A Novel MOGA approach for power saving strategy and optimization of maximum temperature and maximum pressure for liquid cooling type battery thermal management system. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Karthik, Aswin; Kalita, Pankaj; Garg, Akhil; Gao, Liang; Chen, Siqi; Peng, Xiongbin
• Is Part Of: International journal of green energy. Volume 18:Issue 1(2021)
• Page Start: 80
• Page End: 89
• Publication Date: 2021-01-02
• Subjects: Maximum temperature minimization -- liquid cooling -- electrochemical storage -- battery thermal management system -- power saving strategy -- multi-objective optimization; Power resources -- Research -- Periodicals; Energy industries -- Periodicals; Energy development -- Periodicals; 333.79
• DOI: 10.1080/15435075.2020.1831507
• Languages: English
• ISSNs: 1543-5075

Abstract

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.