Comparison between organic and inorganic PCMs providing effective battery thermal management – A machine learning approach

Abstract

Lithium-ion batteries (LIBs) are favored for their high energy density and long cycle life; however, their performance is highly sensitive to temperature fluctuations during charge-discharge cycles. To ensure effective heat dissipation, battery thermal management systems (BTMS) are required. The BTMS of LIBs is critical and highly essential, particularly as power batteries, in electric vehicle (EV) applications. An internal method of BTMS interferes with cell components, and hence, many researchers employed an external mode, a passive method through the use of phase change materials (PCMS) has attracted the research community. The present study, conducted using ANSYS 2023 R1, investigates the thermal performance of two LIB geometries using passive cooling strategies with phase change materials. The analysis evaluates the influence of PCM type, thickness, cell volume, and ambient temperature at discharge rates of 5 C, 6 C, and 8 C. Both organic and inorganic PCMS were analysed, demonstrating that thermal conductivity is a key factor in effective heat dissipation. Among the PCMs studied, Galinstan, an inorganic type, exhibited superior performance. Ambient temperatures significantly impact the use of PCMS and thus necessitate wider phase transition ranges to provide better adaptability. To enhance predictive capabilities, a machine learning model was employed, achieving high accuracy with an RMSE of 0.629 and an R² of 0.997. It is inferred that lower discharge rates are preferable under high ambient temperatures to ensure safe operation, even when using high thermal conductivity PCMs. Additionally, incorporating flame retardants or anti-corrosive agents, tailored to the PCM type, can further improve safety and system performance.