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The structure and control method of hybrid power source for electric vehicle
Energy 112 (2016) 1273e1285
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Energy journal homepage: www.elsevier.com/locate/energy
The structure and control method of hybrid power source for electric vehicle Maobing Li a, 1, Hui Xu d, 1, Weimin Li c, d, *, Yin Liu a, b, 1, Fade Li a, **, Yue Hu d, 1, Li Liu e, 1 a
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, 271018, China School of Mechanical and Automotive Engineering, Qilu University of Technology, Jinan 250353, China c Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China d Jining Institutes of Advanced Technology, Chinese Academy of Sciences, Jining 272000, China e School of Vehicle and Mechanical Engineering, Changsha University of Science & Technology, 960, 2nd Section, Wanjiali South RD, Changsha, Hunan, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 September 2015 Received in revised form 26 May 2016 Accepted 3 June 2016
In this paper, an electric vehicle powertrain configuration is presented, which the lithium-ion battery integrated with ultracapacitors is developed as the hybrid power system to improve the transient performance of an electric vehicle, and to decrease the damage to the battery pack. In the proposed system, a bidirectional direct current/direct current converter is used to couple the ultracapacitors bank to the main battery pack. The energy management strategy based on fuzzy logic for hybrid power system has been proposed to promote the performance of energy flow in the electric vehicle. The experiment results in urban driving cycles show remarkable advantages of the proposed hybrid system configuration and energy management strategy. About 30% of the battery capacity energy is saved while using the hybrid power source. Besides, the voltage and current curves of battery become smoother than that with the single power. Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.
Keywords: Ultracapacitors Hybrid power system Energy management Fuzzy logic
1. Introduction Because of the growing attention on global warming, fossil fuel shortage and vehicle safety, the governments, industry and public around the world are showing more interests on hybrid and electric vehicle. Hybrid and electric vehicle are the research focus of numerous papers. Due to the limitation of the driving range of electric vehicle (EV), hybrid electric vehicle (HEV) is popular. There are different hybrid configurations for different hybrid electric vehicle. Different HEVs have different hybrid configuration [1]. Besides, Energy management is the key technology of HEV [2], and some algorithms have been applied to energy management [3]. However, in some circumstances we need zero emission vehicles. Therefore, some research papers have been studied on electric
* Corresponding author. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. ** Corresponding author. E-mail addresses: [email protected] (M. Li), [email protected] (H. Xu), wm.li@siat. ac.cn (W. Li), [email protected] (Y. Liu), [email protected] (F. Li), [email protected]. cn (Y. Hu), [email protected] (L. Liu). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.energy.2016.06.009 0360-5442/Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.
vehicle or electric vehicle integrated renewable energy [4], and electric vehicle has advantages over others (biofuel, hydrogen) in reduction of total fuel demand, mitigation cost, and profitability of fuel supply [5]. Battery management system is important to electric vehicle since it can protect the cells and battery packs from getting damaged, make the batteries operating within the proper voltage and temperature range, guarantee the safety and prolong their service life as long as possible and maintain the batteries to operate in a state that the batteries could fulfill the vehicles' requirement [6]. However, with current battery technology, battery cannot suffer from low temperatures and high current cycling frequently [7]. Ultracapacitors (UC) can serve as a high-power, low-energy auxiliary storage in electric vehicle and UC can replace battery during regenerative braking to achieve higher efficiency. Therefore, in recent years, some researchers have proposed the hybrid power system (HPS) including batteries and UC for enhancing the performance of electric vehicles, such as reducing the damage of high current to battery pack [8]. There are three typical HPS topologies: 1) an UC bank connected with a battery pack in parallel form directly [9], power electronics are not required in this configuration, and thus it is the simplest one. However, as the voltage sweep of UC is restricted by the voltage of battery pack in this
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configuration, the UC cannot be utilized effectively; 2) a bidirectional direct current/direct current (DC/DC) converter implemented between an UC bank and a battery pack in a series form, UC voltage can be used in a wide range in this configuration, about 75% energy stored in the UC can be delivered when the UC is permitted to have a voltage of 50%, then it is the most widely applied configuration in the HPS; 3) an UC connected with a battery pack in parallel after connection to a bidirectional DC/DC converter, though this configuration can utilize the UC effectively, the voltage of the EV is probable to be altered and motor controller may needs some modifications [10]. In addition to the design of the HPS topology, some researchers have contributed to the development of parameter optimizing and energy management strategies for the HPS. A formulation was proposed to optimize size and the parameter of control method based on power distribution control strategy in three different cycles. The results show the optimum size and control strategy has better battery life. However, the results are just achieved from simulation and the physical experiments results are not provided [11]. A novel model predictive controller and a dynamic programming algorithm including a simplified battery aging model in their formulations were presented, the proposed method based on model predictive and dynamic programming reduce the root mean value by 6% and 10% respectively. However, the proposed method is not easy to be applied to the hardware. Then the results only can be acquired from simulation. And the dynamic programming method is just used to offline optimize the rule-based algorithm, the strength of DP is not developed fully [12]. A Genetic Algorithm (GA) was used to optimize the component size with aim of minimizing the cost of the energy storage system, and three different driving cycles are incorporated into the optimization process. The author provides one effective method to optimize component size, but the results are not validated by vehicle test [13]. A field-programmable gate array (FPGA) was designed to control interleaved bidirectional buck-boost converter working in a discontinuous condition mode, and control strategy is based on dividing the current demand of the motor into two parts (high-frequency current and low-frequency current). However, the control strategy is simple and not suitable for the hybrid energy system, for the hybrid energy system consists of high nonlinearity and time-varying characteristics [14]. A parallel hybrid electric vehicle with an internal combustion engine and a hybrid energy storage system is presented. An energy management strategy based on fuzzy logic is used to improve the economy of the HEV. The experiments show that fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle. However, this paper doesn't describe clearly the contribution of ultracapacitors in the hybrid electric vehicle [15]. A hybrid battery-ultracapacitors energy source is presented for a vehicle and energy management is designed to achieve highefficiency. However, the author doesn't describe how to decide the size and capacity of ultracapacitors. The simulation results also don't provide the quantitative strength of the proposed system [16]. Supervisory energy management based on dynamic programming and neural networks is proposed. The online energy extends the battery life by 64.8% in comparison to the battery-only system. The proposed strategy may be possible to instruct the other algorithms design. However, the neural networks algorithm is not easy to be applied in the real vehicle system. Besides, this paper doesn't describe clearly how to achieve the results [17]. Power management strategy based on the fuzzy logic is been proposed for battery-ultracapacitors energy storage. The experiment results show that hybrid power system improves performance and reliability of the energy source. However, this paper doesn't describe clearly how to design the hybrid power system. Besides, the
quantitative strength of the proposed strategy and energy system is also not provided [18]. A novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions is proposed and decreases the 1.76% energy loss. This optimal approach could be used to improve the performance of other control strategies [19]. Although many researchers have made progress in developing or improving the HPS topology and their control method, two problems must be noted. 1) The topology of HPS has a significant effect on the performance and efficiency to different types of EVs. 2) An optimal control strategy with unsuitable system will not improve the energy efficiency, that is, there is an optimal coupling problem in terms of energy management strategy and system parameter. Development of energy regeneration helps to improve the performance of electric vehicles. During the urban driving conventional vehicles consumes significant amount of energy during braking [20]. The energy generated during the braking should be saved with minimum loss and maximum efficiency [21]. Both saving and producing the regenerative energy are important. Thus charging rate of energy storage device is a significant parameter to reach the best efficiency and achieve more energy. In Refs. [22], Bo Long and Shin Teak Lim proposed an energy management control scheme based on H∞ to acquire more regenerative braking energy, hence decreasing the energy consumption and increasing the driving range. However, this energy strategy could not be able to deal with the parameter variations of the buck-boost inductance and capacitance of ultracapacitors pack. In Refs. [23], a regenerative braking co-operative control system for the automatic transmission-based hybrid electric vehicle is proposed. The cooperative control algorithm is suggested for the friction braking and regenerative braking. Though the driver's demand for the braking was satisfied with proposed control strategy, the efficiency of regeneration energy was not shown. However, it's one applicable method to recover energy. In Refs. [24], the regenerative energy is increased by improving braking method, averaging the deceleration, without changing the power system, and this method increases the regenerative energy to 18%. It's one applicable method to increase the driving range of electric vehicle. However, there is a need to do more research in the field of hybrid power system and its application. Due to the two layer physical structure, UC is more efficient and compatible compared to battery during charging condition. Better performance in power density and infinity times charging and discharging without performance deterioration make UC a better option. Using these characteristics of UC along with batteries, the transient performance of an EV can be improved, which increases the useful life of the batteries [25,26]. During the transient or peak demand periods, UC acts as a temporary power source in order to meet the load demand which battery fails to provide. Thus more energy with higher efficiency can be recovered by utilizing the dynamic behavior of energy storage. To couple the battery and UC together to a vehicle, a bidirectional DC/DC converter is needed [27]. Because of the efficiency and demanding power of the system, an efficient and reliable control strategy is needed to select power source in order to supply the load at each operating point of the DC/DC converter [28]. And then the optimal system efficiency and mass can be achieved by optimizing performance and size of the energy system with the help of the power management strategy. Besides, the power control strategy can ensure the proper energy flow properly between the UC and battery pack especially in acceleration and deceleration situations [23]. Due to the nonlinearity and complexity of the whole system, fuzzy logic control strategy has been employed in this paper. And a specified test bench experiment results are obtained. The topology of the HPS in the paper is easy for industrial applications
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magnetic motor (PMSM), a lithium-ion battery, and an automatic/ manual transmission system. In the powertrain, the motor is directly linked to the input of the transmission. The picture of EV and some important parameters are shown in Fig. 1 and Table 1, respectively. This prototype has been used previously in research work related to the intelligent regenerative braking of electric vehicle. Hence in order to improve the performance of battery only system, we proposed one type of hybrid power system based on the said prototype EV.
