Soc Estimation Of Li-Ion Battery Of Electric Vehicle Based On Ekf

Abstract

Due to the rising concern about global warming and depletion of fossil fuels, electric vehicles powered by lithium batteries are expected to become more popular over the next decade. Effective battery management relies on precise inference of state-of-charge (SoC) parameter which alerts drivers of their vehicle’s range capability. SOC is a demanding battery monitoring parameter and has a high impact on predicting the vehicle mileage, boosting battery life, and enhancing electric vehicle performance. In this work, a novel SOC prediction model based on Extended Kalman Filter (EKF) integrated with Thevenin equivalent battery circuit model is proposed. First, the LI battery is modeled in MATLAB/SIMULINK using a first - order resistor-capacitor (RC) equivalent circuit and battery parameters are calculated by conducting a pulse discharge test. As the battery’s discharge characteristics are nonlinear, EKF is preferred over simple Kalman Filter. The EKF algorithm is simulated under MATLAB environment. The actual SoC of the cell is obtained from the lithium-ion cell model and the estimated SoC is obtained from output of the EKF block. When compared it was found that the estimated value following the actual value with an error of 0.01. The findings demonstrate that the algorithm has good robustness that can match the functional requirements of technological applications.