A Joint Forgetting Factor-Based Adaptive Extended Kalman Filtering Approach to Predict the State-of-Charge and Model Parameter of Lithium-Ion Battery

The accuracy of model-based State of Charge (SOC) estimators often degrades due to parametric uncertainty, measurement errors, and variations in operating temperature. Many Kalman filter-driven SOC estimators in the literature overlook these uncertainties, leading to imprecise SOC estimation. To address these challenges, this study proposes a joint forgetting factor-based adaptive extended Kalman filter (JFFAEKF). The JFFAEKF approach evaluates uncertainties in the battery model and incorporates them into the SOC estimation process under dynamic operating conditions. By augmenting both the SOC and battery model parameters into a single state vector, the estimator concurrently updates these variables. An adaptive correction mechanism for process and measurement noise covariance matrices is introduced, leveraging the innovation and residual errors of estimated terminal voltage. These covariance updates enable the computation of an appropriate filter gain to mitigate the adverse effects of model and measurement uncertainties. Additionally, a forgetting factor is integrated into the design to enhance computational efficiency and convergence rate. The practical applicability of the proposed JFFAEKF is validated using real-world current profiles from the LA92, UDDS, and US06 drive cycles at various operating temperatures. The accuracy of the SOC estimation is demonstrated by comparing the root mean square error ( E RMS ) and maximum absolute error ( Max AE ) with other Kalman filter-based estimators. Furthermore, the estimator's robustness is tested under adverse conditions, including offset current, sensor bias voltage, and parametric uncertainties in the battery model and state estimator. Results from diverse dynamic operating conditions confirm the superior performance of the JFFAEKF in SOC estimation compared to existing methods.