Design of a new optimized U-shaped lightweight liquid-cooled battery thermal management system for electric vehicles : A machine learning approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number106209
Journal / PublicationInternational Communications in Heat and Mass Transfer
Volume136
Online published25 Jun 2022
Publication statusPublished - Jul 2022

Abstract

Despite significant advances in battery thermal management system (BTMS), current electric vehicles (EVs) still lag behind user expectations in terms of battery performance and safety. A reliable, low-cost, and lightweight system is crucial for an effective BTMS that enables the battery's operation within a reasonable temperature range. In this study, a novel U-shaped lightweight liquid cooling method is introduced to optimize the thermal safety and weight of a prismatic battery cell. Numerical calculations are first carried out to investigate the effects of different fluid flow directions, flow rates, channel dimensions, fluid media, and discharge rates on the battery's temperature, and the results reveal that these factors simultaneously influence both the maximum temperature and temperature uniformity. To save computational costs and avoid the overwhelming complexity of the numerical measurements, a neural network-based regression model was constructed on the basis of 125 sets of simulation data, which comprised of four kernel functions with two outputs: maximum temperature and temperature difference. Each kernel function had a desirable testing accuracy (99.1%, 97.0%, 97.8%, 98.4%) after training, which indicates that the Gaussian Process Regression (GPR) with Matern 5/2 kernel function can better express complex non-linear relations for the prediction of maximum temperature and temperature difference with minimal errors between the simulated and predicted values compared with other kernel functions. Additionally, applying the GPR model to all cases studied, it is observed that Case 1 decreases the maximum temperature of the battery cell by 21% and lowers the weight of the cooling plate by 45% compared to the conventional liquid-cooled BTMS. Hence, it is chosen as the optimal design. The newly introduced BTMS can contribute to the development of lightweight and thermally safe battery cells.

Research Area(s)

  • Battery thermal management, Gaussian process regression, Lithium-ion battery, U-shaped lightweight

Citation Format(s)

Design of a new optimized U-shaped lightweight liquid-cooled battery thermal management system for electric vehicles : A machine learning approach. / Khan, Shahid Ali; Eze, Chika; Dong, Kejian; Shahid, Ali Raza; Patil, Mahesh Suresh; Ahmad, Shakeel; Hussain, Iftikhar; Zhao, Jiyun.

In: International Communications in Heat and Mass Transfer, Vol. 136, 106209, 07.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review