考虑松弛和滞回的锂离子电池建模及SOC估计
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摘要
针对锂离子电池在电流状态突然变化时产生的松弛现象和滞回现象,在分析了电池等效电路模型的基础上,引入线性滤波器和滞回模块,建立了电池的自校正模型。通过恒流脉冲实验和动态应力工况测试验证自校正模型在对电池电压特性跟随的可靠性,并在该模型的基础上使用有限差分扩展卡尔曼滤波FDEKF(finite difference extended Kalman filter)算法实现了电池的荷电状态SOC(state of charge)估计。实验分析表明,自校正模型能较好地体现电池的动态特性,并使SOC估计保持很好的精度。
When the current state of a lithium-ion battery changes suddenly, relaxation and hysteresis will appear.In this paper, based on the analysis of an equivalent circuit model of battery, a linear filter and a hysteresis module were introduced, and a self-tuning model of battery was established. Then, constant-current pulse tests and dynamic stress test(DST) were conducted to verify the reliability of the self-tuning model in following the voltage characteristics of the battery. On the basis of the proposed model, the finite difference extended Kalman filter(FDEKF) algorithm was used to estimate the state of charge(SOC) of battery. Experimental results showed that the self-tuning model can better reflect the dynamic characteristics of the battery while keeping the SOC estimation with high accuracy.
When the current state of a lithium-ion battery changes suddenly, relaxation and hysteresis will appear.In this paper, based on the analysis of an equivalent circuit model of battery, a linear filter and a hysteresis module were introduced, and a self-tuning model of battery was established. Then, constant-current pulse tests and dynamic stress test(DST) were conducted to verify the reliability of the self-tuning model in following the voltage characteristics of the battery. On the basis of the proposed model, the finite difference extended Kalman filter(FDEKF) algorithm was used to estimate the state of charge(SOC) of battery. Experimental results showed that the self-tuning model can better reflect the dynamic characteristics of the battery while keeping the SOC estimation with high accuracy.
引文
[1]何仕品,朱建新.锂离子电池管理系统及其均衡模块的设计与研究[J].汽车工程,2009,31(5):444-447.He Shipin,Zhu Jianxin.Design and study of battery management system and its equalization module for li-ion batteries[J].Automotive Engineering,2009,31(5):444-447(in Chinese).
[2]Joo G K,Byungrak S,el al.A review of lithium and nonlithium based solid state batteries[J].Journal of Power Sources,2015,282(15):299-322.
[3]Dai Haifeng,Guo Pingjing,Wei Xuezhe,et al.ANFIS(adaptive neuro-fuzzy inference system)based online SOC(State of Charge)correction considering cell divergence for the EV(electric vehicle)traction batteries[J].Energy,2015,80:350-360.
[4]Liu Tianhua,Chen D F,Fang C C.Design and implementation of a battery charge with a state-of-charge estimator[J].International Journal of Electronics,2000,87(2):211-216.
[5]Fleischer C,Waag W,Heyn H M,et al.On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models:Part 1.Requirements,critical review of methods and modeling[J].Journal of Power Sources,2014,260(15):276-291.
[6]Zhang Fei,Liu Guangjun,Fang Lijin,et al.Estimation of battery state of charge with,observer:applied to a robot for inspecting power transmission lines[J].IEEE Transactions on Industrial Electronics,2012,59(2):1086-1095.
[7]Sheng Hanmin,Xiao Jian.Electric vehicle state of charge estimation:Nonlinear correlation and fuzzy support vector machine[J].Journal of Power Sources,2015,281:131-137.
[8]方明杰,王群京.基于扩展卡尔曼滤波算法的锂离子电池的SOC估算[J].电工电能新技术,2013,32(2):39-42.Fang Mingjie,Wang Qunjing.Strategy of estimating state of charge for Lithium ion battery based on extended Karlman filter[J].Advanced Technology of Electrical Engineering and Energy,2013,32(2):39-42(in Chinese).
[9]刘艳莉,戴胜,程泽,等.基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J].电工技术学报,2014,29(1):221-228.Liu Yanli,Dai Sheng,Cheng Ze,et al.Estimation of state of charge of lithium-ion battery based on finite difference extended kalman filter[J].Transactions of China Electrotec-hnical Society,2014,29(1):221-228(in Chinese).
[10]王笑天,杨志家,王英男,等.双卡尔曼滤波算法在锂电池SOC估算中的应用[J].仪器仪表学报,2013,34(8):1732-1738.Wang Xiaotian,Yang Zhijia,Wang Yingnan,et al.Application of dual extended Kalman filtering algorithm in the state-of-charge estimation of lithium-ion battery[J].Chinese Journal of Scientific Instrument,2013,34(8):1732-1738(in Chinese).
[11]魏学哲,杨静,刘耀锋,等.锂离子动力电池内阻模型与实验研究[J].同济大学学报:自然科学版,2015,43(10):1542-1549.Wei Xuezhe,Yang Jing,Liu Yaofeng,et al.Lithium-ion power battery internal resistance model and its experiment study[J].Journal of Tongji University:Natural Science,2015,43(10):1542-1549(in Chinese).
[12]李凯,秦文萍,张海涛,等.含混合储能的微电网能量管理系统控制策略[J].电力系统及其自动化学报,2016,28(10):85-91.Li Kai,Qin Wenping,Zhang Haitao,et al.Control strategy for microgrid energy management system including hybrid energy storage[J].Proceedings of the CSU-EPSA,2016,28(10):85-91(in Chinese).
