基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计

doi:10.19595/j.cnki.1000-6753.tces.210950

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Abstract The traditional extended Kalman filter（EKF）algorithm has low accuracy in estimating the state of charge（SOC）of lithium-ion battery under the non-Gaussian noise interference. Therefore, a new extended Kalman filter (MCC-EKF) algorithm based on maximum correlation-entropy criterion was proposed. Firstly, the Thevenin equivalent circuit of the lithium-ion battery was model and its parameters was identified. Secondly, the proposed algorithm MCC-EKF and EKF algorithm were used to estimate the SOC under different noise interference. The experimental results show that, compared with the EKF algorithm, the running time of the new algorithm increases by 0.282s and the estimation accuracy increases by 19% under Gaussian noise interference; under non-Gaussian noise interference, the running time of the new algorithm increases by 0.418s and the estimation accuracy increases by 51%. In addition, given the wrong initial SOC value, the new algorithm can converge to the true value within 10s after the battery starts working, indicating that the new algorithm has better robustness. The proposed algorithm has high estimation accuracy and good robustness while the increase of running time is small, and it is an effective SOC estimation method.

Key words： Lithium-ion battery parameter identification non-Gaussian noise state of charge

Received: 30 June 2021

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TM912

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	Articles by authors
	Wu Chunling
	Hu Wenbo
	Meng Jinhao
	Liu Zhixuan
	Cheng Yanqing

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Wu Chunling,Hu Wenbo,Meng Jinhao等. State of Charge Estimation of Lithium-Ion Batteries Based on Maximum Correlation-Entropy Criterion Extended Kalman Filtering Algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5165-5175.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210950 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I24/5165

[1] Hannan M A, Lipu M S H, Hussain A, et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations[J]. Renewable & Sustainable Energy Reviews, 2017, 78: 834-854.
[2] 孙冬, 许爽, 李超, 等. 锂离子电池荷电状态估计方法综述[J]. 电池, 2018, 48(4): 284-287.
Sun Dong, Xu Shuang, Li Chao, et al.Review of state of charge estimation method for Li-ion battery[J]. Battery Bimonthly, 2018, 48(4): 284-287.
[3] 刘芳, 马杰, 苏卫星, 等. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法[J]. 电工技术学报, 2020, 35(4): 698-707.
Liu Fang, Ma Jie, Su Weixing, et al.State of charge estimation method of electric vehicle power battery life cycle based on auto regression extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707.
[4] 王榘, 熊瑞, 穆浩. 温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计[J]. 电工技术学报, 2020, 35(23): 4980-4987.
Wang Ju, Xiong Rui, Mu Hao.Co-estimation of lithium-ion battery state-of-charge and capacity through the temperature and aging awareness model for electric vehicles[J]. Transactions of China Elec- trotechnical Society, 2020, 35(23): 4980-4987.
[5] 庞辉, 郭龙, 武龙星, 等. 考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算[J]. 电工技术学报, 2021, 36(10): 2178-2189.
Pang Hui, Guo Long, Wu Longxing, et al.An improved dual polarization model of Li-ion battery and its state of charge estimation considering ambient temperature[J]. Transactions of China Electro- technical Society, 2021, 36(10): 2178-2189.
[6] 李建林, 李雅欣, 吕超, 等. 退役动力电池梯次利用关键技术及现状分析[J]. 电力系统自动化, 2020, 44(13): 172-183.
Li Jianlin, Li Yaxin, Lü Chao, et al.Key technology and research status of cascaded utilization in decommissioned power battery[J]. Automation of Electric Power Systems, 2020, 44(13): 172-183.
[7] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. part 1. background[J]. Journal of Power Sources, 2004, 134(2): 252-261.
[8] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. part 2. modeling and identification[J]. Journal of Power Sources, 2004, 134(2): 262-276.
[9] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. part 3. state and parameter estimation[J]. Journal of Power Sources, 2004, 134(2): 277-292.
[10] 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
[11] 宫明辉, 乌江, 焦朝勇. 基于模糊自适应扩展卡尔曼滤波器的锂电池SOC估算方法[J]. 电工技术学报, 2020, 35(18): 3972-3978.
Gong Minghui, Wu Jiang, Jiao Chaoyong.SOC estimation method of lithium battery based on fuzzy adaptive extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3972-3978.
[12] Xiong Rui, Gong Xianzhi, Mi C C, et al.A robust state-of-charge estimator for multiple types of lithium- ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources, 2013, 243: 805-816.
[13] Sun Fengchun, Hu Xiaosong, Zou Yuan, et al.Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles[J]. Energy, 2011, 36(5): 3531-3540.
[14] 魏克新, 陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J]. 中国电机工程学报, 2014, 34(3): 445-452.
Wei Kexin, Chen Qiaoyan.States estimation of Li-ion power batteries based on adaptive unscented Kalman filters[J]. Proceedings of the CSEE, 2014, 34(3): 445-452.
[15] He Wei, Williard N, Chen Chaochao, et al.State of charge estimation for electric vehicle batteries using unscented Kalman filtering[J]. Microelectronics Reliability, 2013, 53(6): 840-847.
[16] 陈息坤, 孙冬, 陈小虎. 锂离子电池建模及其荷电状态鲁棒估计[J]. 电工技术学报, 2015, 30(15): 141-147.
Chen Xikun, Sun Dong, Chen Xiaohu.Modeling and state of charge robust estimation for lithium-ion batteries[J]. Transactions of China Electrotechnical Society, 2015, 30(15): 141-147.
[17] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964.
Xie Changjun, Fei Yalong, Zeng Chunnian, et al.State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle[J]. Transa- ctions of China Electrotechnical Society, 2018, 33(17): 3958-3964.
[18] He Hongwen, Xiong Rui, Guo Hongqiang, et al.Comparison study on the battery models used for the energy management of batteries in electric vehicles[J]. Energy Conversion and Management, 2012, 64: 113-121.
[19] 张廷, 胡社教. 基于改进Thevenin模型锂电池SOC估算方法[J]. 电源技术, 2015, 39(11): 2400-2402, 2496.
Zhang Ting, Hu Shejiao.SOC estimation algorithm of lithium-ion battery based on improved Thevenin model[J]. Chinese Journal of Power Sources, 2015, 39(11): 2400-2402, 2496.
[20] 李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(3): 321-327, 343.
Li Wei, Liu Weiwei, Deng Yelin.SOC estimation for lithium-ion batteries based on EKF[J]. China Mecha- nical Engineering, 2020, 31(3): 321-327, 343.
[21] Chen Badong, Liu Xi, Zhao Haiquan, et al.Maximum correntropy Kalman filter[J]. Automatica, 2017, 76: 70-77.
[22] Shi Wanlu, Li Yingsong, Wang Yanyan.Noise-free maximum correntropy criterion algorithm in non- Gaussian environment[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2020, 67(10): 2224-2228.