Comparative Analysis for State-of-Charge Estimation of Lithium-Ion Batteries using Non-Linear Kalman Filters

Battery Management System is critical for the working of the battery within safety margins. Therefore, monitoring and managing various enhanced battery features State-of-Charge(SOC) is an important parameter. Unlike in Internal Combustion Engine vehicles, where the remaining fuel can be easily measured, the same applies to Electric Vehicle Batteries. Several methods are used to estimate SOC, like Neural Networks, Coulomb Counting, Support Vector Machines(SVM), and many more. Recently Artificial Intelligence is also being used for this purpose. Kalman Filter is one of the Estimation algorithms used for SOC Estimation. This paper compares the Extended Kalman Filter and its variants for the SOC Estimation of Lithium-Ion Batteries. The different filters used in this paper include Extended Kalman Filter(EKF), Adaptive Extended Kalman Filter(AEKF), Q Adaptive Extended Kalman Filter(QAEKF), Iterated Extended Kalman Filter(IEKF), Hybrid Extended Kalman Filter(HEKF). The State-of-Charge Estimation is done using a first-order RC equivalent battery circuit. The LA92 driving cycle of the Turnigy battery is the dataset used for comparing these filters. All the simulations are done using MATLAB and Simulink. RMS error and Signal-to-Noise Ratio (SNR) are the criteria for comparing the Kalman Filters.