A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles

One of the most important aspects in battery management systems (BMS) in electric vehicles is the state of charge (SOC) estimation. SOC needs to be accurately determined for safety and performance reasons but cannot be measured directly due to the flatness and hysteresis of the open circuit voltage (OCV) curve of Lithium-ion chemistries as LiFePO4. The classical approach of current integration (Coulomb counting) cannot solve the problems of accumulative error and inaccurate initial values, thus advanced estimation algorithms are applied to determine the sate of charge. In this work, three model-based state observer designs including Luenberger observer, Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF) are carried out and studied. These estimation approaches are verified using measurement data acquired from commercial LiFePO4 cells. In addition, computational tests analyze the systems performances in terms of tracking accuracy, estimation robustness against temperature uncertainty, sensor drift, and convergence behavior with an initial SOC offset.

► An equivalent circuit is used to describe the characteristics of LiFePO4 batteries.
► Three different model-based algorithms are designed to estimate the battery SOC.
► The estimation approaches are verified with two typical driving profiles.
► The system robustness and the convergence behavior of SOC estimators are compared.