Modeling and identification of Ni-MH battery using dynamic neural network

Battery is a quite complex and nonlinear system comprised of interacting physical and chemical processes, although it may seem deceptively simple. For this reason, existing battery models are either partially successful or too complicated and inconvenient in modeling and simulation applications. This paper presents a dynamic neural network model with time-delayed system output feedback for modelling and identification of a Ni-MH battery during discharging. Comparisons between the simulation and measurement verify the presented model. Compared with the methods based on the Peukert equation, which is often used for the calculation of the available capacity and simulation of battery discharging curves, the ANN method is more accurate.