太赫兹科学与电子信息学报2025，Vol.23Issue(2)：182-187,6.DOI:10.11805/TKYDA2023204
基于深度学习的电动汽车锂电池寿命预测模型
Lithium battery life prediction model for electric vehicles based on hybrid deep learning
摘要
Abstract
In response to the current issue of low prediction performance in the remaining service life of electric vehicle lithium batteries,a hybrid deep learning model for predicting the remaining service life of electric vehicle lithium batteries is proposed.The model employs Empirical Mode Decomposition(EMD)to decompose battery data,forming high-frequency and low-frequency components of the battery capacity sequence.It utilizes Multilayer Long Short-Term Memory(MLSTM)and Elman neural networks to learn high-frequency and low-frequency battery capacity characteristics,extracting high-level representations of battery capacity.It combines high-frequency and low-frequency prediction results through stacking rules to achieve high-precision prediction of the battery's remaining service life.Experimental results show that the loss generated by the proposed hybrid deep learning detection model in the training set is approximately 7.87%.Compared with Support Vector Machine(SVM),Logistic Regression(LR),Recurrent Neural Network(RNN),and LSTM models,the proposed hybrid deep learning model demonstrates superior comprehensive performance indicators,with an Mean Absolute Percentage Error(MAPE)of only 1.438%.The experiments validate the effectiveness and practicality of the proposed model.关键词
电动汽车/锂电池/剩余使用寿命预测/特征提取/长短时记忆(LTSM)Key words
electric vehicles/lithium battery/prediction of remaining service life/feature extraction/Long Short Term Memory(LSTM)分类
信息技术与安全科学引用本文复制引用
范晋衡,刘琦颖,马力,刘力豪..基于深度学习的电动汽车锂电池寿命预测模型[J].太赫兹科学与电子信息学报,2025,23(2):182-187,6.基金项目
南方电网广州供电局科技资助项目(GZHKJXM20210055) （GZHKJXM20210055）
