Joint Estimation of Lithium Battery SOC-SOH Based on ASRCKF Algorithm

Abstract

To achieve accurate estimates of a lithium-ion battery’s charge level (SOC) and health condition (SOH), this paper tackles key issues in battery management by introducing a framework built around an adaptive square root cubature Kalman filter (ASRCKF) that tracks parameters in real time for better performance in changing environments. It uses ASRCKF to gauge SOC, while an extended Kalman filter (EKF) identifies battery traits online and monitors capacity loss, with a two-way feedback system that feeds SOH updates directly into the SOC calculations. Testing in high-speed driving, the New European Driving Cycle, and urban stop–start conditions showed the method keeps average SOC errors to 0.16% at most and peak errors to 0.33%, beating out standard EKF and SRCKF approaches in accuracy; SOH errors averaged 0.42%. Overall, this setup proves reliable for combined SOC-SOH tracking in diverse real-world situations, helping to ensure safer battery operations.