Fayaz H, Afzal Asif, Samee A D Mohammed, Soudagar Manzoore Elahi M, Akram Naveed, Mujtaba M A, Jilte R D, Islam Md Tariqul, Ağbulut Ümit, Saleel C Ahamed
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Department of Mechanical Engineering, P. A. College of Engineering (Affiliated To Visvesvaraya Technological University, Belagavi), Mangalore, 574153 India.
Arch Comput Methods Eng. 2022;29(1):129-194. doi: 10.1007/s11831-021-09571-0. Epub 2021 Apr 26.
Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.
从减少空气和噪音污染从而改善全球环境状况的角度来看,新冠疫情为审视我们的地球带来了一个积极的视角。这场疫情的这一积极成果表明,能源的未来属于绿色能源,而锂离子电池(LIBs)是新兴的绿色能源之一。LIBs是电动汽车的核心,但它们面临一些重大问题,如热性能差、热失控、火灾隐患以及在低温和高温环境下更快的放电速率。因此,为了克服这些问题,大多数研究人员提出了控制和维持LIBs整体热性能的新方法。本综述论文主要关注不同电池热管理系统(BTMSs)下LIBs的热设计和结构设计参数的优化。本文中通常展示的优化后的BTMSs包括电池单元、电池组或电池模块的最高温度、温度均匀性、最大或平均温差、冷却剂入口温度、流速和压降。而本文突出的主要结构设计优化参数包括流道类型、流道数量、流道长度、流道直径、电池间距、进出口静压箱角度和流道布置。在本综述文章中,深入报道了在不同的BTMSs(如空气、相变材料(PCM)、微通道、热管和水冷)下研究的这些优化参数。数据进行了分类,并总结了每种方法的最新研究结果。对用于设计参数优化的各种优化算法(如蚁群算法、遗传算法、粒子群算法、响应面法、NSGA-II等)进行了批判性综述,并针对不同的BTMSs进行了分类,以推进其目标。单目标优化技术有助于获得与电池冷却系统热性能相关的重要设计参数的最优值。最后,还讨论了多目标优化技术,以了解如何在各种相互冲突的参数(如能量、成本、压降、尺寸、布置等)之间进行权衡,这些参数与电池系统的最小化以及热效率/性能的最大化相关。本综述对于致力于提高LIBs热性能和使用寿命的研究人员将非常有帮助。