could achieve better performance. Neural networks and FLC were combined together to give a fuzzy-neural network power management strategy for an HEV in [39]. A comparison between the fuzzy-neural network power management strategy and the FLC strategy was presented. The simulation results showed that the fuzzy-neural network power management strategy has good self-adaptive ability and can improve fuel economy and emissions. A significant proportion of the work described in the literature on vehicle and battery management was based on the vehicle simulation software Advisor to produce offline simulation results. The SOC, which is a very important battery parameter,

LI et al.: ENERGY AND BATTERY MANAGEMENT OF PLUG-IN SERIES HEV USING FUZZY LOGIC

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charge the battery as an alternative to using electricity from the engine-generator system. B. PHEV Control Method

Fig. 1.

Plug-in series HEV structure.

is used to make control decisions, as mentioned earlier. Generally, it is not a problem to use the SOC as a reference for battery management in virtual simulation software, because its initial value SOC0 could be set at the start of the simulation and the relationship between battery voltage and the SOC can be assumed. The SOC can be readily calculated accurately using the ampere-hour counting method and the assumed SOC0 . However, in a real battery under real operating conditions, the initial SOC0 is difficult to accurately determine, which will cause inaccuracies in later estimates. In addition, if the estimated SOC is higher than the real value, a controller may order a battery to discharge, even though the real SOC had reached its permissible minimum value, thus causing the battery to be overdischarged, with negative impact on its state of health [40], [41]. In this paper, an FLC method is applied to control the energy flow in a plug-in series HEV. FLC is used to make decisions on the power split between the two energy sources, i.e., the battery and the ICE based on a novel conceptual quantity called the battery working state (BWS), taking into account both SOC and battery terminal voltage, thus avoiding over-discharge of the battery when the SOC estimate is inaccurate. The enginegenerator system, which turns the chemical energy of the fuel into electric energy, is also under FLC to ensure that it operates inside the fuel economic region. A real-time battery-in-the-loop (BIL) HEV simulation system is used to optimize and evaluate the performance of the proposed FLC using the BWS. The novelty of the proposed control system stems from its structure using the BWS, as well as the design of the fuzzy degree of membership (DOM) functions that include asymmetry and uneven distribution to reflect the practical constraints of the system components. II. P LUG -I N H YBRID E LECTRIC V EHICLE S TRUCTURE AND C ONTROL M ETHOD A. Structure of the PHEV Fig. 1 shows the configuration of the plug-in series HEV investigated in this paper. The engine (E) consumes fuel (F) and drives an electric generator (G) to provide electric power to a power electronic converter (P) to supply a common dc bus supplying the battery (B) and the driving electric motor (M) through a motor controller (within P). The motor is coupled to the wheels (W) through a transmission (T). There is no direct mechanical connection between the engine and the wheels. The vehicle battery charger (Pc), which contains a power electronic ac–dc converter, could be plugged into a source of electricity to

There are several energy-management strategies for plugin vehicles, as classified in [3] and [15]. In this paper, we propose a fuzzy-rule-based blended electric-dominant energymanagement strategy. The battery is charged mainly when the vehicle is plugged into the electricity supply network, which reduces fuel cost and potentially improves overall energy efficiency, if the electricity is supplied from a renewable source. An FLC uses the vehicle power demand and the BWS (see Section IV) to determine the power split in a blended mode, which is similar to a charge-depleting mode but with power supplied mainly from the battery and assisted by the engine. When the BWS (and, hence, the SOC) reaches a set low level, the engine generator will take over from the battery as the main power source, with the battery meeting excess power peaks, i.e., the two sources operate together in a charge-sustaining mode. The BWS will be held near its lowest permissible level, storing just enough energy to meet the peak power demands. III. V EHICLE S YSTEM M ODEL A. Battery Model The battery SOC is estimated using the OCV-ampere-hour counting method in (1) SOC = SOC0 + η IB dt/C where η is the Coulombic efficiency of the battery, and its value was assumed to be 1 during discharge and 0.98 during charge [42]. The battery initial SOC0 was estimated according to the OCVE; the battery was left for 24 h before measuring the OCV, and accordingly, the initial SOC was determined. The SOC–OCV relationship for the battery used in this study is given in E = 1.262SOC + 11.5708.

(2)

The following states the expression for battery power for later reference in the paper: PB = IB VB .

