Application of Neural Network Model to Predicting Surface Temperature of Lithium-Ion Battery

The surface temperature of lithium-ion battery during charging/discharging is predicted by a model based on artificial neural network (ANN). The model is a three layers network, and there are four nodes in input layer, nine neurons in hidden layer and one node in output layer. Training results show that the model is of fast convergence and excellence training quality, which guarantees the prediction accuracy of surface temperature. The results of predicted temperature accord well with the experimental data, indicating that the constructed model is effective. Under ambient temperature of 80 ℃, battery's surface temperature is predicted as 86.71 ℃ at the end of 10C magnifying rate discharging, only 6.71 ℃ higher than the ambient temperature. The presented model could facilitate the research and development of battery thermal management system.