基于短时搁置端电压压降的快速锂离子电池健康状态预测

doi:10.19595/j.cnki.1000-6753.tces.181206

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Abstract The maximum available capacity is an important basis for indicating the State of Healthy (SOH) of lithium-ion battery. Upon analyzing the experiments results of the cycle life and the open circuit voltage, a novel healthy factor could represent the maximum available capacity was proposed in this paper to predict the SOH of the battery. The healthy factor of the paper mainly used the data relative with the terminal voltage drop during the battery rest for 10min after the battery was charged or discharge. Comparing with the traditional methods, the method of the paper does not limit the working conditions of the battery, and it can predict the SOH quickly. Besides, it does not need to discharge the battery to obtain the health factor. Furthermore, four regression prediction methods were used to model the relationship between health factors and the battery available capacity. Upon analyzing the performance and results of the regression neural networks, an improved weighted hybrid neural network was proposed. The experimental results show that the proposed health factor can be used to characterize SOH of the battery and it is robust to the working conditions. The weighted hybrid neural network of the paper can achieve high precision for SOH prediction.

Key words： Lithium-ion battery terminal voltage drop SOH regression prediction

Received: 11 July 2018 Published: 12 October 2019

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TM912

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	Guo Yongfang
	Huang Kai
	Li Zhigang

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Guo Yongfang,Huang Kai,Li Zhigang. Fast State of Health Prediction of Lithium-Ion Battery Based on Terminal Voltage Drop During Rest for Short Time[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 3968-3978.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181206 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I19/3968

