Construction of battery charge state prediction model for new energy electric vehicles

Abstract

New energy vehicles have made considerable achievements after nearly a decade of development, but the range and safety of automobile battery packs are still a difficult problem for the industry to overcome. The charge state of the battery can reflect the charging and discharging situation and battery health, so it is significant to construct an accurate prediction model of the battery charge state. In view of this, an RBF neural network is utilized to predict the battery state of charge. Considering that the battery charge state is a nonlinear time series, the study introduces an integrated learning algorithm to further optimize the prediction model. Experimentally, the loss rate of the raised model is almost stable around 150 steps and the loss rate tends to be close to 0. The accuracy of the raised model also reached 81.60%, which is 2.84% and 1.80% higher than the control model, respectively; The recall rate of the raised model reached 77.92%, which was 4.73% and 25.31% higher than the control model, respectively. From the effectiveness test results, the raised model has higher accuracy and faster convergence rate than the control model, and the recall rate of the raised model is also higher, so the raised model has a certain progressiveness.