重庆理工大学学报2024，Vol.38Issue(1)：308-318,11.DOI:10.3969/j.issn.1674-8425(z).2024.01.034
基于SCSSA-CNN-BiLSTM的行驶工况下锂电池寿命预测
Lithium-ion battery life prediction under driving conditions based on SCSSA-CNN-BiLSTM
摘要
Abstract
As lithium-ion batteries become increasingly popular,the battery life prediction is of crucial importance.Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is a critical part of their health management.In light of this,this paper proposes an algorithm SCSSA-CNN-BiLSTM,aming to perform RUL prediction for lithium-ion batteries used on electric vehicles.By combining convolutional neural networks(CNN),bidirectional long short-term memory(BiLSTM),and sine-cosine and cauchy mutation sparrow search algorithm(SCSSA),our algorithm forms a novel hybrid neural network that enhances the accuracy and stability of RUL predictions for lithium-ion batteries.CNN is employed for comprehensive extraction of deep features related to the state of health(SOH)of the batteries,while BiLSTM investigates these deep features bidirectionally and generates RUL predictions through dense layers.To validate the effectiveness of the proposed approach,battery data from NASA are compared with multiple commonly used models.Our research results show the hybrid model improves the coefficient of determination(R2)by 4％～23％and reduces the RUL absolute error to 1,demonstrating a higher prediction accuracy.The cyclic experiments are conducted later on vehicles under CLTC dynamic conditions,and predictions are made on battery life degradation.Our results reveal the SCSSA-CNN-BiLSTM model yields a root mean square error(RMSE)of 1.64 A·h and an R2value of 0.98,delivering strong predictive and generalization performances.关键词
锂离子电池/电动汽车/健康状态/剩余寿命预测/优化算法Key words
lithium-ion batteries/electric vehicles/health state/remaining life prediction/optimization algorithm分类
信息技术与安全科学引用本文复制引用
刘泽宇,彭泽源,韩爱国..基于SCSSA-CNN-BiLSTM的行驶工况下锂电池寿命预测[J].重庆理工大学学报,2024,38(1):308-318,11.基金项目
国家重点研发计划重点专项(2017YFB0103900) （2017YFB0103900）
