Health Feature Parameters-Based TCN-GRU-AM for Lithium-Ion Battery SOH and RUL Estimation

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries reflect the current extent of aging of the battery from different perspectives, and accurate and efficient health assessment is crucial for ensuring their safe use. This study proposes a TCN-GRU-AM model based on health feature parameters for predicting SOH and RUL. This method extracts health features related to battery capacity degradation from raw data and selects key features as model inputs through Spearman correlation analysis. The TCN network uses dilated convolution to capture local temporal features during the charging and discharging processes; the GRU effectively captures the changing trends of short-term dynamics; the multi-head self-attention mechanism enhances the focus on key information. Experimental results show that for the local capacity regeneration phenomenon, this method demonstrates good prediction performance and can more accurately predict the battery capacity decay trend under different cycle starting points; compared to CNN-GRU, LSTM-AM, and GRU-AM models, it has lower RMSE and MAE values and better robustness. Additionally, in RUL prediction, the TCN-GRU-AM model maintains an absolute error of 0 to 2 cycles in all tests, significantly outperforming the comparison models and achieving high prediction accuracy.