基于卷积神经网络的锂离子电池SOH估算

doi:10.19595/j.cnki.1000-6753.tces.191617

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Abstract State of health (SOH) of Lithium-ion battery describes the current aging degree of the battery. The difficulty of its estimation lies in the lack of a clear definition, the inability to directly measure, and the difficulty in determining the appropriate number and high correlation of the estimation input. In order to overcome the above problems, this article defines SOH from the perspective of capacity, and takes the voltage, current, and temperature curves of the lithium-ion battery constant current-constant voltage charging process as input, and proposes to use a one-dimensional deep convolutional neural network (CNN) to achieve lithium-ion battery capacity estimation to obtain SOH. Experimental results on NASA's lithium-ion battery random use data set and Oxford battery aging data set show that this method can achieve accurate SOH estimation, and has the advantages of fewer network parameters and less memory. In addition, the influences of network input, model structures and data augmentation on the proposed SOH estimation method are discussed through experiments.

Key words： Lithium-ion battery state of health (SOH) convolution neural network deep learning

Received: 26 November 2019

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TM912

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	Articles by authors
	Li Chaoran
	Xiao Fei
	Fan Yaxiang
	Yang Guorun
	Tang Xin

Cite this article:

Li Chaoran,Xiao Fei,Fan Yaxiang等. An Approach to Lithium-Ion Battery SOH Estimation Based on Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191617 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I19/4106

