Data-driven State-of-Charge estimator for electric vehicles battery using robust extended Kalman filter

An accurate battery State-of-Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to make two contributions to the existing literatures through a robust extended Kalman filter (REKF) algorithm. (1) An improved lumped parameter battery model has been proposed based on the Thevenin battery model and the global optimization-oriented genetic algorithm is used to get the optimal polarization time constant of the battery model. (2) A REKF algorithm is employed to build an accurate data-driven based robust SoC estimator for a LiFePO4 lithium-ion battery. The result with the Federal Urban Driving Schedules (FUDS) test shows that the improved lumped parameter battery model can simulate the dynamic performance of the battery accurately. More importantly, the REKF based SoC estimation approach makes the SoC estimation with high accuracy and reliability, it can efficiently eliminate the problem of accumulated calculation error and erroneous initial estimator state of the SoC.