Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

计算流体力学 相变材料 材料科学 电池(电) 参数统计 热的 机械工程 工程类 汽车工程 热力学 航空航天工程 物理 功率(物理) 统计 数学
作者
Hasan Najafi Khaboshan,Farzad Jaliliantabar,Abdul Adam Abdullah,Satyam Panchal,Amiratabak Azarinia
出处
期刊:Applied Thermal Engineering [Elsevier BV]
卷期号:247: 123080-123080 被引量:80
标识
DOI:10.1016/j.applthermaleng.2024.123080
摘要

The focus on developing an effective battery thermal management system (BTMS) to maintain optimal temperatures for lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications, has grown significantly. The effective BTMS not only enhances the cooling performance of LIBs but also contributes to increased passenger safety and mileage of EVs. This study investigates BTMS configurations with fins, metal foam, and phase change material (PCM) to minimize temperature of battery during 3C discharging in varying conditions. Additionally, the study explores the impact of different BTMS material combinations and various fins lengths on system performance as a parametric investigation. Moreover, to streamline the analysis process and introduce novelty, artificial intelligence is explored as an alternative to computational fluid dynamics for predicting liquid fraction of PCM and temperature of battery, enhancing the innovative aspect of this study. Numerical simulations, using a non-equilibrium thermal model for metal foam modeling, reveal that the fourth case, integrating all three passive approaches, maintains the lowest temperature and enhances LIB cooling. The optimum BTMS shows a reduction of 3 K compared to BTMS utilizing pure PCM. During discharge process, the temperature difference in the battery decreases by approximately 75 % and 66 % in the fourth case compared to the first case (with pure PCM) under normal and harsh environmental conditions, respectively. Applying copper metal foam and copper fins yields the best results in reducing battery temperature. Increasing the length of fins and adding more fins effectively lower the battery temperature. Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. The model exhibits excellent predictive capabilities, achieving high R-squared values of 0.98 for PCM liquid fraction and 0.99 for battery temperature.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
天天快乐应助wangli采纳,获得10
1秒前
xu完成签到,获得积分10
2秒前
shining完成签到,获得积分10
2秒前
LINJMX完成签到 ,获得积分10
3秒前
地球发布了新的文献求助10
4秒前
wuliumu完成签到,获得积分10
5秒前
张辰熙完成签到 ,获得积分10
6秒前
无极微光应助ZZ采纳,获得20
6秒前
乐观半凡完成签到,获得积分10
6秒前
冷静的孙悟空完成签到,获得积分10
7秒前
安江涛完成签到,获得积分10
9秒前
灰灰完成签到,获得积分10
9秒前
10秒前
按时毕业的小王完成签到,获得积分10
11秒前
玩命的语兰完成签到,获得积分10
12秒前
骑着蚂蚁追大象完成签到,获得积分10
12秒前
天明发布了新的文献求助10
14秒前
ZZ完成签到,获得积分20
14秒前
orixero应助shh采纳,获得10
14秒前
爆米花应助shh采纳,获得10
14秒前
初七完成签到 ,获得积分10
14秒前
汉堡包应助shh采纳,获得10
14秒前
wanci应助shh采纳,获得10
14秒前
沉默的倔驴应助shh采纳,获得10
15秒前
乐乐应助shh采纳,获得10
15秒前
桐桐应助shh采纳,获得10
15秒前
脑洞疼应助shh采纳,获得10
15秒前
烟花应助shh采纳,获得10
15秒前
爆米花应助shh采纳,获得10
15秒前
ZXW完成签到,获得积分10
16秒前
敖江风云完成签到,获得积分10
16秒前
俭朴听双完成签到,获得积分10
18秒前
18秒前
MOMO发布了新的文献求助20
19秒前
Blank完成签到 ,获得积分10
20秒前
科研通AI2S应助科研通管家采纳,获得10
21秒前
bkagyin应助科研通管家采纳,获得10
21秒前
风趣如松应助科研通管家采纳,获得10
21秒前
房延彤应助科研通管家采纳,获得10
21秒前
在水一方应助科研通管家采纳,获得10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Introduction to Helicopter and Tiltrotor Flight Simulation, Second Edition 2500
Developing Genetic Editing Tools for Lysobacter 2000
卤化钙钛矿人工突触的研究 2000
Моделирование процессов самоорганизации в кристаллообразующих системах 1000
History of U.S. Space Surveillance and Satellite Cataloging 1000
Malcolm Fraser : a biography 700
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6512543
求助须知:如何正确求助?哪些是违规求助? 8306030
关于积分的说明 17743264
捐赠科研通 5614318
什么是DOI,文献DOI怎么找? 2923811
邀请新用户注册赠送积分活动 1901047
关于科研通互助平台的介绍 1762746