2.2. Hybrid power system Fig. 1. LF620 EV prototype.
Table 1 Vehicle parameters. Dimensions (mm) Curb weight (kg) Gearbox ratio (Fixed ratio) Main difference PMSM Lithium-ion battery
Power (kW) Torque (N$m) Voltage (V) Capacity (Ah)
4550 1705 1495 1550 1.895 4.308 30/60 75/150 320 80
with remaining the performance, and the control strategy based on fuzzy logic is suitable for the real-time control with the vehicle running. The fuzzy logic is not only applied in the power system but also regenerative braking. Besides, the experimental results are acquired through vehicle test not model test, which can reflect the performance of the proposed topology and control strategy more effectively. The remainder of this paper is organized as follows: the EV structure is introduced in section 2 along with the structure of hybrid power system. Section 3 discusses the control strategy of hybrid energy system. In Section 4, the experimental results are obtained, which are analyzed in detail. Finally, the conclusions are summarized in Section 5. 2. EV power system and hybrid power system 2.1. Structure of prototype EV “LF620” is used as the prototype EV in this study, which is developed by Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (SIAT-CAS), and has passed National Passenger Car Quality Supervision and Inspection Center Test. What's more, it is also served as the police patrol car during the Shanghai Expo 2010. The prototype EV is a front wheel drive, with a permanent
Different typologies available are used for the hybrid power system. For instance, the UC with battery system can be integrated with or without a power electronic converter via series or parallel connection. The hybrid system implemented into the electric vehicle is proposed, as shown in Fig. 2. It comprises four components: a lithium-ion battery pack, an UC bank, a smoothing inductor LS and a bidirectional DC/DC converter based on Insulated Gate Bipolar Transistors (IGBTs). Both UC bank and the smoothing inductor LS are connected in parallel to the main battery pack. The bus voltage is 320v and should be remained constant. Then the UC is connected to the bus via a bidirectional DC/DC converter. The energy from the electrical machine can be used to recharge the UC when the kinetic energy is being recovered during the braking, or via DC/DC converter using the energy stored in the battery. In order to prevent the capacity of the UC bank from being totally utilized, the UC bank and the load terminal voltages change with the terminal voltage of driving motor system. Thus, the parameters of UC bank are confined by the terminal voltage of the motor system. And the size of UC bank is affected by the voltage and capacitance. In this paper, the total capacitance of the UC is calculated. The objective is to offer a dynamic energy storage unit that can absorb the kinetic energy while braking a car with 1500 kg mass at the velocity of 120 km/h. Besides, there is a need to have a capacitor that can save the energy with enough high power at its lowest instantaneous voltage. Therefore a quarter of the nominal voltage is chosen to be the minimum operational voltage of the capacitor. And as a result, the efficiency of charging and discharging of the capacitor would affect energy storage. It holds: Vehicle kinetic energy
Wkin ¼
1 2 1500 1202 mv ¼ kws ¼ 833:3kws ¼ 231wh 2 2 3:62
(1)
We assume the efficiency coefficient kef about energy stored into the capacitor is 0.5. When the voltage changes from 60 V to 240 V during charging, energy stored in the capacitor is:
Fig. 2. Hybrid power system.
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Fig. 3. Printed board of DSP controller.
As 30.9F is not a standard capacitance value, therefore C ¼ 33F is chosen. During deceleration (regenerative braking), the capacitor voltage is allowed to charge from its minimum voltage (60 Vdc) to full charge (240 Vdc), while absorbing 231wh useful energy. The amount of energy is apparently poor, thus we can take 40 kW of power without damaging the battery life during 20 s which is longer than the time for a good deceleration. During acceleration, energy is delivered in a similar way, discharging the UC. 200 A is the nominal current of the UC and DC/DC while 250 A is the maximum current. Thus the capacitor can be charged with the maximum power
P ¼ Umax I ¼ 240V 250A ¼ 60kw
Fig. 4. DSP controller with electric environment.
Wcap ¼
1 2 2 C Umax Umin 2
(2)
Then the capacitance can be calculated by:
C¼
mv2 kef 2 2 Umax Umin
¼
1500 1202 1 F ¼ 30:9F 2402 602 3:62
(3)
(4)
This amount of power is sufficient for the laboratory tests. But we should take a consideration on the efficiency coefficient which is remained to be measured during vehicle accelerating and decelerating. The battery pack consists of 100 cells in series (320 Vdc nominal). The UC bank comprises of 5 modules in series, each one with 165 F and 48 V dc nominal (BMOD 0165P048). Then the total UC bank has the capacity of 33 F, with a 60v~240v voltage fluctuating range. About 93.75% of the initial energy stored in the UC bank can be utilized if the terminal load voltage is allowed to decrease 25% of
Fig. 5. Efficiency map of DC/DC.
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Fig. 6. (a) Membership functions of Preq (Preq>0). (b) Membership functions of Preq (Preq<0).
Fig. 7. (a) Membership functions of BSOC (Preq>0). (b) Membership functions of BSOC (Preq<0).
