Online Battery State of Power Prediction Using PRBS and Extended Kalman Filter

This paper presents a hybrid battery parameterization technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centered around an opportunistic initialization of a dual extended Kalman filter (DEKF) algorithm using pseudorandom binary sequence (PRBS) battery excitation. A second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilized to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC, and subsequently its SOP in real time. The experimental results obtained from an electrochemical impedance spectroscopy method give confidence to the performance of the proposed hybrid battery parameterization technique.