Advancing Battery Safety: Machine Learning-Driven Thermal Management and Cloud-Based Analytics

In this paper, we explore how machine learning can be used to improve battery safety in an machine learning thermal management system, with a focus towards cloud battery safety analytics. Describing how critical the battery fire hazard mitigation is, the aim of this study is to make predictions of real values set as temperature by comparing predicted values based on three machine learning models used as linear regression, decision tree and random forest. The experimental results were analyzed based on performance metrics (explained variance), training time and prediction time for lithium iron phosphate (LiFePO4), The Random Forest model the most accurate demonstrated by the highest R² (0.997), least MSE (0.0024) and least MAE (0.026). Learning curve and action taken curves confirm superiority of random forest model. the decision tree (R² = 0.99 0 and MSE Lowest belonging model) also gave good results as were already in earlier models. Linear regression (fastest, least accurate with an R² of 0.604) The results of this research highlight how essential cloud-based battery safety analytics are in harvesting ML driven methodologies for ideal mitigation of fire hazards and recommend for the use of ML particularly random forest model so as to operate energy storage systems reliably.