海南热带海洋学院学报2024，Vol.31Issue(2)：59-68,10.DOI:10.13307/j.issn.2096-3122.2024.02.09
基于多尺度分解的LSTM-ARIMA锂电池寿命预测
LSTM-ARIMA Model with Multiscale Decomposition for Life Prediction of Lithium-ion Battery
摘要
Abstract
The prediction of remaining useful life(RUL)of lithium-ion batteries is an important research direction in battery technology.Through accurate prediction of RUL,batteries can be better managed and maintained to extend their lifespan.To achieve accurate RUL prediction of lithium-ion batteries,a model which combines variational mode decomposi-tion(VMD)with long short-term memory(LSTM)and autoregressive integrated moving average(ARIMA)was proposed.Firstly,VMD algorithm was used to decompose the capacity data from the NASA lithium-ion battery dataset into multiple high-frequency and low-frequency components in order to reduce the noise interference in the capacity data.Then,with re-gard to the characteristics of each component,LSTM and ARIMA were used to establish separate sub-models to predict the high-frequency and low-frequency components,respectively.Finally,the predicted values of each sub-model were com-bined and reconstructed to obtain the RUL result of the lithium-ion battery.Experimental results showed that the VMD-LSTM-ARIMA prediction model had better RUL prediction capability compared with other prediction models.Furthermore,generalization experiments on the CALCE lithium-ion battery dataset showed that the model was applicable to different battery RUL prediction tasks.关键词
锂电池/剩余寿命预测/变分模态分解/长短时记忆网络/自回归移动平均模型Key words
lithium-ion battery/remaining life prediction/variational mode decomposition/long short-term memory neural network/ARIMA分类
信息技术与安全科学引用本文复制引用
张意,汤文兵,张斌..基于多尺度分解的LSTM-ARIMA锂电池寿命预测[J].海南热带海洋学院学报,2024,31(2):59-68,10.基金项目
国家自然科学基金资助项目(22239003) （22239003）
