State of health estimation for echelon-used batteries based on BP neural network

doi:10.3969/j.issn.1674-1951.2021.07.007

Abstract

Abstract:

In the context of rapid developing energy storage industry and the gradually increasing decommissioned power batteries for vehicles, the echelon-used batteries and their application scenarios in energy storage are introduced, and the necessity of their state of health is expounded. Influence factors for battery state of health are discussed. A three-layer BP neural network is constructed by taking battery DC resistance, discharge rate and surface temperature as inputs. Experiment results show that trained by 30 echelon-used batteries, the network can effectively converge and keep the health state estimation errors of echelon-used batteries within 3%. Estimating battery state of health of batteries with BP neural network is feasibility and of great significance in sorting as well as energy storage for echelon-used batteries.

Key words: echelon-used battery, state of health, BP neural network, battery DC resistance, discharge rate, decommissioned power battery, lithium battery, peak regulation and frequency modulation, energy storage

CLC Number:

TK02：TM911

LI Yongqi, LEI Qikai, WANG Hao, HUA Sicong. State of health estimation for echelon-used batteries based on BP neural network[J]. Huadian Technology, 2021, 43(7): 42-46.

Figures/Tables 6

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References 16

[1]	李建林, 修晓青, 刘道坦, 等. 计及政策激励的退役动力电池储能系统梯次应用研究[J]. 高电压技术, 2015, 41(8):2562-2568.
	LI Jianlin, XIU Xiaoqing, LIU Daotan, et al. Research on second use of retired electric vehicle battery energy storage system considering policy incentive[J]. High Voltage Engineering, 2015, 41(8):2562-2568.
[2]	王兴兴, 孙建桥, 陈明. 储能火电联合调频系统设计与研究[J]. 华电技术, 2020, 42(4):72-76.
	WANG Xingxing, SUN Jianqiao, CHEN Ming. Design and research on energy storage and thermal power combined frequency modulation systems[J]. Huadian Technology, 2020, 42(4):72-76.
[3]	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, LYU 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.
[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]	吴盛军, 袁晓冬, 徐青山, 等. 锂电池健康状态评估综述[J]. 电源技术, 2017, 41(12):1788-1791.
	WU Shengjun, YUAN Xiaodong, XU Qingshan, et al. Review on lithium-ion battery health state assessment[J]. Chinese Journal of Power Sources, 2017, 41(12):1788-1791.
[6]	黄业伟. 电动汽车锂离子动力电池健康状态估计方法研究[D]. 合肥:合肥工业大学, 2014.
[7]	朱国才, 何向明. 废旧锂离子动力电池的拆解及梯次利用[J]. 新材料产业, 2017(9):43-46.
[8]	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(S1):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.
[9]	徐晶. 梯次利用锂离子电池容量和内阻变化特性研究[D]. 北京:北京交通大学, 2014.
[10]	DUBARRY M, LIAW B Y. Identify capacity fading mechanism in a commercial LiFePO₄ cell[J]. Journal of Power Sources, 2009, 194(1):541-549. doi: 10.1016/j.jpowsour.2009.05.036
[11]	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.
[12]	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. doi: 10.1109/5.726791
[13]	XI Z M, DAHMARDEH M, XIA B, et al. Learning of battery model bias for effective state of charge estimation of lithium-ion batteries[J]. IEEE transactions on vehicular technology, 2019, 68(9):8613-8628. doi: 10.1109/TVT.25
[14]	XU X L, MI J F, FAN M S, et al. Study on the performance evaluation and echelon utilization of retired LiFePO₄ power battery for smart grid[J]. Journal of Cleaner Production, 2019(213):1080-1086.
[15]	ZHAO R X, KOLLMEYER P, LORENZ R D, et al. A compact methodology via a recurrent neural network for accurate equivalent circuit type modeling of lithium-ion batteries[J]. IEEE Transactions on Industry Applications, 2019, 55(2):1922-1931. doi: 10.1109/TIA.2018.2874588
[16]	SARASKETA-ZABALA E, GANDIAGA I, RODRIGUEZ-MARTINEZ L M, et al. Calendar ageing analysis of a LiFePO₄/graphite cell with dynamic model validations:Towards realistic lifetime predictions[J]. Journal of Power Sources, 2014(272):45-47.