Accurate estimation of state of charge and state of health of electric vehicle power batteries based on dynamic channel pruning informer-stacked denoising autoencoder model - Peng, Ziran; Wang, Shunhao; Zhang, Shengchun; Meng, Shuangwu (Electrical engineering, 2026)
Accurate estimation of state of charge (SOC) and state of health (SOH) of electric vehicle (EV) power batteries is critical for users to plan their trips and improve EV safety. The informer model, an improved variant of the transformer, excels in long-sequence time-series forecasting tasks; however, the number of heads in its multihead probsparse attention and multihead self-attention usually needs to be set manually, and redundant heads not only lead to overparameterization but also reduce estimation accuracy. This paper introduces the dynamic channel pruning (DCP) technique to highlight dominant attention features, which enhance the performance and stability of the informer model. Many estimation methods overlook the necessity of data cleaning; therefore, this paper proposes stacked denoising autoencoder (SDAE) to clean voltage, current, and temperature data to provide higher quality data for the informer model. Finally, the cleaned data are input into the dynamic channel pruning informer (DCPInformer) model to estimate SOC and SOH. Experimental validation is conducted using battery datasets from the University of Oxford and Beijing Institute of Technology. Results indicate that the mean absolute error (MAE) and root mean square error (RMSE) of the DCPInformer-SDAE model reach 0.25% and 0.38%, respectively, for estimating SOC, and 0.51% and 0.64%, respectively, for estimating SOH. In the inference stage, DCPInformer-SDAE exhibits higher efficiency; compared with traditional transformer, long short-term memory, gated recurrent unit, and extreme learning machine models, it achieves lower inference latency and higher throughput, while also improving accuracy, stability, and generalizability in SOC and SOH estimation.