Physics-Informed Machine Learning for Battery Pack Thermal Management

• Published in: Proceedings. Annual Reliability and Maintainability Symposium pp. 1 - 7
• Main Authors: Liu, Zheng, Jiang, Yuan, Li, Yumeng, Wang, Pingfeng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.01.2025
• Subjects: Batteries; battery thermal management; Computational modeling; Convolutional neural networks; Finite element analysis; Heat transfer; Mathematical models; Physics-informed machine learning; Safety; Temperature distribution; Thermal management; Training
• ISSN: 2577-0993
• DOI: 10.1109/RAMS48127.2025.10935157

With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences batteries' performance and safety. Battery thermal management systems can effectively control the temperature of batteries; therefore, batteries' performance and safety can be ensured. However, the development process of battery thermal management systems is time-consuming and costly due to the extensive training dataset needed by data-driven models requiring enormous computational costs for finite element analysis. Therefore, a new approach to constructing surrogate models is needed in the era of AI. Physics-informed machine learning enforces the physical laws in surrogate models, making it the perfect candidate for estimating battery pack temperature distribution. In this study, we first developed a 21700 battery pack indirect liquid cooling system with cold plates on the top and bottom with thermal paste surrounding the battery cells. Then, the simplified finite element model was built based on experiment results. Due to the high coolant flow rate, the cold plates can be considered as constant temperature boundaries, while battery cells are the heat sources. The physics-informed convolutional neural network served as a surrogate model to estimate the temperature distribution of the battery pack. The loss function was constructed considering the heat conduction equation based on the finite difference method. The physics-informed loss function helped the convergence of the training process with less data. As a result, the physics-informed convolutional neural network showed more than 15% improvement in accuracy compared to the data-driven method with the same training data.