State of Health Estimate Method for Lithium Batteries Based on Deep Transfer Learning

Battery State of Health (SoH) estimation is critical for battery management systems, and traditional machine learning approaches face challenges in terms of prediction accuracy and robustness due to limitations in data volume and complexity in real-world application scenarios. We propose a battery SoH estimation method based on transfer learning techniques to address this issue. First, we analyzed the battery charge and discharge data and selected three highly correlated features with SoH drop. Next, we built a gated recurrent neural network (GRU) model. We trained the GRU neural network using data from many different operating conditions as the source domain data. Finally, the trained GRU model in the source domain was migrated to the target domain, where only a small amount of data was available for fine-tuning. Experimental results show that the proposed migration learning model achieves an RMSE of 0.0097, an improvement of 59.4% compared to its benchmark model. It demonstrates the effectiveness and potential of migration learning in battery health state prediction and provides an efficient SoH estimation method for battery management systems.