Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data

Natthida Sukkam, Tossapon Katongtung, Pana Suttakul, Yuttana Mona, Witsarut Achariyaviriya, Korrakot Yaibuathet Tippayawong, Nakorn Tippayawong

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system.