Comparative Analysis of the Model Based State of Charge Estimation Methods for Li Ion Batteries Used in Electric Vehicle Applications 2021-26-0163

Electric vehicles are getting increasing importance because of the absence of vehicular emissions, simpler design, and efficient control. Li Ion based battery pack is most preferred in electric vehicles due to higher power and energy density, low discharge rates and longer life. In order to operate Li Ion Cells in a safe and efficient manner, robust battery management system (BMS) is essential. Accurate estimation of State of Charge (SOC) is important BMS function. It affects many functions for e.g. remaining vehicle range estimation, cell balancing and cell protections etc. Coulomb counting method is simplest method to estimate SOC. However, it requires highly accurate and precise current sensor and requires information of initial SOC. Model Based SOC estimation techniques use mathematical model of the Li Ion cell to estimate the cell SOC. They are robust to the variations in the Li ion cell parameters as compared to the coulomb counting method. This paper compares various Model Based SOC estimation methods viz. Luenberger Observer, PI Observer, Sliding Mode Observer and Unscented Kalman Filter on various metrics. Convergence time, Accuracy of estimation, effects of voltage and current sensor accuracy and variations in battery pack parameters are the metrics considered to assess effectiveness of these methods. Li Ion Cell plant model was developed based on Equivalent Circuit approach. Developed Li Ion cell plant model was used to design the mentioned Model based SOC estimation methods. Estimation models are developed in MATLAB-Simulink environment. Standard test profile is used to assess the above-mentioned metrics. Finally, a comparative analysis of all the above-mentioned estimation techniques is presented.