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Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

Author

Listed:
  • Julan Chen

    (School of Aviation Maintenance Engineering, Chengdu Aeronautic Polytechnic, Chengdu 610000, China)

  • Guangheng Qi

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    Shandong Suoxiang Intelligent Technology Co., Ltd., Weifang 261101, China)

Abstract

Lithium-ion batteries, as a typical energy storage device, have broad application prospects. However, developing lithium-ion batteries with high energy density, high power density, long lifespan, and safety and reliability remains a huge challenge. Machine learning, as an emerging artificial intelligence technology, has successfully solved many problems in academic research on business, financial management, and high-dimensional complex problems. It has great potential for mining and revealing valuable information from experimental and theoretical datasets. Therefore, quantitative “structure function” correlations can be established to predict battery health status. Machine learning also shows significant advantages in strategy optimization such as energy optimization management strategy. For lithium-ion batteries, their performance and safety are closely related to the material structure, battery health, fault analysis, and diagnosis. This article reviews the application of machine learning in lithium-ion battery material research, battery health estimation, fault analysis, and diagnosis, and analyzes its application in aviation batteries in conjunction with the development of green aviation technology. By exploring the practical applications of machine learning algorithms and the advantages and disadvantages of different applications, this article summarizes and prospects the application of machine learning in lithium batteries, which is conducive to further understanding and development in this direction.

Suggested Citation

  • Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6318-:d:1229659
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    References listed on IDEAS

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    2. Igor Kabashkin, 2024. "Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems," Mathematics, MDPI, vol. 12(21), pages 1-32, October.

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