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

doi:10.3390/batteries8070069

Batteries (Jul 2022)

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

DOI: https://doi.org/10.3390/batteries8070069
Journal volume & issue: Vol. 8, no. 7
p. 69

Abstract

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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.

Published in Batteries

ISSN: 2313-0105 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Production of electric energy or power. Powerplants. Central stations; Technology: Chemical technology: Industrial electrochemistry
Website: http://www.mdpi.com/journal/batteries

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