CNN-GRU Battery SOC Estimation Method Fused with Attention Mechanism for Electric Multiple Units

Nickel-cadmium alkaline batteries are the core energy source for auxiliary devices of electric multiple units. Hence, an accurate estimation of their state of charge (SOC) is significantly important for prolonging battery life and improving energy efficiency. Given the limitations of existing SOC estimation methods when dealing with small-sample battery cycling data, this paper proposes an attention mechanism integrated convolutional neural network-gated recurrent unit (CNN-GRU) model for battery SOC estimation, and experimental validation is conducted on the LPH140A model nickel-cadmium batteries used in electric multiple units. The model employs a convolutional neural network (CNN) to extract short-term feature dependencies from long sequences within the battery cycling data. Then, an attention mechanism-integrated gated recurrent unit (GRU) is adopted to capture long spatial distance dependencies of the extracted feature data, resulting in more precise battery SOC estimation. To precisely estimate the SOC of small-sample battery cycling data, this paper transforms the continuous regression model into a classification problem, discretizes the battery SOC ranges, and converts the final prediction result into discrete SOC values. The experimental results show that compared with the CNN-GRU algorithm, the proposed approach improves three key metrics — root mean square error, mean absolute error, and mean relative error by 18.90%, 17.92% and 19.78%, respectively, demonstrating impressive prediction accuracy and stability.