Phase change material based thermal management of lithium ion batteries: A review on thermal performance of various thermal conductivity enhancers

World is slowly moving away from the conventional energy sources to renewable and sustainable energy alternatives. Introduction of electric vehicles in the market is a step in the right direction for the forward march towards energy security and reduction of carbon footprint. Lithium ion (Li-ion) batteries are popular for the use in electric vehicles, because of their high energy density, long cycle life and so on. The cell temperature has a significant impact on the reliability, safety and lifespan of these batteries, making thermal assessment of battery inevitable for its use in various applications. Phase change material (PCM) based thermal management is highly promising in this regard. Overcoming the low thermal conductivity of these materials is the key challenge in the development of a PCM-based battery thermal management system (BTMS). Apart from focussing on various aspects of Li-ion battery and PCM, the main thrust of this review paper is on providing necessary details of various thermal conductivity enhancing techniques used in tandem with PCMs. The thermal conductivity enhancers discussed are: metal fins, metal foams, metal mesh, carbon fibre, carbon nanotubes, graphene and expanded graphite. Machine learning techniques employed for multi scale modelling of PCM based Li-ion battery thermal management were also discussed. This review mainly focusses on the literature published in the last five years in order to provide new insights to the development of Li-ion battery thermal management using various PCM-thermal conductivity enhancer combinations. Finally, conclusions are drawn and recommendations are presented to highlight the research gap in this area.

Automated summary

This article discusses new approaches to lithium-ion battery thermal management using PCM-thermal conductivity enhancer combinations. The focus of the research is on discussing different thermal conductivity enhancing techniques and providing multi-scale modeling using machine learning techniques for PCM-based Li-ion battery thermal management.