Battery Thermal Management System for electric vehicles using Phase Change Materials. (2022)

• Record Type: Journal Article
• Authors: Aswin Sevugan, P.; Pradeep, M.; Krishnaswamy, Abhishek; Karunamurthy, K
• Is Part Of: Materials today. Volume 51:Part 3(2022)
• Page Start: 1604
• Page End: 1611
• Publication Date: 2022
• Subjects: Battery -- Phase Change Material -- Electric Vehicles -- Lithium-ion Batteries -- Thermal Management System; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.10.468
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Electric Vehicles (EVs) owing to their relatively low emissions and noise pollution are the most suitable alternatives for conventional vehicles. Lithium-ion batteries which are widely used in Electric Vehicles are meant to operate within a strict temperature range of 20 °C–50 °C Battery Thermal Management Systems (BTMS) plays a vital role in maintaining this temperature range, improving performance, service life and safety of the battery. Conventionally, there are two categories of BTMS, namely active cooling and passive cooling. Owing to the drawbacks such as power consumption and design complexities involved in active cooling, passive cooling techniques are increasingly gaining popularity. Passive cooling using Phase Change Materials (PCMs) are excellent candidates for BTMS application. PCMs have high latent heat and can absorb a large quantity of heat generated from the battery, with minimal change in temperature. Two PCMs (n-Docosane and Sodium Thiosulfate Pentahydrate) were chosen based on their properties such as melting temperature, Latent heat, Specific heat, Thermal Conductivity, Dynamic Viscosity and Density. This article also provides a methodology to optimize the usage of PCM for BTMS applications. While excess usage of PCM will add to the battery's weight and also lead to ineffective use of PCM, using insufficient amount of PCM might lead to uncontrolled rise in cell temperature. This objective can be achieved by observing the trends of two specific output parameters – Mean Cell Temperature and Liquid Fraction derived from computational methods.