Battery State Estimation Using Mixed Kalman/Hinfinity, Adaptive Luenberger and Sliding Mode Observer

For electric vehicles, the improvement of the range of miles and with it the utilization of the available cell/battery capacity has become an important research focus in the community. For optimization of the same, an accurate knowledge of internal cell parameters like the state-of-charge (SoC) or the impedance is indispensable. Compared to the state-of-the-art, in this paper discrete-time Kalman and H_∞ filtering based SoC estimation schemes - up to now applied to linear battery models - are applied to the nonlinear model of a Li-Ion battery. For that, a linearization method is proposed, which utilizes a prior knowledge about the predominant nonlinearities in the model together with a coarse SOC estimate to obtain a linear state estimation problem. Based on that, a mixed Kalman/H_∞ filter-, a discrete-time sliding mode observer-, and an adaptive Luenberger based estimation scheme is furthermore investigated for the nonlinear battery model under test. The above-mentioned methods are compared to the state-of-the-art reduced order SoC observer and the Coulomb counting method. In order to compare the performance, an appropriate battery simulation framework is used, which includes measurement and modeling uncertainties. The evaluation is done with respect to the ability to reduce the impact of error sources present in realistic scenarios. For the simulated load current pattern, best results are achieved by the mixed Kalman/H_∞ filtering approach, which achieves an average SoC estimation error of less than 1%.