[1]PENG Ziran,WANG Shunhao,XIAO Shenping.SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer[J].CAAI Transactions on Intelligent Systems,2025,20(4):969-983.[doi:10.11992/tis.202408010]
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SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 969-983 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer
Author(s):
PENG Ziran1; 2;  WANG Shunhao1; 2;  XIAO Shenping1; 2
1. Department of Electrical and Information Engineering, Hu’nan University of Technology, Zhuzhou 412007, China;
2. Hu’nan Electrical Transmission and Intelligent Equipment Key Laboratory, Hu’nan University of Technology, Zhuzhou 412007, China
Keywords:
electric vehicle; power battery; state of health; state of charge; stacked denoising autoencoder; data cleaning; dynamical channel pruning; enhanced informer
CLC:
TP274; U469.72; TM912
DOI:
10.11992/tis.202408010
Abstract:
To address the problems of low computing efficiency and estimation accuracy of existing electric vehicle power battery state of charge (SOC) and state of health (SOH) estimation methods, a model is proposed to estimate SOC and SOH. First, a stacked denoising automatic encoder (SDAE) is used to clean the anomalies and vacancies in the voltage, current, and temperature data to reduce the effect on the estimation accuracy. Second, the dynamic channel pruning (DCP) technique is introduced to address the sparsity of the Informer model to improve the performance and stability of the model. Finally, the cleaned data are input into the DCPInformer network model to estimate SOC and SOH. Experiments reveal that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed SDAE-DCPInformer model reach 0.25% and 0.38% for estimating SOC, respectively, and the MAE and RMSE for estimating SOH reach 0.51% and 0.64%, respectively. Compared with traditional models such as the Transformer, the proposed model predicts faster, and the estimation accuracy is improved with better stability and generalization.
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