[13]程泽,吕继考,刘继光,等.等效滞回模型在锂离子电池SOC估计中的应用[J].湖南大学学报:自然科学版,2015,42(4):63-70.Cheng Ze,Lv Jikao,Liu Jiguang,et al.Application of equivalent hysteresis model in estimation of state of charge of lithium-ion battery[J].Journal of Hunan University(Natural Sciences),2015,42(4):63-70(in Chinese).
[14]梁奇,于春梅,王顺利,等.基于PNGV电路模型的航空钴酸锂电池内阻研究[J].电源学报,2017,15(2):153-158.Liang Qi,Yu Chunmei,Wang Shunli,et al.Research on internal resistance of aviation battery based on PNGV circuit model[J].Journal of Power Supply,2017,15(2):153-158(in Chinese).
[15]Chen Xiaopeng,Shen Weixiang,Cao Zhenwei.Adaptive gain sliding mode observer for state of charge estimationbased on combined battery equivalent circuit model[J].Computers and Chemical Engineering,2014,64(18):114-123.
[16]Schei T S.A finite-difference method for linearization in nonlinear estimation algorithms[J].Automatica,1997,33(11):2053-2058.
[2]Joo G K,Byungrak S,el al.A review of lithium and nonlithium based solid state batteries[J].Journal of Power Sources,2015,282(15):299-322.
[3]Dai Haifeng,Guo Pingjing,Wei Xuezhe,et al.ANFIS(adaptive neuro-fuzzy inference system)based online SOC(State of Charge)correction considering cell divergence for the EV(electric vehicle)traction batteries[J].Energy,2015,80:350-360.
[4]Liu Tianhua,Chen D F,Fang C C.Design and implementation of a battery charge with a state-of-charge estimator[J].International Journal of Electronics,2000,87(2):211-216.
[5]Fleischer C,Waag W,Heyn H M,et al.On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models:Part 1.Requirements,critical review of methods and modeling[J].Journal of Power Sources,2014,260(15):276-291.
[6]Zhang Fei,Liu Guangjun,Fang Lijin,et al.Estimation of battery state of charge with,observer:applied to a robot for inspecting power transmission lines[J].IEEE Transactions on Industrial Electronics,2012,59(2):1086-1095.
[7]Sheng Hanmin,Xiao Jian.Electric vehicle state of charge estimation:Nonlinear correlation and fuzzy support vector machine[J].Journal of Power Sources,2015,281:131-137.
[8]方明杰,王群京.基于扩展卡尔曼滤波算法的锂离子电池的SOC估算[J].电工电能新技术,2013,32(2):39-42.Fang Mingjie,Wang Qunjing.Strategy of estimating state of charge for Lithium ion battery based on extended Karlman filter[J].Advanced Technology of Electrical Engineering and Energy,2013,32(2):39-42(in Chinese).
[9]刘艳莉,戴胜,程泽,等.基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J].电工技术学报,2014,29(1):221-228.Liu Yanli,Dai Sheng,Cheng Ze,et al.Estimation of state of charge of lithium-ion battery based on finite difference extended kalman filter[J].Transactions of China Electrotec-hnical Society,2014,29(1):221-228(in Chinese).
[10]王笑天,杨志家,王英男,等.双卡尔曼滤波算法在锂电池SOC估算中的应用[J].仪器仪表学报,2013,34(8):1732-1738.Wang Xiaotian,Yang Zhijia,Wang Yingnan,et al.Application of dual extended Kalman filtering algorithm in the state-of-charge estimation of lithium-ion battery[J].Chinese Journal of Scientific Instrument,2013,34(8):1732-1738(in Chinese).
[11]魏学哲,杨静,刘耀锋,等.锂离子动力电池内阻模型与实验研究[J].同济大学学报:自然科学版,2015,43(10):1542-1549.Wei Xuezhe,Yang Jing,Liu Yaofeng,et al.Lithium-ion power battery internal resistance model and its experiment study[J].Journal of Tongji University:Natural Science,2015,43(10):1542-1549(in Chinese).
[12]李凯,秦文萍,张海涛,等.含混合储能的微电网能量管理系统控制策略[J].电力系统及其自动化学报,2016,28(10):85-91.Li Kai,Qin Wenping,Zhang Haitao,et al.Control strategy for microgrid energy management system including hybrid energy storage[J].Proceedings of the CSU-EPSA,2016,28(10):85-91(in Chinese).
[13]程泽,吕继考,刘继光,等.等效滞回模型在锂离子电池SOC估计中的应用[J].湖南大学学报:自然科学版,2015,42(4):63-70.Cheng Ze,Lv Jikao,Liu Jiguang,et al.Application of equivalent hysteresis model in estimation of state of charge of lithium-ion battery[J].Journal of Hunan University(Natural Sciences),2015,42(4):63-70(in Chinese).
[14]梁奇,于春梅,王顺利,等.基于PNGV电路模型的航空钴酸锂电池内阻研究[J].电源学报,2017,15(2):153-158.Liang Qi,Yu Chunmei,Wang Shunli,et al.Research on internal resistance of aviation battery based on PNGV circuit model[J].Journal of Power Supply,2017,15(2):153-158(in Chinese).
[15]Chen Xiaopeng,Shen Weixiang,Cao Zhenwei.Adaptive gain sliding mode observer for state of charge estimationbased on combined battery equivalent circuit model[J].Computers and Chemical Engineering,2014,64(18):114-123.
[16]Schei T S.A finite-difference method for linearization in nonlinear estimation algorithms[J].Automatica,1997,33(11):2053-2058.