Prediction of Battery Thermal Management System Performance by AI Method

Authors

Devarajan Ramasamy, Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia AND Centre of Excellence for Advanced Research in Fluid Flow (CARIFF), Universiti Malaysia Pahang, Gambang 26300, Pahang, Malaysia
S. Subramaniam, Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia
Muhammad Danil Zikry Bin Ezani, Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia

Abstract

This paper presents a novel approach to predicting the performance of battery thermal management systems (BTMS) using artificial intelligence (AI) methods. The proposed method leverages machine learning algorithms to analyze large datasets of battery performance data and identify patterns that can be used to predict future performance. This project focuses on developing an AI model to predict battery temperature and investigating the performance of the AI model. The study limits its scope to using cylindrical lithium-ion batteries and employs Orange software for machine learning. The findings demonstrate that machine learning models, particularly Random Forest and Gradient Boosting, are highly effective in predicting BTMS performance. These models show superior performance with low Mean Squared Error (MSE), Mean Absolute Error (MAE), and high R² values, indicating near-perfect predictive accuracy. The research highlights the significant impact of battery spacing on thermal management and underscores the importance of optimizing spacer size to enhance BTMS efficiency. Future research should explore hybrid models and address scalability and computational efficiency for real-time applications.

First Page

7

Last Page

17

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Ramasamy, Devarajan; Subramaniam, S.; and Bin Ezani, Muhammad Danil Zikry (2025) "Prediction of Battery Thermal Management System Performance by AI Method," The Nexus of Sustainability and Energy Technology Journal: Vol. 1: Iss. 1, Article 2.
Available at: https://tnosaetj.alnoor.edu.iq/home/vol1/iss1/2