ABSTRACT

Electric vehicles that run on batteries have a major disadvantage of temperature abnormalities when operated at extreme working conditions. Therefore, thermal management of battery pack is essential to ensure its safety and performance. There are three operation strategies of thermal management air-based, liquid-based, and phase change material-based battery thermal management systems (BTMSs). Optimization studies on BTMSs have been focused mainly on the structural parameters as compared to the operating parameters. In liquid cooled multichannel flow-type BTMSs, equal flow rates are employed in all the channels. However, only a few studies have focused on variable coolant flow rates in channel-type liquid BTMS and its optimization. In this paper, a multichannel cold plate-based liquid BTMS is proposed for Lithium-ion battery pack comprising of two prismatic cells operating at 1 C discharge rate. Multi-objective optimization (MOO) technique coupled with computational fluid dynamics (CFD) simulations is used for obtaining optimal mass flow rate combination of coolant in the channels for reducing the power consumption of the BTMS without compromising on its thermal performance. Response surface methodology is adopted for the sensitivity analysis of the operating parameters and Multi Objective Genetic Algorithm (MOGA) approach is used to obtain the optimal solution set. The results showed a maximum reduction of 66.33%, 38.10%, and 43.56% for mass flow rate, maximum pressure and power consumption respectively in comparison to equal mass flow rate case whereas the temperature rise and temperature distribution of the battery system remain within the nominal range.

摘要

在极端的工作条件下运行时，使用电池供电的电动汽车的主要缺点是温度异常。因此，电池组的热管理对于确保其安全性和性能至关重要。有三种基于空气，液体和相变材料的电池热管理系统（BTMS）的热管理策略。与运行参数相比，BTMS的优化研究主要集中在结构参数上。在液冷多通道流动型BTMS中，所有通道均采用相同的流速。但是，只有很少的研究集中在通道型液体BTMS中的可变冷却剂流量及其优化上。在本文中，提出了一种用于锂离子电池组的基于多通道冷板的液体BTMS，该电池组包括两个以1 C放电速率工作的棱柱形电池。多目标优化（MOO）技术与计算流体力学（CFD）模拟相结合，用于获得通道中冷却剂的最佳质量流率组合，以减少BTMS的功耗，而不会影响其热性能。采用响应面方法对运行参数进行敏感性分析，并采用多目标遗传算法（MOGA）获得最优解集。结果表明，质量流量最大降低了66.33％，38.10％和43.56％，