Functional thermal fluids and their applications in battery thermal management: A comprehensive review

热流体 电子设备和系统的热管理 传热 热的 热导率 粘度 纳米流体 材料科学 计算机冷却 对流换热 工艺工程 计算机科学 工程类 机械工程 物理 热力学 复合材料
作者
Xinyue Xu,Keyu Weng,Xitao Lu,Yuanqiang Zhang,Shuyan Zhu,Deqiu Zou
出处
期刊:Journal of Energy Chemistry [Elsevier BV]
卷期号:94: 78-101 被引量:18
标识
DOI:10.1016/j.jechem.2024.02.054
摘要

With the increasing requirements for fast charging and discharging, higher requirements have been put forward for the thermal management of power batteries. Therefore, there is an urgent need to develop efficient heat transfer fluids. As a new type of heat transfer fluids, functional thermal fluids mainly including nanofluids (NFs) and phase change fluids (PCFs), have the advantages of high heat carrying density, high heat transfer rate, and broad operational temperature range. However, challenges that hinder their practical applications remain. In this paper, we firstly overview the classification, thermophysical properties, drawbacks, and corresponding modifications of functional thermal fluids. For NFs, the high thermal conductivity and high convective heat transfer performance were mainly elaborated, while the stability and viscosity issues were also analyzed. And then for PCFs, the high heat carrying density was mainly elaborated, while the problems of supercooling, stability, and viscosity were also analyzed. On this basis, the composite fluids combined NFs and PCFs technology, has been summarized. Furthermore, the thermal properties of traditional fluids, NFs, PCFs, and composite fluids are compared, which proves that functional thermal fluids are a good choice to replace traditional fluids as coolants. Then, battery thermal management system (BTMS) based on functional thermal fluids is summarized in detail, and the thermal management effects and pump consumption are compared with that of water-based BTMS. Finally, the current technical challenges that parameters optimization of functional thermal fluids and structures optimization of BTMS systematically are presented. In the future, it is necessary to pay more attention to using machine learning to predict thermophysical properties of functional thermal fluids and their applications for BTMS under actual vehicle conditions.
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