LAUSR

Abstract This paper presents a simple approach to estimate the state of charge of the LiFePO 4 batteries in electric vehicles under dynamic loads and variable temperature, which are inevitable during practical working conditions. This approach employs a highly adaptive estimation algorithm based on the recursive least-squares method and an original simple model of the open circuit voltage to the state of charge over a wide range of temperature. The modeling and the estimation in this approach are based on a new term for the state of charge, which is defined based on experimental findings to take into account the battery recovery capacity due to temperature variations. The proposed approach is validated through Urban Dynamometer Driving Schedule experiments including harsh temperature conditions, which have been mostly overlooked in previous research. The obtained results show that this approach maintains an accurate state of charge estimation, with an error of less than 5.2% under such conditions. The accuracy and the simplicity of the proposed algorithm are crucial for a feasible battery management system to be used in electric vehicles.