Optimized Cooling Solutions for Lithium-Ion Batteries in Electric Vehicles using PCM Composites

¹ Department of Mechanical Engineering, Nandha Engineering College, Erode 638052, Tamil Nadu, India
² Department of Mechanical Engineering, Sona College of Technology, Salem 636005, Tamil Nadu, India
³ Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, Tamil Nadu, India
⁴ Department of MBA, Panimalar Engineering College, Chennai 600123, Tamil Nadu, India
⁵ Department of Mechanical Engineering, Erode Sengunthar Engineering College, Perundurai 638057, Tamil Nadu, India
⁶ Department of Artificial Intelligence and Data Science, Muthayammal Engineering College, Rasipuram 637408, Tamil Nadu, India
⁷ Department of Electrical and Electronics Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem 636308, Tamil Nadu, India

^* Corresponding author: muthukumar.marappan@nandhaengg.org

Abstract

Electric vehicles that use lithium ion (Li-Ion) batteries as an alternative to fossil fuels have emerged as a viable solution to the environmental and sustainability problems associated with these fuels. Due to their sensitivity, Li-Ion batteries have been the subject of intense heat management research for the last ten years. There are a number of ways to regulate the complicated dynamics that cause Li-Ion batteries’ temperatures to rise. This work shows how to optimize the thermal management control variables using design of experiments (DOE), keeping it as the research emphasis. The variables used for optimization include the phase change materials mass denotes as X, the thermal conduction of paraffin aluminum composite denotes as Y, and the water flow rate denotes as Z. Researchers have looked at how these factors affect the rate of heat buildup in Li-Ion batteries. Studying the effect of Li-Ion battery temperature management parameters required a full factorial DOE with two repetitions. In order to evaluate the hypotheses, multivariate analysis made use of analysis of variance (ANOVA). This included controlling for both the 1^st and 2^nd order interface impact. All of the research factors significantly affected the increase in Li-Ion battery temperature, according to the hypothesis testing.