Batteries temperature prediction and thermal management using machine learning: An overview

Batteries, particularly lithium-ion batteries, play an important role in powering our modern world, from portable devices to electric vehicles and renewable energy storage. However, during charging and discharging, they generate heat due to chemical reactions within them. This heat can lead to reduced performance, shortened lifespan, and even safety risks if not properly managed. To address this problem, Machine learning has been emerged as a changing tool in battery technology due to its ability to analyze large datasets that can be used in predicting battery temperatures and enhancing their thermal management. In this work, we address machine learning features along with a look at its various learning categories, frameworks, and applications. In a comprehensive study, various machine learning methods and neural networks used in battery temperature prediction and thermal management are analyzed and discussed along with its various training algorithms. Moreover, the paper reviews and summarizes various research publications examining battery temperature prediction and battery thermal management using the various machine learning algorithms. As a result, there is no superior machine learning algorithm for battery temperature prediction and thermal management, as the performance of the model may vary depending on the data set, training algorithm, and other parameters. However, among these machine learning algorithms researchers are preferring to use artificial neural networks due to its accuracy and model complexity. In particular, artificial neural network integrated with proper cooling technology can reduce the battery temperature by more than 25%.