计算机应用研究2023，Vol.40Issue(12)：3551-3558,8.DOI:10.19734/j.issn.1001-3695.2023.04.0154
基于WOA优化概率分布参考点的锂电池故障诊断
Lithium batteries fault diagnosis based on WO A optimized probability distribution reference points
摘要
Abstract
Lithium-ion batteries are widely used in various fields due to their superior energy storage performance.However,with the increase of using time,the aging of lithium-ion batteries is prone to lead to different failure degrees,so online fault di-agnosis for lithium-ion batteries is crucial.To improve the accuracy and transparency of fault diagnosis,this paper proposed a fault diagnosis model based on continuous probability distribution evidential reasoning(ER)rule,and optimized the related parameters by optimization method.Firstly,this paper extracted characteristic indicators that could reflect batteries'state of health(SOH)from charging and discharging process,and used Spearman correlation coefficient to analyze the correlation be-tween characteristic indicators and the SOH to extract health indexes.Secondly,considering the uncertainty of battery fault in-formation,this paper proposed a fault diagnosis method based on continuous probability distribution reference points of eviden-tial reasoning(ER)rule,it used Gaussian distribution to describe the reference points,so as to achieve online fault diagnosis.Thirdly,it designed a whale optimization algorithm(WOA)with constraints to optimize evidence parameters to construct the GER-W fault diagnosis model,so that the accuracy of model fault diagnosis reached the best.Finally,it made fuzzy division of faults by analyzing SOH,and verified the effectiveness of the GER-W model by taking the NASA battery data set as an exam-ple.In addition,the model was extended to batteries'SOH estimation.The verification results show that GER-W model has higher accuracy and more transparent process than other fault diagnosis methods,and it also has a certain effect in SOH estima-tion.关键词
锂离子电池/故障诊断/证据推理规则/高斯分布/信息转换/鲸鱼优化算法Key words
lithium-ion batteries/fault diagnosis/evidential reasoning rule/Gaussian distribution/information conver-sion/whale optimization algorithm(WOA)分类
信息技术与安全科学引用本文复制引用
李康乐,张云逸,韩劲松,贺维..基于WOA优化概率分布参考点的锂电池故障诊断[J].计算机应用研究,2023,40(12):3551-3558,8.基金项目
中国博士后科学基金资助项目 （）
黑龙江省自然科学基金资助项目 （）
