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2018 2nd International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA 2018) ISBN: 978-1-60595-594-0

Battery Equalization Control Strategy Based on Fuzzy Control

Yao WANG

1

, Li-fei SHEN

1

, Xiao MA

2

, Qing TAO

2

and Dan-feng QIU

1,2,* 1

Industry Development Promotion Center, Ministry of industry and information technology of the People’s Republic of China, Beijing, 100846, China

2

Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Education, College of Electronic and Information Engineering, Nanjing University of

Aeronautics and Astronautics, Nanjing, 210016, China

*Corresponding author

Keywords: Battery equalization, Fuzzy control, MATLAB/Simulink.

Abstract. With the pollution of the environment and the shortage of energy, electric vehicles have been widely concerned in the world. Power battery is the most important energy storage element in electric vehicle. In the process of the use of power batteries, parameters such as the voltage and capacity between the batteries are different. The battery is prone to overcharging and over-discharging, which seriously affects the cycle life of the battery. Therefore, it is necessary to design a battery equalization system to balance the parameters between batteries. Based on fuzzy control, this paper designs a battery equalization system which can automatically adjust the balance current according to the state of the batteries and uses the Simulink module in MATLAB to simulate the system. The experimental results show that the system can effectively adjust the battery equalization current which fully meets the design requirements.

Introduction

In recent years, energy is becoming increasingly scarce throughout the world. Energy conservation and environmental protection have become the major social issues of concern in every country. At present, the traditional fuel automobiles still occupy most of the market share. However, traditional cars consume a lot of oil and exhaust a lot of gas during driving, which makes the environmental pollution more and more serious. The electric vehicle with high efficiency, energy saving, low emission or zero emission has regained its vitality and has received extensive attention from all over the world. As the key component of electric vehicles, battery performance directly determines the performance of the pure electric vehicles and the hybrid electric vehicles, so the development of the electric vehicles depend on the development of the battery technology. However, the current power storage battery has not made breakthrough in power, environmental protection, cycle life, cost and so on, which has become the bottleneck of the industrialization and marketization of electric vehicles.

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power and efficiency of the whole battery packs [6-8]. Therefore, battery power balance is of great importance to BMS (battery management system).

Because the ordinary mathematical model cannot accurately describe the state of the battery, the fuzzy control method is used to design a battery equalization system in this paper, and the system is simulated with the Simulink module in MATLAB. The experimental results show that the designed system can control the equalization current of the battery.

Common Equalization Methods

Equalization Index

The capacity of the lithium battery packs is the same as the capacity of the lithium battery with the smallest capacity, so the difference in capacity will directly affect the battery cycle life. In order to ensure the maximum performance of the battery groups, the balance control of the battery packs must be carried out. Fulfilled by the energy transfer between energy storage elements, balance equalization scheme is designed to reduce the balance loss and improve the efficiency of equalization. At present, there are three commonly used equalization indicators: battery voltage, battery capacity and battery SOC (state of charge).

1) Equalization based on battery voltage: This method balances the abnormal battery voltage in the battery packs. In the charge and discharge process of the lithium battery, the voltage of each battery is detected in real time. If the voltage of one of the batteries is not consistent with the voltage of other batteries, the system will start the balance module and balance the battery until the battery voltage is restored to normal. Because battery voltage is easy to measure and intuitive, many studies use battery voltage as an evaluation index [9]. However, it is easy to be affected by the internal parameters of the battery, which will lead to uncertainty in judgment.

2) Equalization based on battery capacity: The equalization scheme takes battery capacity as the research object and improves capacity utilization as the research objective. Because this method keeps the capacity of the battery consistent, it is a method that best fits the equalization purpose. The advantage is that capacity maximization can be achieved. However, battery capacity can only be obtained under static conditions, so it is not suitable for online balancing [10]. Therefore, the method based on battery capacity is no longer used.

3) Equalization based on battery SOC: The principle of this method is to balance the batteries with different states. During the charging and discharging process of the battery pack, the system can detect the state of each battery in real time. When the state of a battery in the battery group is different, the balance module will be started and the battery of the abnormal state is balanced. The hardware circuit of this control method is simple and the equalization effect is better for series batteries [11]. However, in the process of charging and discharging, it is difficult to determine the charging and discharging model of lithium batteries.

Equalization Technology

The equalization technology classification in reality includes passive equalization and active equalization. Generally, passive equalization is achieved by power dissipation or external power devices and active equalization is achieved by the transfer of battery energy in the battery groups.

The scheme of resistance energy consumption equalization in passive equalization is more mature in practical application. The balanced circuit parallels each single cell with a discharging resistor, using resistance shunt to suppress the rise of the battery voltage, which will release the energy of unevenly high energy batteries in the form of heat. However, passive equalization is a kind of method that dissipates the excess quantity of lithium battery directly in the form of heat. Energy will be lost, the efficiency is low, and the effect of heat must be prevented. Therefore, the passive equalization method is less used in practice.

