State of charge estimation of Li-ion batteries considering uncertainties due to sensor measurement biases and temperature variations

Different uncertainties such as the sensor measurement errors and varying operating temperatures cause an error in battery model parameters, which, in turn, adversely affects the accuracy of the state of charge (SoC) estimation algorithm used in the battery management system. This paper proposes an SoC estimation method of the Li-ion battery that adopts a temperature-compensated battery model to deal with the varying model parameters that change with operating temperatures and a dual extended Kalman filter (DEKF) to estimate the SoC considering sensor measurement errors/biases. This study only focuses on the voltage and current sensor measurement error estimation in the DEKF framework, together with SoC estimation. The performance of the DEKF with biases elimination is compared with the DEKF without the elimination of biases. The efficacy of the proposed method is validated through extensive experimental investigation, and the results show that the SoC of the Li-ion battery can be estimated effectively with higher accuracy.