变温度下IHF-IGPR框架的锂离子电池健康状态预测方法

doi:10.19595/j.cnki.1000-6753.tces.201593

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Abstract By researching the correlation between the capacity degradation of lithium-ion batteries and the trends of current, voltage, temperature and other parameters during charging and discharging, a state of health (SOH) prediction method of lithium-ion batteries based on the extracting of indirect health features and improved Gaussian process regression (GPR) model under different temperature conditions is proposed. Considering the problem of difficulty in extracting IHF under variable temperature, the HF in the voltage and time curve is adaptively obtained by random number method. In addition, the GPR model was established with the rational quadratic covariance as the kernel function to solve the problem of capacity regeneration, among which the conjugate gradient algorithm was used to optimize the GPR model. Finally, two evaluation metrics, root mean square error (RMSE) and mean absolute percentage error (MAPE), were set up to validate the proposed health feature (HF) and degradation model with single-cell and multi-cell experiments on battery datasets at different temperatures, and the results show that the IGPR model of the CVD-ETS at room temperature and DTD-EVS at high (low) temperature are the most effective way to predict the nonlinear trend of capacity degradation for lithium-ion batteries, which has advantage in small sample size, high prediction accuracy and wide applicability.

Key words： Indirect health features improved gaussian process regression capacity degradation lithium-ion batteries state of health (SOH) prediction

Received: 04 December 2020

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TM911

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	Han Qiaoni
	Jiang Fan
	Cheng Ze

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Han Qiaoni,Jiang Fan,Cheng Ze. State of Health Estimation for Lithium-Ion Batteries Based on the Framework of IHF-IGPR under Variable Temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3705-3720.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201593 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I17/3705