[1] Zheng Linfeng, Zhang Lei, Zhu Jianguo, et al.Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model[J]. Applied Energy, 2016, 180: 424-434.
[2] 李晓宇, 徐佳宁, 胡泽徽, 等. 磷酸铁锂电池梯次利用健康特征参数提取方法[J]. 电工技术学报, 2018, 33(1): 9-16.
Li Xiaoyu, Xu Jianing, Hu Zehui, et al.The health parameter estimation method for LiFePO₄ battery echelon use[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 9-16.
[3] Guo Jian, Li Zhaojun, Pecht M.A Bayesian approach for Li-ion battery capacity fade modeling and cycles to failure prognostics[J]. Journal of Power Sources, 2015, 281: 173-184.
[4] 孙冬, 许爽. 梯次利用锂电池健康状态预测[J]. 电工技术学报, 2018, 33(9): 2121-2129.
Sun Dong, Xu Shuang.State of health prediction of second-use lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2121-2129.
[5] 程泽, 孙幸勉, 程思璐. 一种锂离子电池荷电状态估计与功率预测方法[J]. 电工技术学报, 2017, 32(15): 180-189.
Cheng Ze, Sun Xingmian, Cheng Silu.Method for estimation of state of charge and power prediction of lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2017, 32(15): 180-189.
[6] 潘海鸿, 吕治强, 李君子, 等. 基于灰色扩展卡尔曼滤波的锂离子电池核电状态估算[J]. 电工技术学报. 2017, 32(21): 1-8.
Pan Haihong, lü Zhiqiang, Li Junzi, et al. Estimation of lithium-ion battery state of charge based on grey prediction model-extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 1-8.
[7] Zou Changfu, Manzie C, Nesic D, et al.Multi-time-scale observer design for state-of-charge and state-of-health of a lithium battery[J]. Journal of Power Sources, 2016, 335: 121-130.
[8] Berecibar M, Germandia M, Gandiage I, et al.State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application[J]. Energy, 2016, 103: 784-796.
[9] Remmlinger J, Buchholz M, Meiler M, et al.State of health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation[J]. Journal of Power Sources, 2011, 196(12): 57-63.
[10] Groenewald J, Grandjean T, Marco J.Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles[J]. Renewable and Sustainable Energy Reviewa, 2017, 69: 98-111.
[11] 黄凯, 郭永芳, 李志刚. 动力锂离子电池荷电状态估计综述[J]. 电源技术, 2018, 42(9): 1398-1401.
Huang Kai, Guo Yongfang, Li Zhigang.Review of state of charge estimation methods for power lithium-ion battery[J]. Chinese Journal of Power Ssources, 2018, 42(9): 1398-1401.
[12] He Wei, Williard N, Chen Chaochao, et al.State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation[J]. Electrical Power and Energy Systems, 2014, 62: 783-791.
[13] Tong S J, Klein M P, Park J W.On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health estimation[J]. Journal of Power Sources, 2015, 293: 416-428.
[14] Farmann A, Sauer D U.A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries[J]. Journal of Power Sources, 2017, 347: 1-13.
[15] 薛楠, 孙丙香, 白恺, 等. 基于容量增量分析的复合材料锂电池分区间循环衰退机理[J]. 电工技术学报, 2017, 32(13): 145-152.
Xue Nan, Sun Bingxiang, Bai Kai, et al.Different state of charge range cycle degradation mechanism of composite material lithium-ion batteries based on incremental capacity analysis[J]. Transactions of China Electrotechnical Society, 2017, 32(13): 145-152.
[16] 姜久春, 马泽宇, 李雪, 等. 基于开路电压特性的动力电池健康状态诊断与估计[J]. 北京交通大学学报, 2016, 40(4): 92-98.
Jiang Jiuchun, Ma Zeyu, Li Xue, et al.State of health diagnosis and estimation of power lithium-ion batteries based on open circuit voltage characteristic[J]. Journal of Beijing Jiaotong University, 2016, 40(4): 92-98.
[17] Liu Datong, Zhou Jianbao, Liao Haitao, et al.A health Indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems, Man, and Cybernetics Systems, 2015, 45(6): 915-928.
[18] 韦海燕, 陈孝杰, 吕治强, 等. 灰色神经网络模型在线估算锂离子电池SOH[J].电网技术, 2017, 41(12): 4038-4044.
Wei Haiyan, Chen Xiaojie, Lü Zhiqiang, et al.Online estimation of lithium-ion battery state of health using grey neural network[J]. Power System Technology, 2017, 41(12): 4038-4044.
[19] 刘大同, 周建宝, 郭力萌, 等. 锂离子电池健康评估和寿命预测综述[J]. 仪器仪表学报, 2015, 36(1): 1-16.
Liu Datong, Zhou Jianbao, Guo Limeng, et al.Survey on lithium-ion battery health assessment and cycle life estimation[J]. Chinese Journal of Scientific Instrument, 2015, 36(1): 1-16.
[20] Ma Zeyu, Jiang Jiuchun, Shi Wei, et al.Investigation of path dependence in commercial lithium-ion cells for pure electric bus applications: aging mechanism identification[J]. Journal of Power Sources, 2015, 274: 29-40.
[21] Gregory L P.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.
[22] Xia Bizhong, Cui Deyu, Sun Zhen, et al.State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network[J]. Energy, 2018, 153: 694-705.
[23] Dang Xuanju, Yan Li, Xu Kai, et al.Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model[J]. Electrochimica Acta, 2016, 188: 356-366.
[24] Cai Yihan, Lin Yang, Deng Zhongwei, et al.Online identification of Lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine[J]. Energy, 2018, 147: 621-635.
[25] 赵超, 郑守锦. 基于K均值小波神经网络的二阶段空调负荷预测[J]. 福州大学学报(自然科学版), 2018, 46(3): 416-421.
Zhao Chao, Zheng Shoujin.A two-stage prediction for air-conditioning load base on K-means wavelet neural network[J]. Journal of Fuzhou University (Natural Science Edition), 2018, 46(3): 416-421.
[26] Chang W Y.Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA[J]. International Journal of Electrical Power & Energy Systems, 2013, 53: 603-611.
[27] 黄俊铭, 朱建全, 庄远灿. 基于动态RBF神经网络的广义电力负荷建模[J]. 电网技术, 2018, 42(2): 591-597.
Huang Junming, Zhu Jianquan, Zhuang Yuancan.Generalized power load modeling based on dynamic RBF neural network[J]. Power System Technology, 2018, 42(2): 591-597.
[28] Bendu H, Deepak B B V L, Murugan S. Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN-PSO[J]. Applied Energy, 2017, 154: 38-45.