(3)

B. Motor/Controller Model The characteristics of a permanent-magnet motor with a direct torque controller were used in the virtual vehicle simulator. Since the response time of the motor/controller, which is of the order of milliseconds, is much faster than the mechanical time constant of the vehicle and mechanical power train, the electric motor transient response could be neglected. The steady-state model based on the system output characteristics experimentally obtained was therefore used [43]. Fig. 2 shows the output torque–speed characteristics and efficiency map of the motor/controller system. The output torque of the motor/controller system is determined by the driver’s action on the pedal signal Aα ∈ [−1, 1].

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Fig. 4.

Generator system.

The output torque of the engine is controlled according to the different serrated rod positions (u ∈ [0, 1]). Parameters ai and b were obtained using a Matlab least-square method to fit a sixthorder polynomial curve to the experimental data. The values of the polynomial coefficient are given in Table IV. A simple power balance approach is used to model the generator system as shown in Fig. 4 to enable fast numerical simulations in real time with BIL. The alternator, rectifier, and dc–dc converter behave like an ideal current source that supplies current to the dc bus. The machine back torque is proportional to the dc–dc converter’s input side current. Neglecting mechanical losses in the gearbox, the engine torque TE drives the generator according to

Fig. 2. Motor/controller output characteristics.

TE dωG , − TG = J DG dt Fig. 3. Engine maximum torque–speed characteristics.

Aα > 0 designates an acceleration command, and Aα ≤ 0 designates a deceleration command. The motor/controller subsystem in the virtual model calculates the maximum output torque TMAX for the given angular velocity ωM and then calculates motor torque TM and power PM according the pedal signal, as given by TM = Aα TMAX (ωM ) PM = T M ω M .

(4) (5)

Power PM , i.e., the vehicle’s drive power demand, determines battery power PB and generator power PG according to (PB + PG )ηM , Aα ≥ 0 PM = (6) Aα < 0. −PB /ηM , The positive vehicle power demand is provided by the battery and engine generator. Negative power is charged into the battery during regenerative braking. C. Engine-Generator Model A diesel turbo engine-generator model is used for the purpose of producing electric power to the dc bus. In this paper, experimental data are used to model the turbo diesel engine output torque under different speed and serrated rod positions [44]. Fig. 3 shows the maximum output torque versus speed and the fitted curve given in 6 i ai ωE + b . (7) TE = u i=1

DG =

ωG ωE

(8)

where DG is the gear ratio between the engine and the generator. The generator counter torque TG has two components, as shown in TG = I1 KT + ωG µ.

(9)

The first component is the electromagnetic torque, which is proportional to the current, and the second is the friction component, which is assumed to be proportional to speed. As the winding resistance is relatively very small, the resistance voltage drop is neglected, and the rectified output of the generator is assumed to be proportional to the speed, as shown in U1 = KE ωG .

(10)

The generator ohmic power losses are allowed for in the converter efficiency ηD in U1 I1 ηD = U2 I2 .

(11)

D. Vehicle Drive Frame Model The power from the motor/controller is transmitted through a gearbox with a ratio D = ωM /ωD to decrease speed and increase torque. Motor torque TM transfers to the wheels to provide drive force FD to accelerate the vehicle according to TD = TM D FD = TD /RW .

(12) (13)

On a level road, the vehicle has to overcome rolling resistance force FR and wind resistance force FW . The vehicle


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dynamic model is described by [45] mV˙ = FD − FR − FW FR = mg(0.0076 + 0.0002V ) 1 FW = CD ρAV 2 . 2

(14) (15) (16)

It is assumed during the simulations that there is no headwind in (16). Once the brake pedal is pressed, the motor/controller starts to work as a generator to provide braking torque and to regenerate part of the braking energy to be stored in the battery. Mechanical braking force FB is determined according to the magnitude of braking pedal signal Aα , as given in FB = −Aα TMAXB /RW .

(17)

TMAXB is the maximum braking torque that can be produced by the mechanical braking system. The rest of the breaking torque will be provided by regenerative breaking of the motor, and accordingly, the total breaking force is given by FD = TD /RW + FB .

