Optimization of State-of-Charge Estimation Algorithm Based on Coefficient of Variation in Cell-to-Cell Imbalance of Reusable Battery Packs

As the demand for electric vehicles increases, the recycling and reuse of used batteries have emerged as significant challenges. Performance degradation and imbalance between battery cells can hinder the overall performance and lifespan of the battery pack. To address this issue, this study proposes a state of balance (SOB) index to quantify cell imbalance using the coefficient of variation, combined with state of health (SOH) to develop a new state of charge (SOC) estimation algorithm. This algorithm is based on the extended Kalman filter (EKF) and improves SOC estimation accuracy by simultaneously reflecting the degradatio state and imbalance of the battery. Based o the comparisons of four algorithms using MATLAB/Simulink, the proposed algorithm, which incorporates SOH and SOB, achieved a maximum estimation error rate of 0.94%, significantly enhancing performance compared with the conventional EKF algorithm. The study’s algorithm is expected to contribute to improving the performance and safety of used batteries, as well as enhancing the accuracy and reliability of reuse battery management systems.