State-of-charge (SOC) estimation of high power Ni-MH rechargeable battery with artificial neural network

Abstract

The paper presents a three-layer feedforward backpropagation (BP) artificial neural network (ANN), whose output is battery state-of-charge (SOC), to estimate and predict SOC of high power Ni-MH rechargeable battery. Five ANN inputs are novelly selected to improve the accuracy of ANN prediction by the proposed method of correlation coefficient ranking based on correlation analysis of different variables and SOC, and they are: battery discharging current i, accumulated ampere hours Ah, battery terminal voltage v, time-average terminal voltage tav and twice time-average voltage ttav (i.e. time-average of tav). Meanwhile, six training sets are equally selected from thirteen data sets about constant current discharging (CCD) from 100 % to 0 % SOC and Levenberg-Marquardt training algorithm is selected. Comparisons between simulation and measurement verify the proposed ANN model. Especially, the ANN can satisfyingly estimate SOC of battery (pack) whose starting SOC (i.e. SOC/sub 0/) is not originally known after about ten minutes (short time compared with the whole discharging process) constant load discharging (CLD), and most of absolute values of absolute errors are not more than 5 %.