摘要
随着锂离子电池的广泛应用,其寿命预测与健康管理已成为当今的热点问题。锂离子电池寿命预测对于电池管理系统的稳定运行有着重要意义。采用最小二乘支持向量机(LSSVM)模型对锂离子电池剩余寿命进行预测,并采用鸟群优化算法(BSA)对LSSVM参数进行寻优。为提高BSA的全局搜索能力,对BSA进行改进,并提出改进鸟群算法(IBSA)。最后,采用IBSA优化LSSVM模型,建立了IBSA-LSSVM预测模型并对锂离子电池剩余寿命进行预测。测试结果表明,IBSA-LSSVM模型对锂离子电池剩余寿命有良好的预测效果和预测稳定性。
With the wide application of lithium-ion batteries, life prediction and health management have become a hot issue nowadays. Life prediction of lithium batteries is of great signifcance to the stable operation of battery management system. Least squares support vector machine(LSSVM) model is used to predict the remaining life of lithium ion batteries, and bird swarm optimization algorithm(BSA) is used to optimize the parameters of LSSVM. In order to improve the global search ability of BSA, the BSA algorithm is improved and an improved bird swarm algorithm(IBSA) is proposed. Finally, the least squares support vector machine(LSSVM) is optimized by using IBSA, and the IBSA-LSSVM prediction model is established to predict the life of lithium-ion batteries. The test results show that IBSA-LSSVM model has good prediction effect and stability.
作者
王雪莹
张君婷
赵全明
WANG Xueying;ZHANG Junting;ZHAO Quanming(School of Electronic Information Engineering,Hebei University of Technology,Tianjin 300401,China;School of Electrical Engineering,Hebei University of Technology,Tianjin 300130,China)
出处
《电气应用》
2020年第7期74-78,共5页
Electrotechnical Application
关键词
锂离子电池
剩余寿命预测
改进的鸟群算法
最小二乘支持向量机
lithium-ion battery
prediction of remaining life of Li-ion battery
improved bird swarm optimization algorithm
least squares support vector machine
作者简介
王雪莹(1987-),女,助教,主要研究方向为智能信息处理。