(18)

Note that torque TD in (18) is negative. IV. D ESIGN OF THE F UZZY L OGIC C ONTROLLERS A. BWS The SOC of a battery is one of its most important parameters, because it not only informs a driver about the amount of remaining charge and mileage but also is a parameter that needs to be carefully monitored to avoid damage that can be caused by overcharging or over-discharging the battery. Unfortunately, it is a difficult quantity to accurately estimate as it not only depends on temperature, past usage history, and aging, but it is also subject to uncertainties introduced by SOC calculation algorithms based on ampere-hour accumulation and associated accumulation errors. The terminal voltage of a battery is another important parameter that can also be used to indicate battery SOC. For example, the terminal voltage of many types of battery is normally used as an indicator of the SOC during constant current charging or discharging, e.g., lithium battery terminal voltage is typically limited to be in the range of 4.2–3.0 V. The BWS quantity uses both the SOC and the terminal voltage to give a composite battery state. The introduction of the BWS to battery management could overcome the disadvantage of using the SOC only, which, if it were overestimated, could lead to over-discharging the battery. The basic idea of the BWS is to add an additional limit on battery discharge based on the measured terminal voltage. The relationship between the BWS and terminal voltage and SOC can be formulated using a set of fuzzy logic rules. Using fuzzy logic allows for the uncertainty arising from not taking the variation of current and equivalent series resistance of the battery into account. Fig. 5 shows the DOM function of the inputs (SOC and terminal voltage) and output (BWS). A Gaussian curve membership function was used for the three levels of SOC between 0.2 and 0.8. Two trapezoidal membership functions were used for SOC < 0.2 and SOC > 0.8, where the SOC could be readily

Fig. 5. Membership function of SOC, voltage, and BWS.

identified as being very low or very high. For the battery voltage, two trapezoidal membership functions were used when the voltage is high or low, and an asymmetric trapezoidal membership function was used for the middle voltage area, which corresponds to the battery’s nominal working voltage range. The asymmetry of the membership function, which was determined based on experimental observations, was designed to ensure that, once the voltage reaches its lower limit, the FLC will strongly stop the discharge; the left side of this asymmetric function is more abrupt, which makes the FLC more sensitive when the BWS is close to its lower limit. The membership function for the BWS is similar to that used for the SOC. Table I shows the rules used to relate the BWS to voltage and SOC. Because the voltage is directly measured but the SOC is estimated, the rules in Table I are therefore skewed in favor of preferring the voltage measurement. For example, when the voltage is L, the highest BWS value is ML, even when the estimated SOC is H. When the voltage is H, we would rather believe that the BWS is MH, even if the SOC is L. When the

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TABLE I BWS F UZZY RULES

inaccurate curve or point B on the accurate curve, thus reducing further damage to the battery due to over-discharge; the SOC at B is at least 0.1 larger than that at point C, which means that the BWS-based control strategy in this case has resulted in avoiding at least 10% capacity over-discharge. B. Fuzzy Control of Power Split

Fig. 6. BWS working contour map and constant current discharge curves.

battery voltage is M, i.e., within the nominal range, the SOC value is relied on more to decide the value of the BWS. For example, when the voltage is M and the SOC is L, the BWS is set to be VL, and when the SOC is ML and the voltage is L, the BWS is set to be ML. Generally speaking, the value of the BWS tends to be conservative: We only trust the estimated SOC values when the voltage and the SOC are within the safe range. Otherwise, we trust the measured voltage values. Using these fuzzy rules and membership functions, a deterministic relationship between the BWS and the SOC and voltage can be derived, as shown in Fig. 6. During the blended charge-depleting mode, the battery will discharge to a set low level, and then, the engine generator and the battery will work together in charge-sustaining mode, as discussed earlier. However, as mentioned earlier, the SOC may be overestimated, and the battery may easily over-discharge. Using the BWS contour in Fig. 6, we could overcome this problem. To illustrate how this can be achieved, two constant current discharge curves (labeled as “Accurate” and “Inaccurate”) are plotted over the BWS contour map. The “Accurate” curve represents the accurate SOC–voltage characteristic, and the other is an overestimated inaccurate SOC–voltage characteristic. The minimum BWS is assumed to be 0.3, which corresponds to an actual SOC of 0.3. On the accurate SOC–voltage curve, when the battery reaches point A (SOC = BWS = 0.3), the blended charge-sustaining energy-management strategy will let the battery provide top-up power only during short peak power demand periods. However, if the SOC was overestimated to be at A , instead of its actual value A, then a charge-depleting management strategy based only on SOC will continue to discharge the battery to point C on the inaccurate SOC-voltage curve. This corresponds to point C on the actual SOC–voltage curve, thus resulting in a severe over-discharge of the battery because the real SOC is actually already lower than 0.2. However, using the BWS limit of 0.3, the battery discharge will be limited to point B on the

