SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error

Abstract

State of Charge (SOC) estimation is critical for battery powered devices in order to find out the remaining charge level. This process is relatively straightforward when the battery is in the resting state. However, it can be challenging while the device is operating, due to the process disturbances and model uncertainties. Various kinds of approaches have already been proposed in the literature like Neural Networks, Kalman Filtering, and Nonlinear Observers. Nevertheless, proposed methods in the literature do not have fast response for initial condition errors. This paper proposes a new implementation of Extended Kalman Filter, which improves the convergence characteristics of states for SOC estimation. The importance of initial condition errors is articulated in this paper, especially from an automotive perspective.