Sampled-Data Observer For Estimating The State Of Charge, State Of Health, And Temperature Of Batteries

This paper deals with the problem of sampled-data observer design to estimate the state of health (SoH), the state of charge (SoC), and the internal temperature for batteries. The observation is based on the estimation of the battery internal resistance, which is supposed to be unknown and varying with temperature. The difficulty is that the system state equation contains an unknown parameter (which represents the internal resistance) and an output-dependent term only accessible to measurement at sampling time. The proposed approach consists of several levels of estimation and calculation using non-linear observers and fitting functions exploiting experimental data. It has been shown, using theoretical analysis, simulations, and experimental results, that the proposed method gives a novel approach to estimate the quantities (SoH, SoC, and temperature) useful for the battery management system (BMS). The point is that this approach only involves the estimation of the internal resistance of the battery.