The absence of a mechanical link between the wheels and the engine in a series PHEV allows a control strategy that ensures that the engine operates in its high-efficiency region. This feature is exploited in the fuzzy logic energy-management strategy proposed in this paper. The FLC (see FLC1 in Fig. 9) decides the power split between battery and engine-generator system based on the vehicle power demand and BWS as follows: 1) When the BWS is high and the vehicle power requirement is low, only the battery is used to supply power. 2) When the BWS is high and the power requirement is also high, the engine will provide most of the power. 3) When the BWS falls and starts to approach the lowest limit (0.3 in this case), the battery will only be used to supply top-up power during short peak power demands that are higher than that available from the engine. 4) When the BWS falls below 0.3, the engine generator will be used to charge the battery back to BWS = 0.3 or slightly higher. The battery will only provide top-up power if necessary, and the engine generator will provide most of the vehicle power demand. Fig. 7 gives the DOM functions for power requirement, BWS, and engine-generator power. Two trapezoidal membership functions are used at either end of the demanded power range PM , and five Gaussian curve membership functions are used in the range of 20–80 kW. For the BWS, two trapezoidal membership functions are used when BWS is either high or low. In the middle region of the BWS DOM plot in Fig. 7, we use three Gaussian curve membership functions, which are not defined to be as evenly distributed. They are relatively narrow at M, because this makes the FLC sensitive when the BWS is near its low limit of 0.3. For engine target power, a narrow triangular membership function is used when the engine-generator target power is zero. Two trapezoidal membership functions are used at either end of the engine-generator target power range of 20–80 kW; in the middle of that range, four Gaussian curve membership functions are used. Table II gives the fuzzy logic rule base used to determine the generator output power, given the overall power requirement and BWS. The objectives of the rules in the table are as follows: 1) Decide the engine-generator power, and if the power is more than the vehicle’s need, then it will be saved in the battery; otherwise, the battery will provide top-up power. 2) Maintain the BWS at 0.3 when it falls to this level, or charge the battery and hold its BWS at the 0.3 level if it is lower than that to start with. For example, when the BWS value is VL and the vehicle power demand is VL, the engine-generator target power will be L; this could satisfy the vehicle power requirement and give a charge to the battery. However, if the BWS is high enough at say MH or VH, and the vehicle power requirement is VL or ML, all the power will be provided by the battery. When the vehicle power requirement is as high as H or VH, then, regardless of the


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Fig. 8. Engine speed–power and speed–torque fuel consumption maps. (a) Speed–power fuel map. (b) Speed–torque fuel map.

discharge. If the BWS is VL or ML, the battery will be charged, and if the BWS is M, then it will be maintained at that level. Fig. 7.

∗. DOM functions of PM , BWS, and PE

TABLE II F UZZY RULES FOR E NGINE TARGET P OWER

value of the BWS, the engine-generator target power will be set to VH. Here, we need to state that, under this circumstance, the battery still needs to provide short-term top-up power during peak demands if its BWS is VL or L. That is because all the rules first need to satisfy the driver’s desire. However, this action is not encouraged, and in a real car, a warning may be given to encourage the driver to avoid this situation. When the vehicle’s power demand is in the range of ML–MH and the BWS is MH or VH, the rules in Table II will let the battery

C. Engine-Generator Control Fig. 8 shows the engine speed–power and speed–torque fuel consumption maps. A Mercedes Ominous 611, 2.2L, with a maximum power of 92 kW at 4200 rev/min was assumed in this study. The contours in the figures are fuel consumption curves representing fuel mass consumed by the engine in grams per kilowatt-hour. The data were obtained from the library of the electric vehicle simulation software Advisor 2002. The aim is to control the engine speed such that the output power follows the optimal curve inside the most fuel economic region. The structure of the proposed controller of the enginegenerator system is shown in Fig. 9. The system controls the output power of the generator to follow the set power demand from the power split controller FLC1 described in the previous section. Two feedback loops are provided; one is for generator torque, and the other is for engine output power. The engine target power will have a unique relationship with its target speed, as provided by the optimal curves in Fig. 8. The generator model will feed back a resistive torque to the gear

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Fig. 9. Engine-generator system control structure. TABLE III E NGINE -G ENERATOR C ONTROL F UZZY RULES

Fig. 10. Engine control membership function.

box, and the engine will produce a drive torque to make the generator reach its target speed. A PID controller, which uses the generator speed error as input, is used to adjust the enginegenerator speed by adjusting its output power. The gearbox model will feed back the actual engine power, and the error between the engine target power and actual power is used as the input to FLC2. FLC2 will increase or decrease the serrated rod position to control the engine output torque, thus adjusting the engine-generator speed and power.