[1] 魏业文, 李应智, 曹斌, 等. 含Buck电路的锂电池低功耗电量均衡技术研究[J]. 电工技术学报, 2018, 33(11): 2575-2583.
Wei Yewen, Li Yingzhi, Cao Bin, et al.Research on power equalization of lithium-ion batteries with less-loss Buck chopper[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2575-2583.
[2] 曾辉, 孙峰, 邵宝珠, 等. 澳大利亚100MW储能运行分析及对中国的启示[J]. 电力系统自动化, 2019, 43(8): 86-92.
Zeng Hui, Sun Feng, Shao Baozhu, et al.Analysis of Australian 100MW energy storage operation and its enlightenment to China[J]. Automation of Electric Power Systems, 2019, 43(8): 86-92.
[3] Zhu Z Q, Cai S.Hybrid excited permanent magnet machines for electric and hybrid electric vehicles[J]. CES Transactions on Electrical Machines and Systems, 2019, 3(3): 233-247.
[4] 中关村储能产业技术联盟. 储能产业研究白皮书2019[R]. 2019.
[5] 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.
[6] 郑志坤, 赵光金, 金阳, 等. 基于库仑效率的退役锂离子动力电池储能梯次利用筛选[J]. 电工技术学报, 2019, 34(增刊1): 388-395.
Zheng Zhikun, Zhao Guangjin, Jin Yang, et al.The reutilization screening of retired electric vehicle lithium-ion battery based on coulombic efficiency[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 388-395.
[7] 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.
[8] 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.
[9] Pan Haihong, Lü Zhiqiang, Wang Haiyan, et al.Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine[J]. Energy, 2018, 160: 466-477.
[10] Wang Zengkai, Zeng Shenpin, Guo Jianbin, et al.State of health estimation of lithium-ion batteries based on the constant voltage charging curve[J]. Energy, 2019, 167: 661-669.
[11] Ng K S, Moo C S, Chen Y P, 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.
[12] Love C T, Virji M B V, Rocheleau R E, et al. State-of-health monitoring of 18650 4S packs with a single-point impedance diagnostic[J]. Journal of Power Sources, 2014, 266: 512-519.
[13] Galeotti M, Cinà L, Giammanco C, et al.Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy[J]. Energy, 2015, 89: 678-686.
[14] Lin Cheng, Zhang Xiaohua. Modeling and simulation research on lithium-ion battery in electric vehicles based on genetic algorithm[J]. Applied Mechanics and Materials, 2014, 494-495: 246-249.
[15] Doyle M, Newman J.Modeling the performance of rechargeable lithium-based cells: design correlations for limiting cases[J]. Journal of Power Sources, 1995, 54(1): 46-51.
[16] Doyle M, Newman J.Analysis of capacity-rate data for lithium batteries using simplified models of the discharge process[J]. Journal of Applied Electrochemistry, 1997, 27(7): 846-856.
[17] Prada E, Di Domenico D, Creff Y, et al.A simplified electrochemical and thermal aging model of LiFePO₄-Graphite Li-ion batteries: Power and capacity fade simulations[J]. Journal of the Electrochemical Society, 2013, 160(4): A616-A628.
[18] Deshpande R, Verbrugge M, Cheng Y T, et al.Battery cycle life prediction with coupled chemical degradation and fatigue mechanics[J]. Journal of the Electrochemical Society, 2012, 159(10): A1730-A1738.
[19] Ning G, Popov B N.Cycle life modeling of lithium-ion batteries[J]. Journal of The Electrochemical Society, 2004, 151(10): A1584.
[20] Zou C, Manzie C, Nesic D, et al.Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery[J]. Journal of Power Sources, 2016, 335: 121-130.
[21] Li J, Adewuyi K, Lotfi N, et al.A single particle model with chemical/mechanical degradation physics for lithium-ion battery state of health (SOH) estimation[J]. Applied Energy, 2018, 212: 1178-1190.
[22] Xiong Rui, Tian Jinpeng, Mu Hao, et al.A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries[J]. Applied Energy, 2017, 207: 372-383.
[23] Chen M, Rincon-Mora G A. Accurate electrical battery model capable of predicting runtime and I-V performance[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 504-511.
[24] Millner A.Modeling lithium ion battery degradation in electric vehicles[C]//IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES), Waltham, USA, 2010: 349-356.
[25] Guha A, Patra A.State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification, 2017: 135-146.
[26] Tang Xiaopeng, Zou Changfu, Yao Ke, et al.A fast estimation algorithm for lithium-ion battery state of health[J]. Journal of Power Sources, 2018, 396: 453-458.
[27] Andre D, Appel C, Soczka-Guth T, et al.Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries[J]. Journal of Power Sources, 2013, 224: 20-27.
[28] Wang D, Miao Q, Pecht M.Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model[J]. Journal of Power Sources, 2013, 239: 253-264.
[29] Salkind A J, Fennie C, Singh P, et al.Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology[J]. Journal of Power Sources, 1999, 80(1-2): 293-300.
[30] Richardson R R, Birkl C R, Osborne M A, et al.Gaussian process regression for in-situ capacity estimation of lithium-ion batteries[J]. IEEE Transactions on Industrial Informatics, 2018, 15(1): 127-138.
[31] Richardson R R, Osborne M A, Howey D A.Gaussian process regression for forecasting battery state of health[J]. Journal of Power Sources, 2017, 357: 209-219.
[32] Richardson R R, Osborne M A, Howey D A.Battery health prediction under generalized conditions using a Gaussian process transition model[J]. Journal of Energy Storage, 2019, 23: 320-328.
[33] Yang Duo, Zhang Xu, Pan Rui, et al.A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384: 387-395.
[34] Lin H T, Liang T J, Chen S M.Estimation of battery state of health using probabilistic neural network[J]. IEEE Transactions on Industrial Informatics, 2013, 9(2): 679-685.
[35] Lin F J, Huang M S, Yeh P Y, et al.DSP-based probabilistic fuzzy neural network control for li-ion battery charger[J]. IEEE Transactions on Power Electronics, 2012, 27(8): 3782-3794.
[36] Chaoui H, Ibe-Ekeocha C C. State of charge and state of health estimation for lithium batteries using recurrent neural networks[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10): 8773-8783.
[37] Lecun Y, Bengio Y, Hinton G.Deep learning[J]. Nature, 2015, 521: 436-444.
[38] Lecun Y, Bottou L, Bengio Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[39] Krizhevsky A, Sutskever I, Hinton G.ImageNet classification with deep convolutional neural networks[C]//NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012: 1097-1105.
[40] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014, arXiv: 1409.1556v6.
[41] Szegedy C, Liu W, Jia Y, et al.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1-9.
[42] He Kaimi g, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//Computer Vision and Pattern Recognition, 2016: 770-778.
[43] Rawat W, Wang Z.Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Computation, 2017, 29(9): 2352-2449.
[44] Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J].2014, arXiv: 1207.0580.
[45] Bole B, Kulkarni C S, Daigle M.Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use[C]//Conference of the Prognostics & Health Management Society, Fort Worth, USA, 2014: 1-9.
[46] Birkl C R.Diagnosis and prognosis of degradation in lithium-ion batteries[D]. Oxford: University of Oxford, 2017.
[47] André M. The ARTEMIS European driving cycles for measuring car pollutant emissions[J]. Science of the Total Environment, 2004, 334-335: 73-84.
[48] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
[49] Wilson A G, Adams R P.Gaussian process covariance kernels for pattern discovery and extrapolation[C]// Proceedings of the 30th International Conference on International Conference on Machine Learning, Atlanta, 2013: 1067-1075.