State-of-Charge Estimation of Lithium-Ion Batteries Based on Dual-Coefficient Tracking Improved Square-Root Unscented Kalman Filter

Accurate state of charge (SOC) estimation is helpful for battery management systems to extend batteries’ lifespan and ensure the safety batteries. However, due pseudo-positive definiteness covariance matrix noise statistics error accumulation, SOC lithium-ion batteries usually inaccurate or even divergent using Kalman filters, such as unscented filter (UKF) square-root (SRUKF). To resolve this problem, an method based on dual-coefficient tracking improved developed. The composed (ISRUKF) a tracker. avoid divergence with definiteness, ISRUKF QR decomposition presented. Moreover, tracker designed track correct battery, which can reduce caused by accumulation model ISRUKF. accuracy robustness developed are validated comparison UKF SRUKF. algorithm shows highest within 1.5%.