Fig. 10 shows the proposed DOM functions used in the engine-generator FLC2. In controller FLC2, we use both the power error and the power error rate to adjust the position of the engine serrated rod. Two Gaussian membership functions and a Gaussian curve membership function were used to define the power error in the range of −10–10 kW. The power error rate DOM functions are similar to those of the power error, with the range being −20–20 kW/s. The output, which is divided into seven levels, is used to control the serrated rod position increment from NH (which means a need to greatly decrease) to H (which means hold) to PH (which means greatly increase). Table III gives the engine-generator fuzzy control rules. For example, when the engine power error is N , which means that the output power is larger than the target and the error is negative, then there are three possible scenarios: 1) The error rate is D, which means that the error is increasing at a large rate: During this time, we need to greatly decrease the serrated rod position increment to NH. 2) The error rate is H, meaning that the error is nearly constant: We choose the serrated rod position increment NM to decrease the output. 3) The error rate is U, meaning that the error is decreasing: We choose the serrated rod position increment NL. The rules are reversed when the engine power error is P , i.e., when the output power is smaller than the target and the error is positive. During this time, all the rules will, in effect, increase the position of the serrated rod. When the engine power error is Z, i.e., very small, then we decrease, hold, or increase the serrated rod position according to the error rate from D to H to U. V. O FFLINE S IMULATION Offline simulations were carried out to test the FLC control strategy for two driving cycles, i.e., the USA Urban Dynamometer Driving Schedule (UDDS) and the New European Driving Cycle (NEDC). Fig. 11 shows the driver’s demand on the acceleration/braking pedal for both cycles, which was obtained from the derivative of speed specified in these cycles. The vehicle speed in two cases and the corresponding powers


Fig. 11.

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Pedal signal, velocity, and power in UDDS and NEDC driving cycles. TABLE IV VALUES OF M ODEL VARIABLE

are also provided as shown in the figure. As expected for these driving cycles, the vehicle will experience very rapid acceleration and deceleration, as well as rapid speed and power changes, which will be challenging for the FLC. A virtual model of the vehicle and battery using Matlab/Simulink SimPower Systems was used for the offline simulation to test the BWS concept used for the FLC control strategy. The values of the model variables are shown in Table IV.

Fig. 12. Battery SOC, current, and voltage obtained from simulations, assuming that the SOC was accurately estimated. (a) Results during four UDDS cycles. (b) Results during six NEDC cycles.

A. SOC Estimated Accurately Four cases were studied, assuming that the SOC was accurately estimated. In the first and second cases in Fig. 12(a), the initial battery SOC is set to be 0.4 and 0.3, respectively, at the start of four UDDS cycles. When the initial SOC is 0.4, it continues to decrease (charge depleting mode) until it reaches the set lowest level of approximately 0.33, where it is held during the charge-sustaining mode. When the initial SOC is 0.3, it increases cycles until it recovers to 0.33, and it is then held at that level like the first case. In the third and fourth cases, as shown in Fig. 12(b), the vehicle experiences six NEDC cycles, starting with battery initial SOC of 0.4 and 0.3, respectively. Battery currents in UDDS and NEDC are significantly different. However, the same SOC results are achieved, and the battery SOC is held at the designed low level at the end of the chargesustaining mode. Not only are the SOC curves similar in the four cases, but the voltage levels are also kept within similar limits as a result of the action of the BWS, which uses both the SOC and voltage together.

Fig. 13. SOC graphs during four UDDS cycles obtained from simulations, assuming that the SOC was inaccurately estimated. The actual initial SOC in all cases is 0.33. The top graph assumes that the initial SOC was inaccurately estimated to be 0.43, the middle graph assumes that the initial SOC was inaccurately estimated to be 0.48, and the bottom graph assumes that the initial SOC was inaccurately estimated to be 0.53.