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its initial value. Aluminum coils are used to design the inductor LS, which is 120 mm width and 0.5 mm thick, and coils are made up of flat plates in order to minimize the skin effect and the losses thus optimizing the weight. It has the ability to sustain 100 A current under steady-state and 250 A current for 2 min. The other parameters of inductance are: m ¼ 20 kg, L ¼ 1.6 mH, R ¼ 0.03 Ohms. Besides air core is used to eliminate iron losses or saturation problems. IGBTs (Intellimod type PM400DSA060) are used to design the DC/DC converter along with a water cooling system in order to reduce the size and weight. In order to install it inside the normal engine compartment, it is designed in a very compact shape. Since an UC should be charged and discharged alternately in acceleration and deceleration (regenerative braking) situation, the DC/DC converter performs two operations: Boost operation used for acceleration and Buck operation used for deceleration [29]. During the Boost operation (acceleration), the IGBT Q2 is turned on and off at a controlled duty cycle such that the required amount of energy can be transferred from the capacitor to the battery pack. When Q2 is switched ON, amount of energy is taken from capacitor and stored in the inductor LS during a period. Then the energy from the LS is transferred into C, via D1, and then into the battery pack when Q2 is OFF. According to the working principle, the UC absorbs energy from the battery pack with the help of converter during the Buck operation. When Q1 is switched ON, the energy is transferred from the battery pack to the UC, and some part of the energy is stored in the LS. Then the remaining energy stored in the LS is transferred inside the UC through D2 when Q1 is switched OFF. TMS320F2812 Digital Signal Processor (DSP) from Texas Instruments is used to implement the boost and buck control of the
DC/DC converter by executing a program based on the algorithm presented. For the execution of this program DSP must capture some available data and then deliver the output according to the designed rule to the IGBT gating ports. Activation of the switches at each moment is determined by the main controller. The inputs of the controller during the buck and boost mode are eICharge (the error of the charging current from its desired value), dICharge/dt (the rate of change of charge current), eVbus (the error of the bus voltage from its desired value), dVbus/dt (the rate of change of bus voltage), and eIL (the error of the inductor current from its desired value), respectively. Each controller delivers the change of modulating signal using the PWM method (Am) as output. However, the modulating signal (m(n)) at each moment is equal to the change of modulating signal (Am(n)) plus the modulating signal of the previous signal (m(n-1)). Besides, the modulating signal will be compared with a saw tooth signal before producing the gate signal. Then two PWM signals are delivered to the IGBT gating ports via optocouples. Printed board of the DSP controller and its links with the electronic environment are shown in Figs. 3 and 4, respectively [30]. The Fig. 5 shows the efficiency of DC/DC at different input voltage. The transferred power varies from 1kw to 60kw. It can be seen that the efficiency increases with increasing power until it reaches peak and then drops slowly with further increasing power. Besides, the efficiency increases with increasing input voltage, the input voltage is higher, the efficiency is higher under the same power.
3. Power management strategy based on fuzzy logic Control strategy is essential for power-sharing between the battery and the UC of a hybrid power system, and a suitable
Fig. 8. (a) Membership functions of CSOC (Preq>0). (b) Membership functions of CSOC (Preq<0).
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Fig. 9. (a) Membership functions of Kcap (Preq>0). (b) Membership functions of Kcap (Preq<0).
Fig. 10. The relationship of Preq, BSOC, CSOC, and Kcap.
strategy can minimize the total capacity in farads, reducing the high cost of the UC. Then the parameters of controller will be optimized with some objectives, such as deceleration behavior and energy consumption, for achieving the following: (1) Ensure drivability;
(2) Maximize energy recovery; Control strategy based on fuzzy logic is designed for the combined system. Fuzzy logic control (FLC) is a nonlinear and adaptive control method, with robust performance for linear and nonlinear systems with parameter variation [31], and for the complexity and
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Fig. 11. The structure of FLC of hybrid power system.
Fig. 12. Test bench.
nonlinearity of hybrid power source system, fuzzy controller is one of the best suitable solutions [32]. Sugeno fuzzy controller can employ adaptive technology and ensure outputs continuity and reach high operational efficiency, thus Sugeno type fuzzy controller
Fig. 13. DSPACE box.
is selected in the proposed control strategy. Fuzzy logic controller (FLC) system mainly consists of three subsystems i.e. input variables, output variables and fuzzy logic rules. Even though two fuzzy logic controllers are designed for the acceleration and deceleration case, respectively, the variables are similar [33]. The desired power from the hybrid power system has a significant effect on the distribution of power between UC and battery pack. In the acceleration case, the battery pack can provide most of the required power when the desired power (Preq) is small. The battery pack and UC can cooperate to output the power to operate at an efficient point when the Preq is medium. The proportion of UC power can be increased to the highest value when the Preq is very high. In order to control more accurately, the fuzzy set of Preq should have more details. Therefore, we prefer the set of Preq (Preq>0) as: {TS, S, M, B, TB}. Where, TS < S < M < B < TB. In the deceleration case, the hybrid power system absorbs energy with limitations, thus the situation is simpler than the situation of acceleration case. Thus we prefer the set of Preq (Preq<0) as:{ S, M, B}.The membership functions can be seen in Fig. 6. The first task of energy management strategy is to maintain an adequate range of energy in the power system, thus state of charge is an important parameter which has effects on power transition. The battery pack should maintain a high enough level of SOC to provide the required peak power for the vehicle in the acceleration cases. When the battery state of charge (BSOC) is high, the battery provides the most of desired power. When the BSOC is low, in order to decrease the damage on the battery, most of the energy should be taken from the UC. In the deceleration case, the regenerative energy is mainly absorbed by UC, decreasing the direct transfer of transient current to the battery packs. Thus the medium value of BSOC should have a very board range in this case, and the trapezoid function is chosen as the member function. Therefore, we prefer both the set of positive BSOC and negative BSOC as:{ L, M, H}, where, L < M < H. The membership functions are shown in the Fig. 7. In the acceleration case, the main objective of UC is to cooperate with battery pack to offer sufficient power. When the required power is increasing, the power taken from the UC is increasing too. Thus we prefer the set of UC (Preq>0) as {TS, S, M, B, TB}, like Preq. The UC pack should keep a low enough level of SOC to have acceptable charging rate and sufficient free capacity to absorb the admissible energy so that the UC can recover the braking energy in the deceleration cases. Thus the range of the lowest value of UC state of charge (CSOC) is broader than that in the acceleration situation. The energy management strategy tries to discharge energy of UC if it has a high SOC value in order to absorb more regenerated energy during braking time. Therefore, we prefer the set of the CSOC (Preq<0) as:{ TL, L, M, H}.The membership functions of CSOC
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Fig. 14. Delivered & Regenerated power.
are shown in the Fig. 8. The output of FLC is the ratio of UC power account for the total desired power (Kcap). In acceleration case, the ratio is increasing as the desired power is increasing. Therefore, the fuzzy set of Kcap (Preq>0) is divided into: {TS, S, M, B, TB}. The situation of Kcap in deceleration case is simpler than that in acceleration case, therefore we prefer the set of Kcap (Preq<0) as:{ TS, S, M, B}. In the Fig. 9, the membership functions of Kcap are shown. The rules of FLC have a significant effect on the performance of the control system, which reflects the relationship among the Preq, BSOC, CSOC, and Kcap. The relationship is shown in Fig. 10. And there are two cases: the positive Preq and negative Preq. When Preq is positive, Kcap is increasing as Preq increases, thus UC acts as short duration power source and coordinates with battery to meet the require power. When BSOC is constant, Kcap is increasing as CSOC, so UC can provide more power when its power is higher. On the other hand, the Kcap is higher when the regenerated energy is higher (Pre<0), then UC can absorb more power. There is a little BSOC variation in this case; it means that we can use the characteristic of UC fully to recover more energy with higher efficiency than Battery. 45 fuzzy reasoning rules are designed when the Preq is positive, and 27 fuzzy reasoning rules are designed when the Preq is negative. A part of these fuzzy rules when Preq is negative are listed as follows: If (Preq is S) and (BSOC is L) and (CSOC is TL) then (Kcap is B); If (Preq is M) and (BSOC is L) and (CSOC is TL) then (Kcap is B); If (Preq is B) and (BSOC is L) and (CSOC is TL) then (Kcap is M); If (Preq is S) and (BSOC is L) and (CSOC is M) then (Kcap is B); …… If (Preq is S) and (BSOC is H) and (CSOC is H) then (Kcap is S); If (Preq is M) and (BSOC is H) and (CSOC is H) then (Kcap is S); If (Preq is B) and (BSOC is H) and (CSOC is H) then (Kcap is M); Thus, the input variables of the FLC are Preq, BSOC and CSOC. The output variable is Kcap, and then UC power will be obtained by multiply Preq. The structure of FLC for hybrid power system is shown in Fig. 11. The FLC is divided into two sections depending on
the polarity of Preq, for the cases in the acceleration and deceleration are different. When the Preq is positive, the FLC1 is used. When the Preq is negative, the FLC2 is used. 4. Experiment results and discussions In order to test the proposed hybrid power system and control strategy, we built a test bench, as shown in Fig. 12 and Fig. 13. The test bench consists of an electrical load equipment, a host computer,a DSPACE box, a VCU and a CAN communication unit. The UDDSS cycle is built with MATLAB software in the host computer, which generates the code data of UDSS. Then the DSPACE box receives the data of the driving cycle from the host computer. The VCU receive the signals with CAN communication from DSPACE box, and meanwhile, the VCU analyzes the signals from the vehicle and then sends the command to the hybrid power system. The system is tested repeatedly at different speeds and torques to encompass the required full operating range of the system, the UC will be charged until their terminal voltages reach the preset value of 240v during one cycle by electric load equipment. Regeneration capability is a significant figure of the system, thus the test cycle consists of a period during which the torque is reversed with the speed held constant by the dynamometer. Thus the ultracapcitors can absorb and provide energy repeatedly, and the net input energy of the UC is zero. The proposed hybrid power system ensures good performance with available control strategy, thus VCU hardware is used to implement the control strategy, and a high performance Freescale micro control unit is selected as the main chip to ensure adequate computational performance. Some experiments are done on the test bench. Fig. 14 shows the picture of the delivered and regenerated power tested under the UDDS cycle while the negative quantities are the regenerated braking values. In the ideal energy storage system, all generated energy should be saved. However, in real applications some amount of energy always dissipates because of much limitation. The regeneration performance of the hybrid power system is also shown in these times in the Fig. 14. Fig. 15 shows the performance of regeneration and drivability of
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Fig. 15. (a). Regeneration performance in hybrid power system at high SOC. (b). Regeneration performance in hybrid power system at low SOC.
