Scientific mapping and review of the research landscape in battery thermal management strategies using heat pipe

Recent literature on the utilization of Heat Pipes in Battery Thermal Management Systems (BTMS-HP) has highlighted advancements and alternatives in the field. The existing literature on BTMS-HP technology is limited and lacks cohesive key information, which presents research hurdles for new researchers. Moreover, research and innovation rely on data-driven approaches, emphasizing the need for identifying key information for analysis, perspectives and mitigating technological risks. To achieve this, a comprehensive scientific mapping is conducted by extracting crucial insights and key knowledge from the dispersed literature of BTMS-HP technology for critical review. The key findings of this study are the trends for the last two decades shedding light on both the maturity and real-world applications of BTMS-HP methods, main contributors (countries, researchers, and production sources) to BTMS-HP technology, and the keywords emphasis in-depth of various novel and perspective areas related to the field. The scientific mapping key highlights a total of 610 publications in the field, showing three distinct phases of growth, with China, the USA, and India emerging as dominant contributors. Top 3 publication sources are “Applied Thermal Engineering”, “Journal of Energy Storage” and “International journal of heat & mass transfer”. The keyword analysis discusses various techniques employed to cool and preheat batteries using HP. Overall, the field of BTMS-HP is still in research phase, numerous methods have been implemented for improving BTMS with many focusing on results of temperature reduction and enhancing power output while overlook cost-effective strategies for power improvement. It is recommended that each method undergoes a detailed 4E (Energy, Exergy, Economic, and Environmental) analysis. Most studies have concentrated on battery surface cooling, there's a need for further research to compare the effectiveness of tab cooling versus battery surface cooling. Utilizing machine learning to optimize design parameters can be effective in reducing the weight of BTMS systems by accurately predicting battery temperatures.