Residual BiLSTM-Based Error Correction Network for Li-Ion Battery SOC Estimation
TL;DR
Results demonstrate that integrating offline covariance optimization with BiLSTM-based residual learning improves estimation accuracy, robustness, and cross-battery generalization, providing a practical solution for lithium-ion battery SOC estimation in battery management systems.
Abstract
The efficient operation of lithium-ion battery management systems (BMSs) depends on accurate state-of-charge (SOC) estimation. However, the performance of conventional model-based SOC estimation methods may progressively worsen owing to parameter uncertainty and nonlinear battery dynamics. This study proposes a hybrid SOC estimation framework termed DO-EKFRes, comprising two sequential stages. In the first stage, the process and measurement-noise covariance matrices are optimized offline using a data-driven strategy. In the second stage, a Bidirectional Long Short-Term Memory (BiLSTM) residual learning network is employed to compensate for the remaining SOC estimation errors. The proposed framework was evaluated using two complementary validation protocols: a synthetic Monte Carlo experiment and a Leave-One-Battery-Out (LOBO) cross-validation framework based on the NASA Prognostics Center of Excellence (PCoE) lithium-ion battery dataset. In the synthetic validation, DO-EKFRes achieved an RMSE of 0.803%, corresponding to reductions of 48.83% and 26.84% relative to the EKF and DO-EKF, respectively. In the NASA LOBO evaluation, the proposed framework achieved a macro-averaged RMSE of 11.534%, corresponding to reductions of 49.25% and 9.68% relative to the EKF and DO-EKF, respectively. These results demonstrate that integrating offline covariance optimization with BiLSTM-based residual learning improves estimation accuracy, robustness, and cross-battery generalization, providing a practical solution for lithium-ion battery SOC estimation in battery management systems.