储能科学与技术2025，Vol.14Issue(2)：659-670,12.DOI:10.19799/j.cnki.2095-4239.2024.0732
变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法
Prediction method for remaining service life of lithium batteries using SSA-LSTM combination under variable mode decomposition
摘要
Abstract
The widespread application of lithium-ion batteries in electric vehicles,renewable energy,and other fields necessitates accurate prediction of their remaining useful life(RUL).Such predictions enable real-time monitoring of the battery's internal performance degradation,thereby reducing the risk associated with battery usage.We propose a combined prediction algorithm utilizing variational mode decomposition,sparrow search algorithm(SSA),and long short-term memory(LSTM)for predicting the remaining life of lithium-ion batteries.Initially,indirect health indicators for predicting RUL were extracted from the current,voltage,and temperature curves of the batteries.These indicators included isobaric charging time,isobaric charging energy,peak discharge temperature,and constant-current charging time.Subsequently,the VMD method was employed to decompose the capacity,aiming to avoid local fluctuations in capacity recovery and interference from test noise that could affect RUL prediction results.To address the susceptibility of traditional LSTM model hyperparameter settings toward experience and randomness,an SSA was proposed to optimize the parameters of the LSTM model,thereby enhancing the model's predictive capabilities.Ultimately,by utilizing NASA and CALCE datasets,a comparison was conducted between the proposed model and other models.Experimental results demonstrate that the proposed method achieves high predictive performance,with the root mean square error for RUL prediction of lithium-ion batteries consistently maintained within 2%.关键词
锂离子电池/剩余使用寿命/变模态分解/麻雀优化算法/长短期记忆网络Key words
lithium-ion batteries/remaining useful life/variational mode decomposition(VMD)/sparrow search algorithm(SSA)/long short-term memory(LSTM)分类
动力与电气工程引用本文复制引用
李嘉波,王志璇,田迪,孙中麟..变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法[J].储能科学与技术,2025,14(2):659-670,12.基金项目
陕西省教育厅科研计划项目(23JK0599),陕西省自然科学基础研究计划资助项目(2023-JC-QN-0658),高速公路筑养装备与技术教育部工程研究中心(300102253513),"咸阳市二〇二三年重点研发计划"项目动力电池过充电故障诊断研究(L2023-ZDYF-0YCX-032),陕西省教育厅科研计划项目(23JK0599). （23JK0599）
