Lithium-ion battery thermal management via advanced cooling parameters: State-of-the-art review on application of machine learning with exergy, economic and environmental analysis

Background: Lithium-ion (Li-ion) batteries are one of the most attractive and promising energy storage systems that emerge in different industrial sectors -at the top of them electrical vehicles (EVs) and electronic devices -regarding the tight collaboration of scientific community and industry. Among crucial factors on performance of Li-ion batteries, thermal management is of great importance as it directly impacted the system from different views. Methods: In the present review, state of the art of advance cooling systems' (such as air/liquid-based cooling, PCM, refrigeration, heat pipe and thermoelectric) parameters of Li-ion batteries from different aspects are scrutinized. Exergy, economic and environmental (3E) analysis used as powerful tools to realize important pa-rameters in battery thermal management. Furthermore, data-driven and machine learning applications in thermal regulation of Li-ion battery and their impact on putting the next steps in this context have been discussed.Significant findings: The pros and cons of each system considering aforementioned tools are realized. Particularly, it was realized that machine learning can be play a vital role in this context while other parameters with respect to 3E analysis can put several steps for better thermal management. Finally, concluding remarks and recom-mendations and research gaps as the future directions presented.