Predicting the Aging Rate of Capacity in Ni/H Battery Using Artificial Neural Network

Back-propagation artificial neural network was developed to study the relationship between the aging rates of capacity in Ni/H battery and alloying elements of cathode materials. Leave-one out method was used to train the ANN model. Test results showed that the prediction performance of the ANN model is satisfactory: the scatter dots distribute along the 0__45°diagonal line in the scatter diagram, the values of statistical criteria are 0.1195(MSE), 20.54%(MSRE), and 1.9144(VOF) respectively. Moreover, the ANN model was used to analyse the quantitative effects of chemical elements of cathode materials on the aging rate of capacity, results showed that the aging rate decreases with the increase of Ni content, increase with the increase of Co, Al and Si content, and change little with the change of La and Nd content.