Internal temperature prediction of ternary polymer lithium-ion battery pack based on CNN and virtual thermal sensor technology

In order to achieve real-time prediction of the battery internal temperature via the external temperature measured, a method for predicting internal temperature of a ternary polymer lithium-ion battery pack based on convolutional neural networks (CNN) and virtual thermal sensor (VTS) was proposed in this paper. A 128-channel thermometer was used to measure the internal (64 uniformly distributed points) and external (64 uniformly distributed points) temperature of the lithium-ion battery pack during seven discharge cycles for a total of 81 376 sets of data. The external temperature measured was used as the input of CNN and the internal temperature predicted as the output of CNN. CNN compared with linear regression (LR) to verify the difference of prediction accuracy. Mean square error (MSE), mean absolute error (MAE), max-error (MAXE), and goodness of fit (R-2-score) were used to evaluate the prediction accuracy. The results showed that the proposed method can accurately predict real-time temperature with the MSE as 0.047. In addition, this method does not require any knowledge of battery thermal properties, heat generation, or thermal boundary conditions.

Automated summary

In this paper, a method for predicting internal temperature of a ternary polymer lithium-ion battery pack based on convolutional neural networks (CNN) and virtual thermal sensor (VTS) was proposed. The results showed that the proposed method can accurately predict real-time temperature with the MSE as 0.047, without requiring any knowledge of battery thermal properties, heat generation, or thermal boundary conditions.