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Title
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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
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Type
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JournalPaper
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Keywords
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Li-ion battery Thermal regulation Artificial neural network (ANN) Deep learning Data-driven methods Energy storage
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Abstract
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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 parameters 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 recommendations and research gaps as the future directions presented.
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Researchers
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Nader Karimi (Not In First Six Researchers), zafar Said (Not In First Six Researchers), Masoud Afrand (Not In First Six Researchers), Sadegh Aberoumand (Fifth Researcher), Amin Shahsavar Goldanloo (Fourth Researcher), Shahin Shoeibi (Third Researcher), Fatemeh Norouzpour (Second Researcher), Seyed Masoud Parsa (First Researcher)
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