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include switched capacitance circuit, shared energy conversion equalization circuit, Buck-Boost balancing circuit, bidirectional isolation DC/DC equalization circuit [12].

In this paper, the Buck-Boost equalization circuit is used. The circuit topology is shown in Figure1, Take the energy transfer between Bat1 and Bat2 as an example, assuming that the SOC of the Bat1 is higher, the MOSFET switch tube Q1 is opened, the MOSFET switch tube Q2 is turned off, Bat1 will be discharged. The inductance L1 stores the energy and charges Bat2. This state will repeat until SOC of two batteries tend to be consistent, that is to achieve the balance betweenBat1 and Bat2. Similarly, the equalization of other neighboring battery can be realized and the SOC equalization of all batteries in the whole battery pack will eventually be achieved.

Figure 1. Buck-boost equalization circuit.

Design of Fuzzy Controller for Battery Equalization System

Because of the use of hundreds of thousands of lithium ion batteries in electric vehicles, ordinary mathematical models can not accurately describe the controlled objects, so the conventional control theory and control algorithms cannot solve the equalization problem of lithium ion batteries. Compared with the traditional controller, fuzzy controller has the following characteristics:

1) The use of language methods does not require accurate mathematical models, and its structural parameters are generally not clear.

2) Fuzzy control is based on heuristic knowledge and language decision rules, simulating the process and method of manual control, enhancing the adaptability of the system.

3) The fuzzy control system has strong robustness, and the changes of interference and parameters have little influence on the system. It is more suitable for controlling nonlinear, strong coupling, time-varying and lagging systems [13].

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flexible according to the voltage distribution of each monomer in the battery pack [14]. The fuzzy control algorithm meets this requirement.

[image:4.595.60.543.103.440.2]

Figure 2. OCV-SOC.

Figure 3. The basic structure of fuzzy controller.

The fuzzy controller is the most important part of the whole fuzzy control system [15]. The design of the fuzzy controller mainly includes four steps: determining the input and output variables, the fuzzification of the exact quantity, establishing fuzzy rules and defuzzification, as shown in Figure3.

To design a reasonable fuzzy controller, the input and output of the controller will first be defined. The fuzzy controller in this paper contains two input variables and one output variable. The input variables include two parts: the voltage difference between the single cell U and the average battery voltage U . The output variable Ieq is the balanced current. The input variable

expression is shown in the following form.

1

i i

U U U

   (1)

1 +

2 i i

U U

U   (2)

U

 determines the time of equalization. In this paper, the experimental environment is set at room temperature (25℃). The lower limit of voltage difference threshold is selected as 50mV, the upper limit of voltage difference threshold is selected as 100mV. The basic domain and the corresponding fuzzy set are shown below.

U

 = [50, 60, 70, 80, 90, 100] (3)

F

U

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U affects the efficiency and safety of the battery work. The value of battery voltage to a certain extent reflects the state of charge of the battery to a certain extent. It can be seen from Figure2 that when the battery voltage is high, SOC is also higher. When the battery voltage is low, SOC is also lower. These two states are not suitable for large current charging and discharging. The optimum voltage range for large current charging and discharging is about 3.2V-3.6V. In this paper, the average voltage domain is 2.9-3.8V, the basic domain and the corresponding fuzzy set are shown below.

U = [2.9, 3.2, 3.5, 3.8] (5)

F

U = {S M B} (6)

The output variable Ieq is related to the two input variables U and U . The equalization

current determines the equalization time of the battery directly. The balancing current is small, the equalization time is long, the balancing current is large, and the equalization time is short. However, if the equalization current is set too small, it will greatly extend the balance time and reduce the efficiency. If the balanced current is too large, it may lead to the excessive increase of the battery temperature and lead to the heat imbalance. Therefore, it is very necessary to accurately design the balance current of the battery. This paper fully considers the actual value range of the battery and design the domain to be [0,5], and the basic domain and the corresponding fuzzy set are as follows.

eq

I = [0, 1, 2, 3, 4, 5] (7)

eq F

[image:5.595.119.477.472.548.2]

I = {VS MS Z MB VB} (8) Fuzzy control rule is a set of fuzzy conditional statements based on the actual experience in the control process. The fuzzy control rules of the fuzzy controller of the battery balanced current designed in this paper are 15, as shown in the following table.

Table 1. The strategy table of fuzzy control.

eq

IU

VS MS Z MB VB

U S Z Z MB VB VB

M MS MS Z MB MB

B VS VS MS Z Z

Construction and Simulation of Battery Equalization System

In the actual construction of control system, quantitative indicators are needed to determine whether the equalization system needs to be activated. In this paper, when the voltage difference between any two battery modules is greater than 100mV, UiUj 100mV , the system starts the equalization module, and when the voltage difference between any two battery modules is less than 50mV, UiUj 50mV , the system closes the balance module. When the system judges that the battery needs to be balanced, the system sends instructions to the equalization circuit to realize the dynamic adjustment of the balanced current.

