State-of-Charge and State-of-Health Lithium-Ion Batteries' Diagnosis According to Surface Temperature Variation

This paper presents a hybrid state-of-charge (SOC) and state-of-health (SOH) estimation technique for lithium-ion batteries according to surface temperature variation (STV). The hybrid approach uses an adaptive observer to estimate the SOH while an extended Kalman filter (EKF) is used to predict the SOC. Unlike other estimation methods, the closed-loop estimation strategy takes into account the STV and its stability is guaranteed by Lyapunov direct method. In order to validate the proposed method, experiments have been carried out under different operating temperature conditions and various discharge currents. Results highlight the effectiveness of the approach in estimating SOC and SOH for different aging conditions.