B. Inaccurate SOC Estimates In Fig. 13, the SOC was assumed to be inaccurate during a simulation of four UDDS cycles. The actual initial SOC

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was set to 0.33 in all cases. Three cases were considered with increasing degrees of SOC inaccuracy from the top to the bottom graph. The initial inaccurate values of the top, middle, and bottom graphs are 0.43, 0.48, and 0.53, respectively. The correct values of SOC are shown on the right vertical axes, and the inaccurate SOC estimates used in the power split controller FLC1 in Fig. 9 are on the left vertical axes of the graphs. The solid curves show the SOC of the battery when the BWS (using the inaccurate SOC estimates) was used to determine the power split. In all cases, the actual SOC is prevented from discharging below 0.25. The dashed curves show the SOC of the battery when inaccurate SOC values were used to determine the power split without taking the battery voltage into account (i.e., the BWS signal in Fig. 9 was replaced by the inaccurate SOC signal). In all cases, the battery continues to discharge well beyond the safe minimum SOC level, and the higher the degree of inaccuracy of SOC estimates, the deeper the over-discharge of the battery. The results demonstrate that using the BWS as a battery index for control strategy to split the power is more effective in preventing battery overdischarge than using the SOC alone when the SOC estimate is erroneous. Fig. 14 shows the battery current and voltage waveforms corresponding to the SOC graphs in Fig. 13. In Fig. 14(a), we can see that the battery current range increases when the SOC error increases. In Fig. 14(b), the corresponding voltage will be significantly different for each SOC case. When the power split controller uses the inaccurate SOC, the average battery voltage is held approximately at 11.5 V in the top graph. In the second graph, it is held at 11.2 V, but in the third graph, it continues to fall. However, when the power split controller uses the BWS, the average battery voltage is not allowed to fall beyond 11.6 V in all cases, regardless of the degree of error in the SOC estimate. Similar simulations were also carried out for the NEDC cycle, as shown in Figs. 15 and 16. Again, in this case, using the BWS proved to be more effective than using SOC estimates at preventing battery over-discharge.

Fig. 14. Battery current and voltage during four UDDS cycles corresponding to the SOC graphs in Fig. 13. The actual initial SOC in all cases is 0.33. The top row of graphs assumes that the initial SOC was inaccurately estimated to be 0.43, the middle graphs assume that the initial SOC was inaccurately estimated to be 0.48, and the bottom graphs assume that the initial SOC was inaccurately estimated to be 0.53. (a) Battery current. (b) Battery voltage.

VI. BATTERY- IN - THE -L OOP S IMULATION A. BIL Simulation System The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real BIL. The test bench comprises a computer running a virtual vehicle model and the FLC, a lead acid battery, dc load, and dc power source, as shown in Fig. 17. The equipment used in Fig. 17(a) was given as follows: 1) power source: Chroma 62024P, with a maximum voltage of 80 V, maximum current of 60 A, and maximum power of 2.4 kW; 2) power load: Chroma 63201, with a maximum voltage of 80 V, maximum current of 300 A, and maximum power of 2.6 kW; 3) battery: Genesis Pure-lead XE, with 12 V, 69 A h, and 400 cycles with 80% depth of discharge (DOD).

Fig. 15. SOC graphs during six NEDC cycles obtained from simulations, assuming that the SOC was inaccurately estimated. The actual initial SOC in all cases is 0.33. The top graph assumes that the initial SOC was inaccurately estimated to be 0.43, the middle graph assumes that the initial SOC was inaccurately estimated to be 0.48, and the bottom graph assumes that the initial SOC was inaccurately estimated to be 0.53.


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Fig. 16. Battery current and voltage during six NEDC cycles corresponding to the SOC graphs in Fig. 15. The actual initial SOC in all cases is 0.33. The top row of graphs assumes that the initial SOC was inaccurately estimated to be 0.43, the middle graphs assume that the initial SOC was inaccurately estimated to be 0.48, and the bottom graphs assume that the initial SOC was inaccurately estimated to be 0.53. (a) Battery current. (b) Battery voltage.