the system based on the Battery SOC and UC SOC under the UDDS driving cycle. The Fig. 15 (a) shows the performance at the high SOC of ultracapacitors and battery. The Fig. 15 (b) shows the performance at the low level SOC. In the two cases, driving energy is delivered in acceleration time and regenerated energy is reproduced with the regenerative braking method. The system can supply a percentage of required energy by recovering energy which is one of the biggest advantages of electric vehicles. Using UC can help reach the higher efficiency and get more green and free energy. Besides, the behavior of UC in charge and discharge situation gives more predictable index than the battery pack for the energy storage system. The important parameter which affects the process of regeneration is SOC level. The control strategy discharges the SOC of UC in acceleration time, so that the UC have enough capacity to absorb energy in deceleration time. For the efficiency of absorbing regenerative braking is related to the initial braking velocity and the time of braking, the UC cannot recover to total regenerative braking energy even at a higher speed, thus the efficiency at low speed is not high.
In Fig. 16 SOC, voltages and currents of the battery only system and Battery-UC hybrid system are presented and compared with each other. The red solid line represents the hybrid power system while the blue dotted line stands for the battery only system. It is shown that about an extra 30% of the battery capacity energy is saved with the hybrid power source in one UDDS cycle. The voltage and current of battery become smoother with the hybrid power source than that with the single power. Thus, the hybrid power system decreases the damage of peak current to the battery pack. Besides, the current is negative in the deceleration condition, which indicates that the hybrid power system absorbs energy. The ultracapacitors voltage and current under UDDS cycle are shown in the Fig. 17, the value of current is higher than that of battery. Thus the ultracapacitors can absorb the big current and provide small current for battery via DC/DC, and the damage of peak current is decreased for battery. The main aim of using the hybrid power is to improve the energy consumption and increase the driving range. The energy consumption and driving range of the hybrid power system and
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Fig. 16. Results under UDDS cycle.
Fig. 17. Ultra-capacitors voltage && current under UDDS cycle.
Table 2 Energy consumption and driving range comparison. Driving cycle (UDDS)
Battery only
Hybrid power
Improvement
Energy consumption (kwh/100 km) Driving range (km)
14.18 208
12.1 245
14.67% 17.80%
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Fig. 18. Current curve comparison.
battery only system are compared and shown in Table 2. We can see that the energy consumption of hybrid power system decreases 14.67% compared with the battery only system while the driving range extends from 208 km to 245 km, almost an improvement of 17.80%. This means that the hybrid power system with the fuzzy controller could increase more driving range than the battery only system by 17.80% with the same battery capacity. Hybrid power system with 66 A h battery capacity and 33F capacitance and battery only system with 78 A h are compared in another experiment. The results indicated that the driving range of these two EV is both 208 km. That means the capacity of battery can be reduced by about 15.38% while reducing the size of the battery at the same time. Since the weight of 78 A h battery is 428 kg and the weight of 66 A h battery is 362 kg, the weight can be reduced by about 15.42%. Besides, the discount amount of battery capacity is 4.56Kw.h, which is about 1689 dollars according to the price of the ternary lithium battery. The cost of hybrid power system is lower than the single power system, though the UC and DC/DC converter will add some cost, which is about 1020 dollars. The delivered currents are also very important for hybrid power system. As shown in Fig. 18, the battery currents charge-discharge curve of hybrid power system is more placid than the curve of the battery only system, thus protecting the battery health.
5. Conclusions The performance of electric vehicle relies mainly on the structure and management strategy. In this paper, hybrid power system and fuzzy logic based control strategy have been proposed. The experiment results show that the energy consumption of hybrid power system is decreased by 14.67%, and driving range is increased by 17.8% compared with single power system. Besides, the performance of charge and discharge of battery is also improved, and then the battery peak current is decreased. What's more, the weight of hybrid power system is lighter than the battery only system in the same driving range, 15.42% weight is decreased. Thus, the proposed hybrid power system and power management strategy improves the performance of electric vehicle apparently while efficiently controlling vehicle during different modes of operation.
Acknowledgments This research is also supported by Major Special Project of Science and Technology of Shandong Province (2015ZDXX0601A01). That is, this research is supported by National Natural Science Foundation of China (61273139), China Postdoctoral Science Foundation (2015t80733), Postdoctoral innovation project of Shandong Province (201403010), Natural Science Foundation of Shandong (ZR2014FP001), National Natural Science Foundation of China (61404011), Major Special Project of Science and Technology of Shandong Province (2015ZDXX0601A01) References [1] Kamil CB, Mehmet AG, Ahmet T. A comprehensive overview of hybrid electric vehicle: powertrain configurations, powertrain control techniques and electronic control units. Energy Convers Manag 2011;52:1305e13. [2] Yue H, Jun S, Weimin L, Yunlong P. A scientometric study of global electric vehicle research. Scientometrics 2014;98(2):1269e82. [3] Khayyam Hamid, Bab-Hadiashar Alireza. Adaptive intelligent energy management system of plug-in hybrid electric vehicle. Energy 2014;69:319e35. [4] Richardson DB. Electric vehicles and the electric grid: a review of modeling approaches, impacts and renewable energy integration. Renew Sustain Energy Rev 2013;19:247e54. [5] Shafiei E, Davidsdottir B, Leaver J, Stefansson H, Asgeirsson EI. Comparative analysis of hydrogen, biofuels and electricity transitional pathways to sustainable transport in a renewable-based energy system. Energy 2015;83: 614e27. [6] Fernandez IJ, Calvillo CF, Sanchez-Miralles A, Boal J. Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles. Energy 2013;60:35e43. [7] Joao JT, Paulo GP, Humberto MJ, Carlos HA. A multi-level energy management system for multi-source electric vehicles-An integrated rule-based metaheuristic approach. Appl Energy 2013;105:304e18. [8] Allegre AL, Bouscayrol A, Trigui R. Influence of control strategies on battery/ supercapacitor hybrid energy storage systems for traction applications. IEEE Veh Power Propuls Conf 2009:213e20. [9] Emadi A, Rajashekara K, Willisamson S, et al. Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations. IEEE Transac Veh Technol 2005;54:763e70. [10] Lukic SM, Wirasingha SG, Rodriguez F, et al. Power management of an ultracapacitor/battery hybrid energy storage system in an HEV. In: IEEE conference on vehicle power and propulsion conference, Windsor; 2006. p. 1e6. [11] Masoud M, Mohammad-Reza H, Vahid E, Ali S. Optimum sizing and optimum energy management of a hybrid energy storage system for lithium battery life improvement. J Power Sources 2013;244:2e10. [12] Alberto S, Aldo S, Constantina L. Power split strategies for hybrid energy storage systems for vehicular applications. J Power Sources 2014;258: 395e407. [13] Lei Z.; Dorrell D.G. Genetic Algorithm based optimal component sizing for an
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
The structure and control method of hybrid power source for electric vehicle Maobing Li a, 1, Hui Xu d, 1, Weimin Li c, d, *, Yin Liu a, b, 1, Fade Li a, **, Yue Hu d, 1, Li Liu e, 1 a
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, 271018, China School of Mechanical and Automotive Engineering, Qilu University of Technology, Jinan 250353, China c Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China d Jining Institutes of Advanced Technology, Chinese Academy of Sciences, Jining 272000, China e School of Vehicle and Mechanical Engineering, Changsha University of Science & Technology, 960, 2nd Section, Wanjiali South RD, Changsha, Hunan, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 September 2015 Received in revised form 26 May 2016 Accepted 3 June 2016
In this paper, an electric vehicle powertrain configuration is presented, which the lithium-ion battery integrated with ultracapacitors is developed as the hybrid power system to improve the transient performance of an electric vehicle, and to decrease the damage to the battery pack. In the proposed system, a bidirectional direct current/direct current converter is used to couple the ultracapacitors bank to the main battery pack. The energy management strategy based on fuzzy logic for hybrid power system has been proposed to promote the performance of energy flow in the electric vehicle. The experiment results in urban driving cycles show remarkable advantages of the proposed hybrid system configuration and energy management strategy. About 30% of the battery capacity energy is saved while using the hybrid power source. Besides, the voltage and current curves of battery become smoother than that with the single power. Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.