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[image:6.595.133.487.108.492.2]

balance of the power battery groups. The following figure (Figure4) shows the fuzzy controller in the equalization control module.

Figure 4. Fuzzy controller in the equalization control module.

Simulation Results Analysis

Figure (5a) shows the charging voltage of 5 single batteries charged by same constant current with no equalization system. It can be clearly seen from the diagram that the imbalance between the batteries at the beginning of the charging is small, and this imbalance increases slowly over time. The initial voltage of No.1 battery in the picture is higher, and the charging speed is the fastest; No.2, 3, 4 battery states are similar; the initial battery voltage of No.5 battery is lower and the charging speed is the slowest. When the time reaches 200s, there is a voltage difference between battery No.1 and 5. When the time reaches 1700s, the voltage difference is even close to 200mV. At 1700s, the battery is depolarized for a period of time and then recharged by 1C constant current. The imbalance still exists. Larger imbalance between batteries will cause greater damage to batteries.

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(a)

[image:7.595.176.418.67.471.2]

(b)

Figure 5. Results of simulation.

Conclusion

Due to the time-varying and nonlinear characteristics of the electric vehicle battery packs, the traditional mathematical model cannot accurately describe the working state of the battery. In this paper, based on the actual control experience of battery balance, a fuzzy rule control library is constructed, and a battery balance control system based on fuzzy control is proposed, and the system is simulated by Simulink. According to the simulation results of MATLAB, the fuzzy controller can well control the switch of the balance module and the equalization current. It effectively solves the imbalance between the batteries, and has certain practical significance for the battery safety and charge discharge efficiency.

Acknowledgement

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References

[1] L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A review on the key issues for lithium-ion battery management in electric vehicles,” J. Power Sources 226, 2013, pp.272–288.

[2] Waag W, Fleischer C, Sauer DU. “Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles,” J Power Sources 258, 2014, pp. 321–339.

[3] Gallardo-Lozano, J. Romero-Cadaval, E. Milanes-Montero, MI. Guerrero-Martinez, MA. “Battery equalization active methods,” Journal of Power Sources 246, 2014, pp. 934-949.

[4] Ma Yan, Duan Peng, Sun Yanshuai, Chen Hong. “Equalization of lithium-ion battery pack based on fuzzy logic control in electric vehicle,”. IEEE Transaction on Industrial Electronics 65, 2018, pp. 6762-6771.

[5] Zheng Jian, Chen Jian, Ouyang Quan. “Variable universe fuzzy control for battery equalization,” Journal of Systems Science &Complexity 31, 2018, pp. 325-342.

[6] Jin Fan, Ma Chen, Chen Xuebo. “Fuzzy-PID control for lithium battery equalization,” in International Conference on Control System and Automation, CSA2013, Changsha, China, 2013.

[7] Dong Bo, Li Ye, Han Yehui. “Parallel architecture for battery charge equalization,”. IEEE Transaction on Power Electronics 30, 2015, pp. 4906-4913.

[8] MDaowd, N Omar, PVD Bossche, JV Mierlo. “Passive and active battery balancing comparison based on MATLAB simulation,” in 2011 IEEE Vehicle Power and Propulsion Conference, VPPC2011, Chicago, USA, 2011.

[9] Wang S Z, Yang S G, Sun G F, Liu Q L. “Based on the fuzzy control of self-tuning fuzzy PID design and simulation,” in 2017 Chinese Automation Congress, CAC2017, China, 2017.

[10]Chen J, Xu C F, Wu C S, Xu W H. “Adaptive Fuzzy Logic Control of Fuel-Cell-Battery Hybrid Systems for Electric Vehicles”. IEEE Transaction on Industrial Informatics 01, 2018, pp. 292-300.

[11]Zhang E Q, Shi S J, Gao W H, Weng Z X. Recent researches and development on fuzzy control system[J]. Control Theory & Applications 02, 2001, pp. 7-11.

[12]Martinez DA, Poveda JD, Montenegro D. “Li-ion battery management system based in fuzzy logic for improving electric vehicle autonomy,” in 3rd IEEE Workshop on Power Electronics and Power Quality Applications, PEPQA2017, Bogota, Colombia, 2017.

[13]Kim Jonghoon. “Fuzzy logic-controlled online state-of-health (SOH) prediction in large format LiMn2O4 cell for energy storage system (ESS) applications,” IEEE International Conference on Industrial Technology, ICIT2014, Busan, South Korea, 2014.

[14]Samadi MF, Salf M. “Takagi-Sugeno fuzzy model identification of Li-ion battery system,” in 2014 World Automation Congress, WAC2014, Waikoloa, USA, 2014.

Figure

Figure 3. The basic structure of fuzzy controller.
Table 1. The strategy table of fuzzy control.
Figure 4. Fuzzy controller in the equalization control module.
Figure 5. Results of simulation.

References

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