The virtual vehicle model and FLC were built in the Simulink/Real-Time Workshop, which provides a standard interface that can work in real time to control the battery charging and discharging equipment. Arrows in Fig. 17(b) represent the signals between different modules. The numbering and definition of each one are given as follows: 1 is acceleration signal, 2 is braking signal, 3 is braking torque, 4 is speed, 5 is motor torque, 6 is vehicle drive force, 7 is motor efficiency, 8 is vehicle power demand, 9 is engine power, 10 is BWS, 11 is serrated rod control, 12 is SOC, 13 is battery voltage, 14 is PG , 15 is current command, and 16 is the equipment initialization command. The simulation sequence is given as follows: the pedal signal sets a driving torque or braking torque demand. The torque goes through the CVT or braking system to the vehicle dynamics. The battery voltage is downloaded from the programmable dc load via an RS232 interface through which the discharge/charge commands are sent. The battery management module uses the battery voltage and battery current to calculate the SOC and BWS. Then, the BWS is sent to the power split FLC1. FLC1 uses the BWS and power demand to make a decision on how

Fig. 17. BIL simulation system. (a) BIL simulation system. (b) BIL simulation system structure.

much power the engine generator needs to provide. The engine generator will then be under the control of FLC2 to work in its high-efficiency region. The engine-generator power varies according to the vehicle power demand, and excess energy is accepted or provided by the battery. The battery management system communicates the battery power demand (voltage and current) to the charger or load, via the real-time instrument module, to the standard serial communication ports of the personal computer. The values of the model variables are shown in Table IV.

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Fig. 18. Battery power.

Fig. 19. Generator output power.

Fig. 20.

Battery current.

Fig. 21.

Evaluated battery SOC.

Fig. 22.

Battery voltage.

B. BIL Experimental Results Real-time simulations were carried out to test the system and FLC. A rigid acceleration/breaking pedal input, which was determined from the UDDS by taking the derivative of the speed as shown in Fig. 11, was used to drive the vehicle model. Fuzzy logic was used in real time to determine the BWS and the power split. Two cases were tested: one starting with SOC0 of 0.4 and another starting with SOC0 of 0.3. The initial SOC was evaluated according to the OCV, as discussed earlier. For the two cases, the vehicle speed and power demand were the same, but the battery and engine power output were significantly different because of the BWS influence on the power split. The FLC makes decisions to control the battery and engine generator to give sufficient power to satisfy the power demand in Fig. 11. Fig. 18 shows the battery power, and Fig. 19 shows the power produced by the engine-generator system. The FLC is clearly able to rapidly respond in real time to rapid changes in power demand.

Figs. 20–23 show battery current, SOC, terminal voltage, and BWS, respectively. The battery charge/discharge current was limited because of the limitations of the equipment. The battery SOC in Fig. 21 was calculated according to (1), and the battery


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Fig. 23. Evaluated BWS.

voltage in Fig. 22 is directly measured across its terminals. The FLC considers both the SOC and voltage to evaluate the value of the BWS in Fig. 23. For an SOC0 of 0.4, we see that, when the BWS approaches the cutoff value of 0.3, the engine generator increases its power, and the average battery voltage stays mainly above 11.6 V. This means that, when the battery SOC is overestimated, the FLC using the BWS will not overdischarge the battery, as discussed earlier. In Fig. 23, for an SOC0 of 0.3, the FLC has also detected that the voltage was low. Thus, the battery is initially charged until the BWS reaches 0.3. It is then held at that level to ensure that the battery can meet short-duration peak power demands. In Fig. 22, we see that, 600 s later, the battery voltage is held just above 12 V, despite several short charges and discharges. In Fig. 23, we observe rapid narrow pulse changes on the BWS curves. These occur during severe large current charging or discharging events. Following an abrupt fall of the BWS due to a severe discharge, the FLC responds by increasing the engine power demand to charge the battery. Fig. 24 shows the engine power and torque operating maps. When SOC0 is 0.4, we see that the battery was the main power source; thus, the working points are fewer than when SOC0 is 0.3. We also see that the FLC ensures that the engine is working near the optimal curve.

VII. C ONCLUSION A new quantity called the BWS, which is related to the SOC and terminal voltage using fuzzy logic, has been proposed together with a fuzzy logic energy-management strategy. Their effectiveness has been evaluated using a real-time virtual vehicle simulator with BIL. The results have shown that the BWS is effective in preventing battery over-discharge when the evaluated SOC is erroneous. The fuzzy logic energy-management controller has also demonstrated the capability of maintaining the engine working in its fuel economic region in a plug-in series HEV.