Keywords: Ultracapacitors Hybrid power system Energy management Fuzzy logic
1. Introduction Because of the growing attention on global warming, fossil fuel shortage and vehicle safety, the governments, industry and public around the world are showing more interests on hybrid and electric vehicle. Hybrid and electric vehicle are the research focus of numerous papers. Due to the limitation of the driving range of electric vehicle (EV), hybrid electric vehicle (HEV) is popular. There are different hybrid configurations for different hybrid electric vehicle. Different HEVs have different hybrid configuration [1]. Besides, Energy management is the key technology of HEV [2], and some algorithms have been applied to energy management [3]. However, in some circumstances we need zero emission vehicles. Therefore, some research papers have been studied on electric
* Corresponding author. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. ** Corresponding author. E-mail addresses: [email protected] (M. Li), [email protected] (H. Xu), wm.li@siat. ac.cn (W. Li), [email protected] (Y. Liu), [email protected] (F. Li), [email protected]. cn (Y. Hu), [email protected] (L. Liu). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.energy.2016.06.009 0360-5442/Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.
vehicle or electric vehicle integrated renewable energy [4], and electric vehicle has advantages over others (biofuel, hydrogen) in reduction of total fuel demand, mitigation cost, and profitability of fuel supply [5]. Battery management system is important to electric vehicle since it can protect the cells and battery packs from getting damaged, make the batteries operating within the proper voltage and temperature range, guarantee the safety and prolong their service life as long as possible and maintain the batteries to operate in a state that the batteries could fulfill the vehicles' requirement [6]. However, with current battery technology, battery cannot suffer from low temperatures and high current cycling frequently [7]. Ultracapacitors (UC) can serve as a high-power, low-energy auxiliary storage in electric vehicle and UC can replace battery during regenerative braking to achieve higher efficiency. Therefore, in recent years, some researchers have proposed the hybrid power system (HPS) including batteries and UC for enhancing the performance of electric vehicles, such as reducing the damage of high current to battery pack [8]. There are three typical HPS topologies: 1) an UC bank connected with a battery pack in parallel form directly [9], power electronics are not required in this configuration, and thus it is the simplest one. However, as the voltage sweep of UC is restricted by the voltage of battery pack in this
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configuration, the UC cannot be utilized effectively; 2) a bidirectional direct current/direct current (DC/DC) converter implemented between an UC bank and a battery pack in a series form, UC voltage can be used in a wide range in this configuration, about 75% energy stored in the UC can be delivered when the UC is permitted to have a voltage of 50%, then it is the most widely applied configuration in the HPS; 3) an UC connected with a battery pack in parallel after connection to a bidirectional DC/DC converter, though this configuration can utilize the UC effectively, the voltage of the EV is probable to be altered and motor controller may needs some modifications [10]. In addition to the design of the HPS topology, some researchers have contributed to the development of parameter optimizing and energy management strategies for the HPS. A formulation was proposed to optimize size and the parameter of control method based on power distribution control strategy in three different cycles. The results show the optimum size and control strategy has better battery life. However, the results are just achieved from simulation and the physical experiments results are not provided [11]. A novel model predictive controller and a dynamic programming algorithm including a simplified battery aging model in their formulations were presented, the proposed method based on model predictive and dynamic programming reduce the root mean value by 6% and 10% respectively. However, the proposed method is not easy to be applied to the hardware. Then the results only can be acquired from simulation. And the dynamic programming method is just used to offline optimize the rule-based algorithm, the strength of DP is not developed fully [12]. A Genetic Algorithm (GA) was used to optimize the component size with aim of minimizing the cost of the energy storage system, and three different driving cycles are incorporated into the optimization process. The author provides one effective method to optimize component size, but the results are not validated by vehicle test [13]. A field-programmable gate array (FPGA) was designed to control interleaved bidirectional buck-boost converter working in a discontinuous condition mode, and control strategy is based on dividing the current demand of the motor into two parts (high-frequency current and low-frequency current). However, the control strategy is simple and not suitable for the hybrid energy system, for the hybrid energy system consists of high nonlinearity and time-varying characteristics [14]. A parallel hybrid electric vehicle with an internal combustion engine and a hybrid energy storage system is presented. An energy management strategy based on fuzzy logic is used to improve the economy of the HEV. The experiments show that fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle. However, this paper doesn't describe clearly the contribution of ultracapacitors in the hybrid electric vehicle [15]. A hybrid battery-ultracapacitors energy source is presented for a vehicle and energy management is designed to achieve highefficiency. However, the author doesn't describe how to decide the size and capacity of ultracapacitors. The simulation results also don't provide the quantitative strength of the proposed system [16]. Supervisory energy management based on dynamic programming and neural networks is proposed. The online energy extends the battery life by 64.8% in comparison to the battery-only system. The proposed strategy may be possible to instruct the other algorithms design. However, the neural networks algorithm is not easy to be applied in the real vehicle system. Besides, this paper doesn't describe clearly how to achieve the results [17]. Power management strategy based on the fuzzy logic is been proposed for battery-ultracapacitors energy storage. The experiment results show that hybrid power system improves performance and reliability of the energy source. However, this paper doesn't describe clearly how to design the hybrid power system. Besides, the
quantitative strength of the proposed strategy and energy system is also not provided [18]. A novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions is proposed and decreases the 1.76% energy loss. This optimal approach could be used to improve the performance of other control strategies [19]. Although many researchers have made progress in developing or improving the HPS topology and their control method, two problems must be noted. 1) The topology of HPS has a significant effect on the performance and efficiency to different types of EVs. 2) An optimal control strategy with unsuitable system will not improve the energy efficiency, that is, there is an optimal coupling problem in terms of energy management strategy and system parameter. Development of energy regeneration helps to improve the performance of electric vehicles. During the urban driving conventional vehicles consumes significant amount of energy during braking [20]. The energy generated during the braking should be saved with minimum loss and maximum efficiency [21]. Both saving and producing the regenerative energy are important. Thus charging rate of energy storage device is a significant parameter to reach the best efficiency and achieve more energy. In Refs. [22], Bo Long and Shin Teak Lim proposed an energy management control scheme based on H∞ to acquire more regenerative braking energy, hence decreasing the energy consumption and increasing the driving range. However, this energy strategy could not be able to deal with the parameter variations of the buck-boost inductance and capacitance of ultracapacitors pack. In Refs. [23], a regenerative braking co-operative control system for the automatic transmission-based hybrid electric vehicle is proposed. The cooperative control algorithm is suggested for the friction braking and regenerative braking. Though the driver's demand for the braking was satisfied with proposed control strategy, the efficiency of regeneration energy was not shown. However, it's one applicable method to recover energy. In Refs. [24], the regenerative energy is increased by improving braking method, averaging the deceleration, without changing the power system, and this method increases the regenerative energy to 18%. It's one applicable method to increase the driving range of electric vehicle. However, there is a need to do more research in the field of hybrid power system and its application. Due to the two layer physical structure, UC is more efficient and compatible compared to battery during charging condition. Better performance in power density and infinity times charging and discharging without performance deterioration make UC a better option. Using these characteristics of UC along with batteries, the transient performance of an EV can be improved, which increases the useful life of the batteries [25,26]. During the transient or peak demand periods, UC acts as a temporary power source in order to meet the load demand which battery fails to provide. Thus more energy with higher efficiency can be recovered by utilizing the dynamic behavior of energy storage. To couple the battery and UC together to a vehicle, a bidirectional DC/DC converter is needed [27]. Because of the efficiency and demanding power of the system, an efficient and reliable control strategy is needed to select power source in order to supply the load at each operating point of the DC/DC converter [28]. And then the optimal system efficiency and mass can be achieved by optimizing performance and size of the energy system with the help of the power management strategy. Besides, the power control strategy can ensure the proper energy flow properly between the UC and battery pack especially in acceleration and deceleration situations [23]. Due to the nonlinearity and complexity of the whole system, fuzzy logic control strategy has been employed in this paper. And a specified test bench experiment results are obtained. The topology of the HPS in the paper is easy for industrial applications
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magnetic motor (PMSM), a lithium-ion battery, and an automatic/ manual transmission system. In the powertrain, the motor is directly linked to the input of the transmission. The picture of EV and some important parameters are shown in Fig. 1 and Table 1, respectively. This prototype has been used previously in research work related to the intelligent regenerative braking of electric vehicle. Hence in order to improve the performance of battery only system, we proposed one type of hybrid power system based on the said prototype EV.
