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

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1290 - 1290

Published: March 25, 2025

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

Language: Английский

Single-port and multi-port self-reconfigurable battery topologies for dynamic cell balancing

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 119, P. 116402 - 116402

Published: March 28, 2025

Language: Английский