Joint Estimation of State of Charge and Capacity throughout the Full Service Life Cycle for Li-Ion Batteries

Accurate SOC and capacity estimation is essential for the safe operation of lithium-ion batteries. However, model parameters drift due to temperature variations and aging. This study proposes a migration-model-based method for joint estimation of SOC and capacity over a wide range of temperatures and degradation levels. The WSPF algorithm identifies migration factors in real time and applies them to estimate SOC and capacity under nonlinear, non-Gaussian conditions. Validation under various test conditions demonstrates clear advantages. Compared to EKF, the migration-model-based algorithm reduces the maximum RMSE of SOC estimation to 0.55%. For capacity estimation, it achieves a maximum RMSE of 1.15%. The estimation accuracy remains high throughout temperature changes and aging, highlighting the robustness and applicability of the proposed method for real-world battery management systems.