Integration of Computational Fluid Dynamics and Artificial Neural Network for Optimization Design of Battery Thermal Management System
Ao Li,
Anthony Chun Yin Yuen,
Wei Wang,
Timothy Bo Yuan Chen,
Chun Sing Lai,
Wei Yang,
Wei Wu,
Qing Nian Chan,
Sanghoon Kook,
Guan Heng Yeoh
Affiliations
Ao Li
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Anthony Chun Yin Yuen
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Wei Wang
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Timothy Bo Yuan Chen
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Chun Sing Lai
Brunel Interdisciplinary Power Systems Research Centre, Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, London UB8 3PH, UK
Wei Yang
School of Energy, Materials and Chemical Engineering, Hefei University, 99 Jinxiu Avenue, Hefei 230601, China
Wei Wu
Department Materials Science and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong 999077, China
Qing Nian Chan
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Sanghoon Kook
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Guan Heng Yeoh
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
The increasing popularity of lithium-ion battery systems, particularly in electric vehicles and energy storage systems, has gained broad research interest regarding performance optimization, thermal stability, and fire safety. To enhance the battery thermal management system, a comprehensive investigation of the thermal behaviour and heat exchange process of battery systems is paramount. In this paper, a three-dimensional electro-thermal model coupled with fluid dynamics module was developed to comprehensively analyze the temperature distribution of battery packs and the heat carried away. The computational fluid dynamics (CFD) simulation results of the lumped battery model were validated and verified by considering natural ventilation speed and ambient temperature. In the artificial neural networks (ANN) model, the multilayer perceptron was applied to train the numerical outputs and optimal design of the battery setup, achieving a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. The simulation results provide a practical compromise in optimizing the battery configuration and cooling efficiency, balancing the layout of the battery system, and safety performance. The present modelling framework demonstrates an innovative approach to utilizing high-fidelity electro-thermal/CFD numerical inputs for ANN optimization, potentially enhancing the state-of-art thermal management and reducing the risks of thermal runaway and fire outbreaks.
