Robust lithium-ion battery state of health estimation based on recursive feature elimination-deep Bidirectional long short-term memory model using partial charging data

Abstract

Accurate perception of the state of health (SOH) of lithium-ion batteries is crucial for their safety and reliable operation. To meet this demand, a recursive feature elimination-deep bidirectional long short-term memory (RFE-DBiLSTM) model suitable for partial charging data is proposed to effectively estimate the SOH of lithium-ion batteries. In this study, the recursive feature elimination (RFE) method is used to screen multiple charging features for obtaining the key features that best represent the SOH under two scenarios with different charging segment lengths. Due to the robust noise-filtering capability and strong ability to capture complex and multi-level temporal dependencies, the deep bidirectional long short-term memory (DBiLSTM) model is used for time series data training, verification, and testing during aging. Experimental results show that compared with benchmark time series models such as long short-term memory (LSTM) and gated recurrent unit (GRU), the proposed method significantly reduces the estimated mean absolute error (MAE) and root mean square error (RMSE) on diverse batteries in the above scenarios. In the scenario for missing partial constant current (CC) charging data, the MAE and RMSE of B0005 cell are 0.0062 and 0.0094, the MAE and RMSE of B0006 cell are 0.0294 and 0.0314, the MAE and RMSE of CS2_36 cell are 0.0510 and 0.0601, the MAE and RMSE of B0029 cell are 0.0057 and 0.0072, and the MAE and RMSE of B0030 cell are 0.0088 and 0.0102. This research innovatively combines the RFE method with the DBiLSTM model to improve the accuracy and robustness of SOH estimation.