Predicting temperature of a Li-ion battery under dynamic current using long short-term memory

The growing energy demands of modern society have led to an increased reliance on secondary batteries, particularly lithium-ion (Li-ion) batteries, due to their superior energy density and power output. These batteries perform most effectively and safely within a specific temperature range, making it essential to develop accurate models for predicting temperature variations under diverse operational and environmental conditions. In particular, it is crucial to forecast temperature changes resulting from random and dynamic current fluctuations, reflecting real-world usage scenarios while considering the surrounding battery system environment. In this study, we employed a long short-term memory (LSTM) network to develop a surrogate model capable of predicting the battery’s core temperature over time, given varying current loads and heat transfer coefficients. The LSTM model demonstrated remarkable accuracy, achieving an average prediction accuracy of 99% in simulating temperature changes induced by arbitrary currents. • LSTM surrogate model to predict temperature is trained with proper hyperparameters. • Average temperature prediction error of the LSTM model is 0.81 K for validation data. • The median prediction error for current and cooling load changes is under 1 K. • 96% of temperature prediction accuracy is achieved for complex battery operation.