Co-estimation of maximum available capacity and state-of-charge for lithium-ion batteries in multi-operating mode with temperature and degradation state adaptivity

Abstract

Accurate state of charge (SOC) estimation is critical for battery management systems (BMS). A new SOC estimation framework is developed, containing four modules: battery modeling, parameter identification, SOC estimation, and capacity estimation. It mainly makes three improvements to improve the adaptability. In the parameter identification module, an improved forgetting factor parameters identification method is proposed to obtain model parameters with high precision. For SOC estimation, considering the impact of noise on SOC estimation, an adaptive square root unscented Kalman filter is proposed, which can solve the divergence issue caused by inappropriate noise matrix iteration. Moreover, a weighted recursive least squares algorithm is developed to estimate the maximum available capacity and feed the result back into the SOC estimation module to improve the estimation accuracy. The experimental results show that the proposed framework can obtain high-precision SOC estimation results under different temperatures and aging conditions.