电子科技2026，Vol.39Issue(1)：18-24,7.DOI:10.16180/j.cnki.issn1007-7820.2026.01.003
基于IDBO-CNN-BiLSTM锂电池剩余使用寿命预测
Remaining Useful Life Prediction for Lithium-ion Battery with IDBO-CNN-BiLSTM Model
摘要
Abstract
The SOH(State of Health)and RUL(Remaining Useful Life)of batteries are important evaluation in-dicators for battery health management.In view of the problem that it is difficult to accurately predict the remaining use-ful life of lithium batteries due to the influence of many complex factors during their use,this study proposes a hybrid prediction model based on IDBO-CNN-BiLSTM(Improved Dung Beetle Optimizer-Convolutional Neural Networks-Bi-directional Long Short-Term Memory).By analyzing the state of the lithium battery during the charging process,nine HF(Health Factor)are extracted.The strongly correlated health factors are screened out through the Pearson cor-relation coefficient and used as the input of the model.The chaotic initialization Tent mapping is adopted to generate the initial positions of the dung beetles,and the sine-cosine strategy is used to optimize the positions of the stealing dung beetles.This solves the local convergence problem caused by the initialization of the DBO(Dung Beetle Optimi-zer)algorithm and optimizes the balance of the DBO algorithm,improving the stability of the prediction.Experiments are carried out based on the publicly available lithium battery aging dataset provided by NASA(National Aeronautics and Space Administration),and different models are used to predict the SOH of NASA's lithium batteries.The results show that the proposed method has a smaller error and has certain application value.关键词
锂离子电池/健康因子/卷积神经网络/双向长短期记忆神经网络/混合模型/健康状态/剩余使用寿命/蜣螂优化算法Key words
lithium-ion battery/health factor/convolutional neural network/long short-term memory/hybrid model/state of health/remaing useful life/dung beetle optimization algorithm分类
信息技术与安全科学引用本文复制引用
梁兆松,田恩刚,李磊..基于IDBO-CNN-BiLSTM锂电池剩余使用寿命预测[J].电子科技,2026,39(1):18-24,7.基金项目
国家自然科学基金(62173231)National Natural Science Foundation of China(62173231) （62173231）
