Comparison between two nonlinear Kalman Filters for reliable SoC estimation on a prototypal BMS

Energy Storage Systems (ESS)s have become widely pervasive in several sectors, both in the civil and in the industrial fields. Among the several applications, two of the most critical concern energy storing in the future Smart Grids and microgrids and power sourcing for Electric and Hybrid Vehicles. In this context, the management of the ESS represents a crucial task in order to guarantee efficient, effective and robust energy storing. The Battery Management System (BMS) is the device designated for performing this management. It has to avoid damages to the cell, to estimate the State of Charge (SoC), the State of Health (SoH) and to perform the cell equalization. In this paper, the SoC estimation by means of state observers has been investigated. In particular, the performances obtained by the Extended Kalman Filter (EKF) and by the Square Root Unscented Kalman Filter (SR-UKF) have been compared on a prototypal BMS. Results show that the SR-UKF succeeds in coping with the nonlinearities of the battery, obtaining better and more robust estimations than the classic EKF