2.2. Hybrid power system Fig. 1. LF620 EV prototype.
Table 1 Vehicle parameters. Dimensions (mm) Curb weight (kg) Gearbox ratio (Fixed ratio) Main difference PMSM Lithium-ion battery
Power (kW) Torque (N$m) Voltage (V) Capacity (Ah)
4550 1705 1495 1550 1.895 4.308 30/60 75/150 320 80
with remaining the performance, and the control strategy based on fuzzy logic is suitable for the real-time control with the vehicle running. The fuzzy logic is not only applied in the power system but also regenerative braking. Besides, the experimental results are acquired through vehicle test not model test, which can reflect the performance of the proposed topology and control strategy more effectively. The remainder of this paper is organized as follows: the EV structure is introduced in section 2 along with the structure of hybrid power system. Section 3 discusses the control strategy of hybrid energy system. In Section 4, the experimental results are obtained, which are analyzed in detail. Finally, the conclusions are summarized in Section 5. 2. EV power system and hybrid power system 2.1. Structure of prototype EV “LF620” is used as the prototype EV in this study, which is developed by Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (SIAT-CAS), and has passed National Passenger Car Quality Supervision and Inspection Center Test. What's more, it is also served as the police patrol car during the Shanghai Expo 2010. The prototype EV is a front wheel drive, with a permanent
Different typologies available are used for the hybrid power system. For instance, the UC with battery system can be integrated with or without a power electronic converter via series or parallel connection. The hybrid system implemented into the electric vehicle is proposed, as shown in Fig. 2. It comprises four components: a lithium-ion battery pack, an UC bank, a smoothing inductor LS and a bidirectional DC/DC converter based on Insulated Gate Bipolar Transistors (IGBTs). Both UC bank and the smoothing inductor LS are connected in parallel to the main battery pack. The bus voltage is 320v and should be remained constant. Then the UC is connected to the bus via a bidirectional DC/DC converter. The energy from the electrical machine can be used to recharge the UC when the kinetic energy is being recovered during the braking, or via DC/DC converter using the energy stored in the battery. In order to prevent the capacity of the UC bank from being totally utilized, the UC bank and the load terminal voltages change with the terminal voltage of driving motor system. Thus, the parameters of UC bank are confined by the terminal voltage of the motor system. And the size of UC bank is affected by the voltage and capacitance. In this paper, the total capacitance of the UC is calculated. The objective is to offer a dynamic energy storage unit that can absorb the kinetic energy while braking a car with 1500 kg mass at the velocity of 120 km/h. Besides, there is a need to have a capacitor that can save the energy with enough high power at its lowest instantaneous voltage. Therefore a quarter of the nominal voltage is chosen to be the minimum operational voltage of the capacitor. And as a result, the efficiency of charging and discharging of the capacitor would affect energy storage. It holds: Vehicle kinetic energy
Wkin ¼
1 2 1500 1202 mv ¼ kws ¼ 833:3kws ¼ 231wh 2 2 3:62
(1)
We assume the efficiency coefficient kef about energy stored into the capacitor is 0.5. When the voltage changes from 60 V to 240 V during charging, energy stored in the capacitor is:
Fig. 2. Hybrid power system.
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Fig. 3. Printed board of DSP controller.
As 30.9F is not a standard capacitance value, therefore C ¼ 33F is chosen. During deceleration (regenerative braking), the capacitor voltage is allowed to charge from its minimum voltage (60 Vdc) to full charge (240 Vdc), while absorbing 231wh useful energy. The amount of energy is apparently poor, thus we can take 40 kW of power without damaging the battery life during 20 s which is longer than the time for a good deceleration. During acceleration, energy is delivered in a similar way, discharging the UC. 200 A is the nominal current of the UC and DC/DC while 250 A is the maximum current. Thus the capacitor can be charged with the maximum power
P ¼ Umax I ¼ 240V 250A ¼ 60kw
Fig. 4. DSP controller with electric environment.
Wcap ¼
1 2 2 C Umax Umin 2
(2)
Then the capacitance can be calculated by:
C¼
mv2 kef 2 2 Umax Umin
¼
1500 1202 1 F ¼ 30:9F 2402 602 3:62
(3)
(4)
This amount of power is sufficient for the laboratory tests. But we should take a consideration on the efficiency coefficient which is remained to be measured during vehicle accelerating and decelerating. The battery pack consists of 100 cells in series (320 Vdc nominal). The UC bank comprises of 5 modules in series, each one with 165 F and 48 V dc nominal (BMOD 0165P048). Then the total UC bank has the capacity of 33 F, with a 60v~240v voltage fluctuating range. About 93.75% of the initial energy stored in the UC bank can be utilized if the terminal load voltage is allowed to decrease 25% of
Fig. 5. Efficiency map of DC/DC.
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Fig. 6. (a) Membership functions of Preq (Preq>0). (b) Membership functions of Preq (Preq<0).
Fig. 7. (a) Membership functions of BSOC (Preq>0). (b) Membership functions of BSOC (Preq<0).
