Adaptive SOC-OCV mapping-based joint estimation of SOC and SOH in aging lithium-ion batteries using extended Kalman filtering

Accurate estimation of the state of health (SOH) and state of charge (SOC) in aging lithium-ion batteries is essential for ensuring their reliability and longevity in practical applications. However, battery aging introduces considerable challenges, particularly through alterations in the state of charge-open-circuit voltage (SOC-OCV) curve, which can degrade estimation accuracy. This study addresses these challenges by first analyzing the effect of the SOC-OCV curve on SOC estimation accuracy. Building upon the forgetting factor regression least-squares extended Kalman filtering strategy, a novel approach is proposed to combines the Coulomb counting method with the extended Kalman filter for developing an SOH observer. This observer adaptively updates the SOC-OCV curve and the available capacity, compensating for SOH degradation. The updated information is then integrated into the SOC estimation process to enhance accuracy. The proposed method is computationally efficient and suitable for on-vehicle applications, because it minimizes SOC estimation errors, provides a rapid response, and maintains low computational demands. Simulation results from battery discharge tests confirm the effectiveness of the method in improving SOC estimation for aging batteries.