Parameters identification and discharge capacity prediction of Nickel–Metal Hydride battery based on modified fuzzy c-regression models

The battery in the electric vehicles provides the electrical energy necessary to power all electrical and electronic components and main-drive electric motor. So, an accurate estimation of discharge capacity to predict the battery’s end of life is of paramount importance and critical for safe and efficient energy utilization, especially for battery management systems. The resistor–capacitor (RC) equivalent circuit model is commonly used in the literature to model battery. However, a battery is a chemical energy storage system, and then the RC model will therefore be extremely sensitive to the presence of vagueness of information due to that some parameters cannot be directly accessed using sensors. In this paper, we propose a new design methodology for estimating simultaneously the model and the discharge capacity of a Nickel–Metal Hydride (Ni–MH) battery. A modified fuzzy c-regression model algorithm is used to construct a prediction model for a small Ni–MH battery pack. Then, the model, so developed, is used to estimate the discharge capacity of the battery and to predict its remaining useful life. The validity of the proposed method is experimentally verified. According to experimental results, the proposed method can achieve satisfactory results with no more than a 2% error rate for the training and test data sets.