Remaining capacity prediction of lithium-ion battery based on the feature transformation process neural network

Abstract

In order to improve the prediction accuracy of discrete time series data, a lithium-ion battery remaining capacity prediction model based on feature transformation process neural network is proposed. According to the time series characteristics of lithium-ion battery performance degradation data, the remaining capacity prediction of lithium-ion battery is converted into a functional approximation method. The integral operation of continuous function is used to realize the time accumulation effect of network input data. In order to simplify the integral operation of continuous function, a discrete Walsh transform is performed on the input data, and the integral operation of continuous function is transformed into the inner product operation of the discrete Walsh transform pair. This method simplifies the integral operation of the continuous function and eliminates the loss of precision caused by the continuity of discrete time series data. A Levenberg-Marquardt network weight learning algorithm based on the discrete Walsh transform is developed. The model and algorithm are applied to predict the remaining capacity of lithium-ion batteries. The experimental results show that the model can reduce the average absolute error, average relative error and root mean square error of lithium-ion battery remaining capacity prediction to 0.0231AH, 1.73% and 0.0299 respectively.