State-of-Charge and State-of-Health Estimation Algorithms of Lithium-ion Batteries using SMO

The state of charge (SoC) and state of health (SoH) monitoring of power lithium-ion batteries is the basis of the battery management system (BMS). The estimation of SoC and SoH is a hot issue in battery management system research today. This paper refers to a third-order electrochemical model based on the average electrode of a lithium battery. Based on this model, the paper establishes a battery SoC and SoH estimation observer based on linear sliding mode and full-order terminal sliding mode algorithms. The paper uses the Luenberger algorithm and terminal sliding mode algorithms to estimate the SoC and SoH of lithium-ion batteries to improve their stability and convergence and uses Lyapunov's theory to verify the convergence of the observer. The accuracy and robustness of SoC and SoH estimations are verified by the dynamic stress test (DST) and federal urban driving conditions (FUDS) test data.