Collaborative Estimation of Lithium Battery State of Charge Based on the BiLSTM-AUKF Fusion Model

To address the issue of decreased accuracy in lithium battery state of charge (SOC) estimation caused by parameter mismatches, modeling error accumulation, and sensitivity to noise, this paper proposes a collaborative estimation method. The proposed method combines a Bayesian optimization (BO)-tuned dual-input bidirectional long short-term memory network (BiLSTM) with an adaptive unscented Kalman filter (AUKF) based on the Sage–Husa adaptive strategy. First, a dual-input BiLSTM network is constructed using a multi-layer cascaded BiLSTM to extract time-dependent features. This network fuses both temporal and static features to perform an initial SOC prediction, while BO is employed to adaptively optimize the network’s hyperparameters. Second, the BiLSTM prediction outputs and the physical model are incorporated into the AUKF framework to achieve real-time iterative SOC estimation. Multi-scenario experiments conducted on the University of Maryland CALCE battery dataset demonstrated that the proposed method achieved a mean absolute error (MAE) below 0.6% and a root mean square error (RMSE) less than 0.8%. This method effectively enhances the robustness and noise immunity of SOC estimation in dynamic scenarios, providing a high-precision state estimation solution for battery management systems.