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its initial value. Aluminum coils are used to design the inductor LS, which is 120 mm width and 0.5 mm thick, and coils are made up of flat plates in order to minimize the skin effect and the losses thus optimizing the weight. It has the ability to sustain 100 A current under steady-state and 250 A current for 2 min. The other parameters of inductance are: m ¼ 20 kg, L ¼ 1.6 mH, R ¼ 0.03 Ohms. Besides air core is used to eliminate iron losses or saturation problems. IGBTs (Intellimod type PM400DSA060) are used to design the DC/DC converter along with a water cooling system in order to reduce the size and weight. In order to install it inside the normal engine compartment, it is designed in a very compact shape. Since an UC should be charged and discharged alternately in acceleration and deceleration (regenerative braking) situation, the DC/DC converter performs two operations: Boost operation used for acceleration and Buck operation used for deceleration [29]. During the Boost operation (acceleration), the IGBT Q2 is turned on and off at a controlled duty cycle such that the required amount of energy can be transferred from the capacitor to the battery pack. When Q2 is switched ON, amount of energy is taken from capacitor and stored in the inductor LS during a period. Then the energy from the LS is transferred into C, via D1, and then into the battery pack when Q2 is OFF. According to the working principle, the UC absorbs energy from the battery pack with the help of converter during the Buck operation. When Q1 is switched ON, the energy is transferred from the battery pack to the UC, and some part of the energy is stored in the LS. Then the remaining energy stored in the LS is transferred inside the UC through D2 when Q1 is switched OFF. TMS320F2812 Digital Signal Processor (DSP) from Texas Instruments is used to implement the boost and buck control of the
DC/DC converter by executing a program based on the algorithm presented. For the execution of this program DSP must capture some available data and then deliver the output according to the designed rule to the IGBT gating ports. Activation of the switches at each moment is determined by the main controller. The inputs of the controller during the buck and boost mode are eICharge (the error of the charging current from its desired value), dICharge/dt (the rate of change of charge current), eVbus (the error of the bus voltage from its desired value), dVbus/dt (the rate of change of bus voltage), and eIL (the error of the inductor current from its desired value), respectively. Each controller delivers the change of modulating signal using the PWM method (Am) as output. However, the modulating signal (m(n)) at each moment is equal to the change of modulating signal (Am(n)) plus the modulating signal of the previous signal (m(n-1)). Besides, the modulating signal will be compared with a saw tooth signal before producing the gate signal. Then two PWM signals are delivered to the IGBT gating ports via optocouples. Printed board of the DSP controller and its links with the electronic environment are shown in Figs. 3 and 4, respectively [30]. The Fig. 5 shows the efficiency of DC/DC at different input voltage. The transferred power varies from 1kw to 60kw. It can be seen that the efficiency increases with increasing power until it reaches peak and then drops slowly with further increasing power. Besides, the efficiency increases with increasing input voltage, the input voltage is higher, the efficiency is higher under the same power.
3. Power management strategy based on fuzzy logic Control strategy is essential for power-sharing between the battery and the UC of a hybrid power system, and a suitable
Fig. 8. (a) Membership functions of CSOC (Preq>0). (b) Membership functions of CSOC (Preq<0).
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Fig. 9. (a) Membership functions of Kcap (Preq>0). (b) Membership functions of Kcap (Preq<0).
Fig. 10. The relationship of Preq, BSOC, CSOC, and Kcap.
strategy can minimize the total capacity in farads, reducing the high cost of the UC. Then the parameters of controller will be optimized with some objectives, such as deceleration behavior and energy consumption, for achieving the following: (1) Ensure drivability;
(2) Maximize energy recovery; Control strategy based on fuzzy logic is designed for the combined system. Fuzzy logic control (FLC) is a nonlinear and adaptive control method, with robust performance for linear and nonlinear systems with parameter variation [31], and for the complexity and
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Fig. 11. The structure of FLC of hybrid power system.
Fig. 12. Test bench.
nonlinearity of hybrid power source system, fuzzy controller is one of the best suitable solutions [32]. Sugeno fuzzy controller can employ adaptive technology and ensure outputs continuity and reach high operational efficiency, thus Sugeno type fuzzy controller
Fig. 13. DSPACE box.
is selected in the proposed control strategy. Fuzzy logic controller (FLC) system mainly consists of three subsystems i.e. input variables, output variables and fuzzy logic rules. Even though two fuzzy logic controllers are designed for the acceleration and deceleration case, respectively, the variables are similar [33]. The desired power from the hybrid power system has a significant effect on the distribution of power between UC and battery pack. In the acceleration case, the battery pack can provide most of the required power when the desired power (Preq) is small. The battery pack and UC can cooperate to output the power to operate at an efficient point when the Preq is medium. The proportion of UC power can be increased to the highest value when the Preq is very high. In order to control more accurately, the fuzzy set of Preq should have more details. Therefore, we prefer the set of Preq (Preq>0) as: {TS, S, M, B, TB}. Where, TS < S < M < B < TB. In the deceleration case, the hybrid power system absorbs energy with limitations, thus the situation is simpler than the situation of acceleration case. Thus we prefer the set of Preq (Preq<0) as:{ S, M, B}.The membership functions can be seen in Fig. 6. The first task of energy management strategy is to maintain an adequate range of energy in the power system, thus state of charge is an important parameter which has effects on power transition. The battery pack should maintain a high enough level of SOC to provide the required peak power for the vehicle in the acceleration cases. When the battery state of charge (BSOC) is high, the battery provides the most of desired power. When the BSOC is low, in order to decrease the damage on the battery, most of the energy should be taken from the UC. In the deceleration case, the regenerative energy is mainly absorbed by UC, decreasing the direct transfer of transient current to the battery packs. Thus the medium value of BSOC should have a very board range in this case, and the trapezoid function is chosen as the member function. Therefore, we prefer both the set of positive BSOC and negative BSOC as:{ L, M, H}, where, L < M < H. The membership functions are shown in the Fig. 7. In the acceleration case, the main objective of UC is to cooperate with battery pack to offer sufficient power. When the required power is increasing, the power taken from the UC is increasing too. Thus we prefer the set of UC (Preq>0) as {TS, S, M, B, TB}, like Preq. The UC pack should keep a low enough level of SOC to have acceptable charging rate and sufficient free capacity to absorb the admissible energy so that the UC can recover the braking energy in the deceleration cases. Thus the range of the lowest value of UC state of charge (CSOC) is broader than that in the acceleration situation. The energy management strategy tries to discharge energy of UC if it has a high SOC value in order to absorb more regenerated energy during braking time. Therefore, we prefer the set of the CSOC (Preq<0) as:{ TL, L, M, H}.The membership functions of CSOC
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Fig. 14. Delivered & Regenerated power.
are shown in the Fig. 8. The output of FLC is the ratio of UC power account for the total desired power (Kcap). In acceleration case, the ratio is increasing as the desired power is increasing. Therefore, the fuzzy set of Kcap (Preq>0) is divided into: {TS, S, M, B, TB}. The situation of Kcap in deceleration case is simpler than that in acceleration case, therefore we prefer the set of Kcap (Preq<0) as:{ TS, S, M, B}. In the Fig. 9, the membership functions of Kcap are shown. The rules of FLC have a significant effect on the performance of the control system, which reflects the relationship among the Preq, BSOC, CSOC, and Kcap. The relationship is shown in Fig. 10. And there are two cases: the positive Preq and negative Preq. When Preq is positive, Kcap is increasing as Preq increases, thus UC acts as short duration power source and coordinates with battery to meet the require power. When BSOC is constant, Kcap is increasing as CSOC, so UC can provide more power when its power is higher. On the other hand, the Kcap is higher when the regenerated energy is higher (Pre<0), then UC can absorb more power. There is a little BSOC variation in this case; it means that we can use the characteristic of UC fully to recover more energy with higher efficiency than Battery. 45 fuzzy reasoning rules are designed when the Preq is positive, and 27 fuzzy reasoning rules are designed when the Preq is negative. A part of these fuzzy rules when Preq is negative are listed as follows: If (Preq is S) and (BSOC is L) and (CSOC is TL) then (Kcap is B); If (Preq is M) and (BSOC is L) and (CSOC is TL) then (Kcap is B); If (Preq is B) and (BSOC is L) and (CSOC is TL) then (Kcap is M); If (Preq is S) and (BSOC is L) and (CSOC is M) then (Kcap is B); …… If (Preq is S) and (BSOC is H) and (CSOC is H) then (Kcap is S); If (Preq is M) and (BSOC is H) and (CSOC is H) then (Kcap is S); If (Preq is B) and (BSOC is H) and (CSOC is H) then (Kcap is M); Thus, the input variables of the FLC are Preq, BSOC and CSOC. The output variable is Kcap, and then UC power will be obtained by multiply Preq. The structure of FLC for hybrid power system is shown in Fig. 11. The FLC is divided into two sections depending on
the polarity of Preq, for the cases in the acceleration and deceleration are different. When the Preq is positive, the FLC1 is used. When the Preq is negative, the FLC2 is used. 4. Experiment results and discussions In order to test the proposed hybrid power system and control strategy, we built a test bench, as shown in Fig. 12 and Fig. 13. The test bench consists of an electrical load equipment, a host computer,a DSPACE box, a VCU and a CAN communication unit. The UDDSS cycle is built with MATLAB software in the host computer, which generates the code data of UDSS. Then the DSPACE box receives the data of the driving cycle from the host computer. The VCU receive the signals with CAN communication from DSPACE box, and meanwhile, the VCU analyzes the signals from the vehicle and then sends the command to the hybrid power system. The system is tested repeatedly at different speeds and torques to encompass the required full operating range of the system, the UC will be charged until their terminal voltages reach the preset value of 240v during one cycle by electric load equipment. Regeneration capability is a significant figure of the system, thus the test cycle consists of a period during which the torque is reversed with the speed held constant by the dynamometer. Thus the ultracapcitors can absorb and provide energy repeatedly, and the net input energy of the UC is zero. The proposed hybrid power system ensures good performance with available control strategy, thus VCU hardware is used to implement the control strategy, and a high performance Freescale micro control unit is selected as the main chip to ensure adequate computational performance. Some experiments are done on the test bench. Fig. 14 shows the picture of the delivered and regenerated power tested under the UDDS cycle while the negative quantities are the regenerated braking values. In the ideal energy storage system, all generated energy should be saved. However, in real applications some amount of energy always dissipates because of much limitation. The regeneration performance of the hybrid power system is also shown in these times in the Fig. 14. Fig. 15 shows the performance of regeneration and drivability of
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Fig. 15. (a). Regeneration performance in hybrid power system at high SOC. (b). Regeneration performance in hybrid power system at low SOC.