[1] 闫金定. 锂离子电池发展现状及其前景分析[J]. 航空学报, 2014, 35(10): 2767-2775.
Yan Jinding.Current status and development analysis of lithium-ion batteries[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(10): 2767-2775.
[2] 王鹏博, 郑俊超. 锂离子电池的发展现状及展望[J].自然杂志, 2017, 39(4): 283-289.
Wang Pengbo, Zheng Junchao.The present situation and expectation of lithium-ion battery[J]. Chinese Journal of Nature, 2017, 39(4): 283-289.
[3] 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 Electrotechnical Society, 2014, 29(1): 221-228.
[4] 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.
[5] Li Li, Liu Kailong.Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109254.
[6] Farmann A, Waag W, Marongiu A, et al.Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles[J]. Journal of Power Sources, 2015, 281: 114-130.
[7] Lu Languang, Han Xuebing, Li Jianqiu, et al.A review on the key issues for lithium-ion battery management in electric vehicles[J]. Journal of Power Sources, 2013, 226: 272-288.
[8] Hossain L M S, Hannan M A, Aini H, et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations[J]. Journal of Cleaner Production, 2018, 205: 115-133.
[9] Ungurean L, Cârstoiu G, Micea M V, et al.Battery state of health estimation: a structured review of models, methods and commercial devices[J]. International Journal of Energy Research, 2017, 41(2): 151-181.
[10] Tian Huixin, Qin Pengliang, Li Kun, et al.A review of the state of health for lithium-ion batteries: Research status and suggestions[J]. Journal of Cleaner Production, 2020, 261: 120813.
[11] 孙丙香, 刘佳, 韩智强, 等. 不同区间衰退路径下锂离子电池的性能相关性及温度适用性分析[J]. 电工技术学报, 2020, 35(9): 2063-2073.
Sun Bingxiang, Liu Jia, Han Zhiqiang, et al.Performance correlation and temperature applicability of lithium-ion batteries under different range degradation paths[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2063-2073.
[12] Ng K S, Moo C S, Chen Yiping, et al.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy, 2009, 86(9): 1506-1511.
[13] Xing Yinjiao, He Wei, Michael P, et al.State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J]. Applied Energy, 2014, 113(1): 106-115.
[14] Xia Zhiyong, Abu-Qahouq J A. Adaptive and fast state of health estimation method for lithium-ion batteries using online complex impedance and artificial neural network[C]//2019 IEEE Applied Power Electronics Conference and Exposition (APEC), Anaheim, CA, USA, 2019: 3361-3365.
[15] Li S E, Wang Baojing, Peng Hui, et al.An electrochemistry-based impedance model for lithium-ion batteries[J]. Journal of Power Sources, 2014, 258: 9-18.
[16] 蒋璐, 李奇, 陈维荣, 等. 基于Nelder-Mead优化的PEMFC三阶RQ等效电路参数辨识研究[J]. 电源学报, 2019, 17(2): 12-18.
Jiang Lu, Li Qi, Chen Weirong, et al.Research on parameter identification of third-order RQ equivalent circuit of PEMFC based on Nelder-Mead optimization[J]. Journal of Power Supply, 2019, 17(2): 12-18.
[17] 傅祎飏, 李锐华, 胡波, 等. 一种参数化电池模拟器模型及参数辨识方法[J]. 电气技术, 2020, 21(11): 7-12.
Fu Yiyang, Li Ruihua, Hu Bo, et al.A parameterized battery model for battery emulator and parameter identification method[J]. Electrical Engineering, 2020, 21(11): 7-12.
[18] Yang Duo, Wang Yujie, Pan Rui, et al.A neural network based on state-of-health estimation of lithium-ion battery in electric vehicles[J]. Energy Procedia, 2017, 105: 2059-2064.
[19] Xu Xiaodong, Yu Chuanqiang, Tang Shengjin, et al.State-of-health estimation for lithium-ion batteries based on wiener process with modeling the relaxation effect[J]. IEEE Access, 2019, 7: 105186-105201.
[20] Guo Peiyao, Cheng Ze, Yang Lei.A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources, 2019, 412: 442-450.
[21] 焦自权, 范兴明, 张鑫, 等. 基于改进粒子滤波算法的锂离子电池状态跟踪与剩余使用寿命预测方法[J]. 电工技术学报, 2020, 35(18): 3979-3993.
Jiao Ziquan, Fan Xingming, Zhang Xin, et al.State tracking and remaining useful life predictive method of lithium-ion battery based on improved particle filter algorithm[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3979-3993.
[22] 陈琳, 陈静, 王惠民, 等. 基于小波包能量熵的电池剩余寿命预[J]. 电工技术学报, 2020, 35(8): 1827-1835.
Chen Lin, Chen Jing, Wang Huiming, et al.Prediction of battery remaining useful life based on wavelet packet energy entropy[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1827-1835.
[23] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[24] Widodo A, Shim M C, Caesarendra W, et al.Intelligent prognostics for battery health monitoring based on sample entropy[J]. Expert Systems with Applications an International Journal, 2011, 38(9): 11763-11769.
[25] Li Junfu, Liu Chao, Wang Lixin, et al.Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter[J]. Journal of Power Sources, 2014, 268: 895-903.
[26] 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 & Cybernetics Systems, 2015, 45(6): 915-928.
[27] 刘燕, 任航永, 张健. 模拟退化算法在飞机巡航路径问题中的应用[J]. 光电技术应用, 2019, 34(4): 39-42.
Liu Yan, Ren Hangyong, Zhang Jian.Application of simulated annealing algorithm in aircraft cruise traveler problem[J]. Electro-Optic Technology Application, 2019, 34(4): 39-42.
[28] 王震, 陆金桂. 改进的ACO-BP 神经网络在锂离子电池 SOC 估算中的应用[J]. 轻工学报, 2019, 34(4): 81-86.
Wang Zhen, Liu Jingui.Application of improved ACO-BP neural network in estimation of SOC of lithium ion battery[J]. Journal of Light Industry, 2019, 34(4): 81-86.
[29] 王桥, 魏孟, 叶敏, 等. 基于灰狼算法优化极限学习机的锂离子电池 SOC 估计[J]. 储能科学与技术, 2021, 10(2): 744-751.
Wang Qiao, Wei Meng, Ye Min, et al.SOC estimation of lithium-ion battery based on gray wolf optimizer optimized extreme learning machine[J]. Energy Storage Science and Technology, 2021, 10(2): 744-751.
[30] Tian Dongdong, Li Liwei, Yang Yuxin, et al.SOC of estimation of lithium battery based on IBA-PF[J]. Energy Storage Science and Technology, 2020, 9(5): 1585-1592.
[31] Saha B, Goebel K.Battery data set[Z]. NASA Ames Prognostics Data Repository, (http://ti.arc.nasa.gov/ project/prognostic-data repository), NASA Ames Research Center, Moffett Field, CA. 2007.
[32] Edd.ahech A, Briat O, Vinassa J M. Determination of lithium-ion battery state-of-health based on constant-voltage charge phase[J]. Journal of Power Sources, 2014, 258: 218-227.
[33] 田民, 刘思峰, 卜志坤. 灰色关联度算法模型的研究综述[J]. 统计与决策, 2008(1): 24-27.
Tian Min, Liu Sifeng, Bu Zhikun.Research review of grey relational degree algorithm model[J]. Statistics and Decision, 2008(1): 24-27.
[34] 阎述学, 李一平, 封锡盛. 一种基于高斯过程回归的AUV自适应采样方法[J]. 机器人, 2019, 41(2): 232-241.
Yan Shuxue, Li Yiping, Feng Xisheng.An AUV adaptive sampling method based on gaussian process regression[J]. Robot, 2019, 41(2): 232-241.
[35] 戴彧虹, 袁亚湘. 非线性共轭梯度法[M]. 上海: 上海科技出版社, 1999.
[36] 胡局新, 张功杰. 基于K折交叉验证的选择性集成分类算法[J]. 科技通报, 2013, 29(12): 115-117.
Hu Juxin, Zhang Gongjie.K-fold cross-validation based selected ensemble classification algorithm[J]. Bulletin of Science and Technology, 2013, 29(12): 115-117.