Fig. 24. Engine power and torque operating maps. (a) Power–speed fuel map. (b) Torque–speed fuel map.

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S. G. Li received the B.S. degree in vehicle engineering from Chang’an University, Xi’an, China, and the Ph.D. degree in energy/battery management of electric vehicles, majoring in vehicle engineering, from the Beijing Institute of Technology, Beijing, China, in 2011. He was a Joint-training Ph.D. student from 2009 to 2010 with the University of Southampton, Southampton, U.K., and he was supported by the China Scholarship Council. He is currently researching new energy electric vehicles with Shaanxi Automobile Group Co., Ltd., Xi’an.

S. M. Sharkh received the B.Eng. and Ph.D. degrees in electrical engineering from the University of Southampton, Southampton, U.K., in 1990 and 1994, respectively. He is currently a Senior Lecturer and Head of the Electro-Mechanical Research Group, University of Southampton. He is also the Managing Director of HiT Systems Ltd., Southampton. He has published more than 100 papers in academic journals and conference proceedings. His research interests are control, electrical machines, and power electronics, with applications to electric vehicles. Dr. Sharkh is a member of the Institution of Engineering and Technology. He is a Chartered Engineer. He was the recipient of The Engineer Energy Innovation Award in 2008 for his work on rim-driven thrusters and marine turbine generators.


F. C. Walsh received the B.Sc. degree in applied chemistry from the University of Portsmouth, Portsmouth, U.K., the M.Sc. degree in materials protection from the University of Manchester, Manchester, U.K., and the Ph.D. degree in electrochemical engineering from the University of Loughborough, Loughborough, U.K. He is currently a Professor of electrochemical engineering and the Deputy Head (Enterprise) of the School of Engineering Sciences, University of Southampton, Southampton, U.K. He directs the research activities of the Electrochemical Engineering Laboratory, Energy Technology Research Group, University of Southampton, and is a member of the steering group of the National Centre for Advanced Tribology, University of Southampton, and the Materials Engineering Research Group. Previous positions have included Head of Chemical Engineering with the University of Bath, Bath, U.K.; Head of Pharmacy and Biomedical Sciences with the University of Portsmouth; and Industrial Chemical Engineer. He is a NonExecutive Director of Poeton Industries Ltd. (surface finishing) and a Member of the Scientific Advisory Boards of ACAL Energy Ltd. (fuel cells) and ITI Energy Ltd. (redox flow cells and materials for energy). He has been a Visiting Professor with the University of Wollongong, Wollongong, Australia, and holds a Visiting Chair in Electrochemical Technology with the University of Strathclyde, Glasgow, U.K. In collaboration with industry, he has developed or improved more than 50 industrial electrochemical processes, primarily in the areas of energy conversion, environmental treatment, corrosion control, and materials recycling. He has authored or coauthored four text books, 70 short course papers, more than 200 conference presentations, more than 300 research papers, and more than 50 educational papers. His research interests include energy conversion, electroactive nanomaterials, coating technology, electrochemical monitoring and sensors, corrosion, surface finishing, and electrochemical process engineering. Dr. Walsh is a Fellow of the Royal Society of Chemistry, the Institute of Mining, Materials and Metallurgy, the Institute of Metal Finishing, the Institute of Corrosion, the Higher Education Academy, and National Association of Corrosion Engineers (NACE); an NACE International Certificated Corrosion Specialist; and a Member of the Electrochemical Society and the International Society of Electrochemistry. He is a Chartered Chemist, Environmentalist, Scientist, and Engineer. He was the recipient of the Westinghouse Prize in 1999, 2009, and 2011 (Best Paper on Metallic Coatings) and the Johnson Matthey Silver Medal of the Institute of Metal Finishing (2007), together with the Breyer Medal of the Royal Australian Chemical Institute (2000) for international contributions to electrochemical engineering and energy.

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C. N. Zhang received the M.E. degree in control theory and control engineering and the Ph.D. degree in vehicle engineering from the Beijing Institute of Technology, Beijing, China, in 1989 and 2001, respectively. He is currently a Professor and Vice Director of the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. He has published more than 70 papers and two books. He is the holder of ten patents. His research interests include electric vehicles, vehicular electric motor drive systems, battery management systems, and chargers. Dr. Zhang twice received the second prize from the National Technological Innovation Awards (2004 and 2009).