Battery temperature estimation at wide C-rates using the LSTM model based on polarization characteristics

Abstract

Data-driven based approaches have achieved significant results in estimating battery temperature. Nevertheless, the present challenge emanates from the dearth of theoretical guidance governing the training strategy of the model, leading to an inefficient training process and constrained accuracy, especially under specific conditions. In this paper, a Long Short-Term Memory (LSTM) neural network based on polarization characteristics is proposed to estimate the battery discharge temperature. Firstly, the LSTM temperature estimation model is established and the hyperparameters are optimized by Genetic Algorithm (GA). The results show that the accuracy of the traditional model is less satisfactory, with the Maximum Error (ME) of 3.79 °C. Subsequently, the voltage and polarization heat production characteristics of the battery are analyzed under various discharge conditions. It is found that the heat production of the battery is smaller at 0–1.5C, and the heat production characteristics are similar at medium-rate, but the change of heat production is significant when C-rate exceeds 3C. Finally, a LSTM temperature estimation model based on polarization characteristics is proposed, which divides the datasets according to heat production characteristics. Further, a strategy for predicting temperatures in later stages based on limited data from the current condition is also proposed, this mothed combines with the transfer learning method to rapidly develop models for different high-rate conditions. The ME of the model on the test set is 1.1 °C, and the training time is reduced by 32.93 s. The results show that the LSTM temperature estimation model based on polarization characteristics has higher accuracy and training efficiency than the traditional LSTM temperature estimation model.