A Joint Estimation Method for the SOC and SOH of Lithium-Ion Batteries Based on AR-ECM and Data-Driven Model Fusion

Accurate estimations of State-of-Charge (SOC) and State-of-Health (SOH) are crucial for ensuring the safe and efficient operation of lithium-ion batteries in Battery Management Systems (BMSs). This paper proposes a novel joint estimation method integrating an Autoregressive Equivalent Circuit Model (AR-ECM) with a data-driven model to address the strong coupling between SOC and SOH. First, a multi-strategy improved Ivy algorithm (MSIVY) is utilized to optimize the hyperparameters of a Hybrid Kernel Extreme Learning Machine (HKELM). Key voltage interval features, including split voltage, differential capacity, and current–voltage product, are extracted and filtered using a sliding window approach to enhance SOH prediction accuracy. The estimated SOH is subsequently incorporated into the AR-ECM state-space equations, where an enhanced particle swarm optimization algorithm optimizes the model parameters. Finally, the Extended Kalman Filter (EKF) is applied to achieve collaborative SOC–SOH estimation. Experimental results demonstrate that the proposed method achieves SOH errors below 1% and SOC errors under 2% on public datasets, showcasing its robust generalization capability and real-time performance.