基于多尺度数据融合的锂电池健康状态评估

doi:10.3969/j.issn.1671-7775.2023.05.005

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摘要针对锂电池健康状态评估问题，提出一种以人工神经网络为核心多尺度数据融合框架下的锂电池健康状态评估方法.选取内阻、充电电压样本熵和等压降放电时间作为典型特征参数，建立3层分布式人工神经网络对特征参数进行多尺度融合计算，以计算拟合输出结果作为评估健康状态的参考值，并通过美国国家航空航天局（national aeronautics and space administration，NASA）试验数据集进行验证.结果表明：提出的评估方法能够基于锂电池充放电测量数据和解算特征参数，利用多尺度数据融合框架迅速迭代收敛，完成锂电池健康状态评估拟合；该评估方法的计算结果与测试平台实测值相比，平均误差小于3%，评估性能衰退趋势与实际劣化趋势一致.

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	郝刚
	金涛

关键词 ：锂电池, 特征参数提取, 多尺度数据融合, 神经网络, 故障预测与健康管理

Abstract：A method of lithium battery health assessment was proposed based on the framework of multiscale data fusion with artificial neural network as the core. The internal constant resistance, the sample entropy of charging voltage and the isobaric discharge time were selected as typical characteristic parameters. The threelayer distributed artificial neural network was established for the multiscale data fusion, and the calculated fitting output was used as reference value for the health assessment. The proposed method was verified by the national aeronautics and space administration（NASA） experimental datasheet. The results show that the method based on the lithium battery typical characteristic parameters and the multiscale data fusion framework can rapidly iteratively converge to complete the evaluation and fitting of the health status of lithium battery. Comparing the calculation results of the proposed method with the test platform data, the average error is less than 3%, and the evaluation performance degradation trend is consistent with the actual deterioration trend.

Key words： lithium battery feature parameter extraction multiscale data fusion neural network prognostics and health management

基金资助:湖北省教育厅科研计划项目（B2021531）；武汉市知识创新专项曙光计划项目（2022010801020435）；武汉城市职业学院博士专项科研基金资助项目（2020whcvcB01）

作者简介: 郝刚（1988—），男，湖北英山人，博士研究生，讲师（haogang@whcp.edu.cn），主要从事装备可靠性评估与故障诊断的研究. 金涛（1966—），男，上海人，教授，博士生导师（通信作者，jandtonic@126.com），主要从事舰船安全性、损管智能化的研究.

引用本文:

郝刚，金涛. 基于多尺度数据融合的锂电池健康状态评估[J]. 江苏大学学报（自然科学版）, 2023, 44(5): 524-529. HAO Gang， JIN Tao. Lithium battery health evaluation based on multiscale data fusion[J]. Journal of Jiangsu University(Natural Science Eidtion) , 2023, 44(5): 524-529.

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http://zzs.ujs.edu.cn/xbzkb/CN/10.3969/j.issn.1671-7775.2023.05.005 或 http://zzs.ujs.edu.cn/xbzkb/CN/Y2023/V44/I5/524

［1］	郝刚,金涛,于利峰.基于SFMEA和三角模糊软集的电池系统风险评估[J].江苏大学学报(自然科学版),2019,40(6):629-635.
	HAO G, JIN T, YU L F. Risk evaluation in Liion battery system based on SFMEA and triangular fuzzy soft sets[J]. Journal of Jiangsu University（Natural Science Edition）, 2019,40(6):629-635.（in Chinese）
［2］	LI Y, LIU K L, FOLEY A M, et al. Datadriven health estimation and lifetime prediction of lithiumion batteries: a review[J]. Renewable and Sustainable Energy Reviews, DOI: 10.1016/j.rser.2019.109254.
［3］	陈猛,乌江,焦朝勇, 等.锂离子电池健康状态多因子在线估计方法[J].西安交通大学学报,2020,54(1):169-175.
	CHEN M, WU J, JIAO C Y, et al. Multifactor online estimation method for health status of lithiumion battery[J]. Journal of Xi′an Jiaotong University, 2020,54(1):169-175.（in Chinese）
［4］	MENG H X, LI Y F. A review on prognostics and health management(PHM) methods of lithiumion batteries[J]. Renewable and Sustainable Energy Reviews,DOI: 10.1016/j.rser.2019.109405.
［5］	ZHANG S S, XU K, JOW T R. The low temperature performance of Liion batteries[J].Journal of Power Sources, 2003，115:137-140.
［6］	RICHMAN J S，MOORMAN J R. Physiological timeseries analysis using approximate entropy and sample entropy[J]. American Journal of Physiology Heart and Circulatory Physiology,2000,278:2039-2049.
［7］	LAKE D E, RICHMAN J S, GRIFFIN M P, et al. Sample entropy analysis of neonatal heart rate variability[J]. American Journal of Physiology Regulatory, Integrative and Comparative Physiology,2002,283:789-797.
［8］	SAHA B，GOEBEL K. ″Battery Data Set″, NASA ames prognostics data repository[EB/OL]. [2021-08-19].http:∥ti.arc.nasa.gov/project/prognosticdatarepository.
［9］	WIDODO A, SHIM M C, CAESERENDRA W, et al. Intelligent prognostics for battery health monitoring based on sample entropy[J].Expert Systems with Applications,2011,38:11763-11769.
[10]	林娅. 基于数据驱动的锂电池剩余使用寿命预测方法研究[D].南京：南京航空航天大学,2018.
[11]	WULLIARD N, HE W, OSTERMAN M, et al. Comparative analysis of features for determining state of health in lithiumion batteries[J]. International Journal of Prognostics and Health Management,2013,4（1）:14-20.