Advanced thermal management with heat pipes in lithium-ion battery systems: Innovations and AI-driven optimization

Abstract

Heat pipes (HP) have been extensively used for thermal management in many sectors as a flexible potential heat transfer mechanism, including laptop computer CPUs, projectors, solar collectors, and battery thermal management systems (BTMSs). This study reviews and compiles the latest advancements in using HPs for efficient thermal management of high-performance lithium-ion battery systems. This review examines the most recent BTMS that are based on HPs, with a particular emphasis on the role of artificial intelligence (AI) in optimizing thermal performance. It also addresses significant distinctions from prior research, including AI-driven predictive models and hybrid cooling techniques. A classification is created using various wick topologies, working fluids within a lithium-ion BTMS's temperature range, and their appropriate envelope materials. The instances of each one's application in the BTMS or potential uses in the future have been presented. HPs are divided into several categories depending on their form (tubular, flat, loop, etc.) and each variety is given thorough explanations, illustrations, and data on how it performed in various trials. Furthermore, extensive literature research reveals AI's role in fine-tuning operational parameters, crafting algorithms to predict core temperatures in HP systems, and employing advanced optimization and deep learning techniques for efficient and safe management of HP-cooled reactors under stringent power limitations. Moreover, hybrid cooling strategies, including air-cooled, liquid-cooled, phase change material (PCM) cooled, and thermoelectrically cooled HPs, are also highlighted. Future research work recommendations have been provided for several studies on HPs to cool lithium-ion batteries.

Keywords: BTMS, Heat pipe, Hybrid BTMS, Lithium-ion battery, Wick Structure, Working Fluid, AI prediction, Deep learning