the system based on the Battery SOC and UC SOC under the UDDS driving cycle. The Fig. 15 (a) shows the performance at the high SOC of ultracapacitors and battery. The Fig. 15 (b) shows the performance at the low level SOC. In the two cases, driving energy is delivered in acceleration time and regenerated energy is reproduced with the regenerative braking method. The system can supply a percentage of required energy by recovering energy which is one of the biggest advantages of electric vehicles. Using UC can help reach the higher efficiency and get more green and free energy. Besides, the behavior of UC in charge and discharge situation gives more predictable index than the battery pack for the energy storage system. The important parameter which affects the process of regeneration is SOC level. The control strategy discharges the SOC of UC in acceleration time, so that the UC have enough capacity to absorb energy in deceleration time. For the efficiency of absorbing regenerative braking is related to the initial braking velocity and the time of braking, the UC cannot recover to total regenerative braking energy even at a higher speed, thus the efficiency at low speed is not high.
In Fig. 16 SOC, voltages and currents of the battery only system and Battery-UC hybrid system are presented and compared with each other. The red solid line represents the hybrid power system while the blue dotted line stands for the battery only system. It is shown that about an extra 30% of the battery capacity energy is saved with the hybrid power source in one UDDS cycle. The voltage and current of battery become smoother with the hybrid power source than that with the single power. Thus, the hybrid power system decreases the damage of peak current to the battery pack. Besides, the current is negative in the deceleration condition, which indicates that the hybrid power system absorbs energy. The ultracapacitors voltage and current under UDDS cycle are shown in the Fig. 17, the value of current is higher than that of battery. Thus the ultracapacitors can absorb the big current and provide small current for battery via DC/DC, and the damage of peak current is decreased for battery. The main aim of using the hybrid power is to improve the energy consumption and increase the driving range. The energy consumption and driving range of the hybrid power system and
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Fig. 16. Results under UDDS cycle.
Fig. 17. Ultra-capacitors voltage && current under UDDS cycle.
Table 2 Energy consumption and driving range comparison. Driving cycle (UDDS)
Battery only
Hybrid power
Improvement
Energy consumption (kwh/100 km) Driving range (km)
14.18 208
12.1 245
14.67% 17.80%
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Fig. 18. Current curve comparison.
battery only system are compared and shown in Table 2. We can see that the energy consumption of hybrid power system decreases 14.67% compared with the battery only system while the driving range extends from 208 km to 245 km, almost an improvement of 17.80%. This means that the hybrid power system with the fuzzy controller could increase more driving range than the battery only system by 17.80% with the same battery capacity. Hybrid power system with 66 A h battery capacity and 33F capacitance and battery only system with 78 A h are compared in another experiment. The results indicated that the driving range of these two EV is both 208 km. That means the capacity of battery can be reduced by about 15.38% while reducing the size of the battery at the same time. Since the weight of 78 A h battery is 428 kg and the weight of 66 A h battery is 362 kg, the weight can be reduced by about 15.42%. Besides, the discount amount of battery capacity is 4.56Kw.h, which is about 1689 dollars according to the price of the ternary lithium battery. The cost of hybrid power system is lower than the single power system, though the UC and DC/DC converter will add some cost, which is about 1020 dollars. The delivered currents are also very important for hybrid power system. As shown in Fig. 18, the battery currents charge-discharge curve of hybrid power system is more placid than the curve of the battery only system, thus protecting the battery health.
5. Conclusions The performance of electric vehicle relies mainly on the structure and management strategy. In this paper, hybrid power system and fuzzy logic based control strategy have been proposed. The experiment results show that the energy consumption of hybrid power system is decreased by 14.67%, and driving range is increased by 17.8% compared with single power system. Besides, the performance of charge and discharge of battery is also improved, and then the battery peak current is decreased. What's more, the weight of hybrid power system is lighter than the battery only system in the same driving range, 15.42% weight is decreased. Thus, the proposed hybrid power system and power management strategy improves the performance of electric vehicle apparently while efficiently controlling vehicle during different modes of operation.
Acknowledgments This research is also supported by Major Special Project of Science and Technology of Shandong Province (2015ZDXX0601A01). That is, this research is supported by National Natural Science Foundation of China (61273139), China Postdoctoral Science Foundation (2015t80733), Postdoctoral innovation project of Shandong Province (201403010), Natural Science Foundation of Shandong (ZR2014FP001), National Natural Science Foundation of China (61404011), Major Special Project of Science and Technology of Shandong Province (2015ZDXX0601A01) References [1] Kamil CB, Mehmet AG, Ahmet T. A comprehensive overview of hybrid electric vehicle: powertrain configurations, powertrain control techniques and electronic control units. Energy Convers Manag 2011;52:1305e13. [2] Yue H, Jun S, Weimin L, Yunlong P. A scientometric study of global electric vehicle research. Scientometrics 2014;98(2):1269e82. [3] Khayyam Hamid, Bab-Hadiashar Alireza. Adaptive intelligent energy management system of plug-in hybrid electric vehicle. Energy 2014;69:319e35. [4] Richardson DB. Electric vehicles and the electric grid: a review of modeling approaches, impacts and renewable energy integration. Renew Sustain Energy Rev 2013;19:247e54. [5] Shafiei E, Davidsdottir B, Leaver J, Stefansson H, Asgeirsson EI. Comparative analysis of hydrogen, biofuels and electricity transitional pathways to sustainable transport in a renewable-based energy system. Energy 2015;83: 614e27. [6] Fernandez IJ, Calvillo CF, Sanchez-Miralles A, Boal J. Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles. Energy 2013;60:35e43. [7] Joao JT, Paulo GP, Humberto MJ, Carlos HA. A multi-level energy management system for multi-source electric vehicles-An integrated rule-based metaheuristic approach. Appl Energy 2013;105:304e18. [8] Allegre AL, Bouscayrol A, Trigui R. Influence of control strategies on battery/ supercapacitor hybrid energy storage systems for traction applications. IEEE Veh Power Propuls Conf 2009:213e20. [9] Emadi A, Rajashekara K, Willisamson S, et al. Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations. IEEE Transac Veh Technol 2005;54:763e70. [10] Lukic SM, Wirasingha SG, Rodriguez F, et al. Power management of an ultracapacitor/battery hybrid energy storage system in an HEV. In: IEEE conference on vehicle power and propulsion conference, Windsor; 2006. p. 1e6. [11] Masoud M, Mohammad-Reza H, Vahid E, Ali S. Optimum sizing and optimum energy management of a hybrid energy storage system for lithium battery life improvement. J Power Sources 2013;244:2e10. [12] Alberto S, Aldo S, Constantina L. Power split strategies for hybrid energy storage systems for vehicular applications. J Power Sources 2014;258: 395e407. [13] Lei Z.; Dorrell D.G. Genetic Algorithm based optimal component sizing for an
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