State-of-charge estimation with adaptive extended Kalman filter and extended stochastic gradient algorithm for lithium-ion batteries

Abstract

Online state-of-charge (SOC) estimation is a critical element for battery management systems and it requires lower computing cost and acceptable range of accuracy. This paper proposes a new model-based SOC estimation method for lithium-ion batteries. By utilizing the state estimation to identify the model parameters and then re-estimate the state by using the identified parameters, the two steps of parameter identification and state estimation are integrated into one closed-loop algorithm and they are implemented by using extended stochastic gradient (ESG) algorithm and adaptive extended Kalman filter (AEKF), respectively. In this method, it is unnecessary to calculate each circuit parameter of the model separately resulting in simper structure and lower computing cost. Experimental results indicate that the proposed SOC estimation algorithm has good performance in terms of estimation accuracy and robustness under different test conditions. It is therefore more suitable for online SOC estimation of lithium-ion batteries.