电源学报2024，Vol.22Issue(2)：197-204,8.DOI:10.13234/j.issn.2095-2805.2024.2.197
锂离子电池全生命周期剩余使用寿命预测
Remaining Useful Life Prediction for Full Life Cycle of Lithium-ion Battery
摘要
Abstract
To ensure the safety of new energy vehicles during the entire period of use,it is necessary to conduct health monitoring for the full life cycle of lithium-ion batteries.Aimed at the low learning rate due to the small capacity of training data set for the remaining useful life(RUL)prediction model based on neural network and the duplicate collinearity of the extreme learning machine(ELM)method,a method for augmenting the training data set is proposed.In addition,based on the improved ELM,an RUL prediction model for the full life cycle of lithium-ion battery is built.First,the early operation data of battery is extracted to formulate health factors,and the Akima interpolation method is used to augment the amount of training data.Then,the salp swarm algorithm is used to improve the ELM network,and the RUL prediction model for the full life cycle of lithium battery is established.Finally,the NASA battery data set is used to validate the model.Experimental results show that the proposed method for augmenting the training data capacity is effective,the capacity tracking capability of the RUL prediction model in full life cycle is strong,and the prediction error is small.关键词
锂离子电池/剩余使用寿命/Akima插补法/樽海鞘群优化算法/极限学习机Key words
Lithium-ion battery/remaining useful life(RUL)/Akima interpolation method/salp swarm algorithm/extreme learning machine(ELM)分类
信息技术与安全科学引用本文复制引用
赵沁峰,蔡艳平,王新军..锂离子电池全生命周期剩余使用寿命预测[J].电源学报,2024,22(2):197-204,8.
