State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

Abstract

Health monitoring is an essential task for lithium battery systems. Recently, with the development of data-driven methods, deep learning has been successfully deployed for state-of-health (SOH) estimation. However, existing models trained using raw samples directly usually contain noise due to sensor errors. To enhance the performance of SOH prediction, short-term segments are extracted for SOH estimation based on reasonable SOC ranges. To address the measuring error that exists in the voltage and temperature samples, the reconstructed feature series (RFSs) is designed to restrain the signals’ noise. Then, a CNN-GRU network with attention mechanism is proposed to achieve SOH estimation based on short-term RFSs’ samples. To further enhance accuracy, a parallel structure is designed to fuse the feature information from both streams, raw samples, and RFSs in a reasonable manner. The performance of our proposed method is validated over a wide range of experiments on the Oxford battery degradation dataset, where the RMSE and MAE averaged 0.582% and 0.524%, respectively, demonstrating its forward estimation performance.