State of Charge estimation of Li-ion battery in EVs based on second-order sliding mode observer

An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. A novel approach based on second-order sliding mode observer for battery state of charge (SOC) estimation has been proposed. The Thevenin equivalent circuit model is selected to model the li-ion battery and cooperative particle swarm optimization parameter identification technique is then utilized to estimate the parametersof the battery model. The performances of the algorithm are validated through some experiments and simulations. Validation results show that the proposed SOC estimation algorithm can achieve an acceptable accuracy within the error less than 2%.