Kalman filter estimation method for battery cell parameters in Reconfigurable Cascaded Multilevel Converter

This paper presents an online estimation technique Kalman filter-based able to evaluate both the open circuit voltage and the internal resistance of the battery cells installed within the Reconfigurable Cascaded Multilevel Converter. This technique fills the gap for the complex reconfigurable structures, allowing the estimation of the parameters with only the knowledge of the output current and voltage and instantaneous active battery cells. The estimation technique is combined with a sorting algorithm to address the specific topic of multilevel converter controlled with digital modulation. Specifically, the estimation algorithm here presented ensures a reduced convergence time, demonstrated by the low mean error computed. Moreover, the estimation technique is simulated during both discharging and charging processes, showing how different initial conditions may affect the convergence time depending on the power flow.