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Cloud-Based Artificial Intelligence Framework for Battery Management System

Author

Listed:
  • Dapai Shi

    (Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, China
    Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China
    These authors contributed equally to this work.)

  • Jingyuan Zhao

    (Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA
    These authors contributed equally to this work.)

  • Chika Eze

    (Department of Mechanical Engineering, University of California, Merced, CA 95343, USA)

  • Zhenghong Wang

    (Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China)

  • Junbin Wang

    (BYD Automotive Engineering Research Institute, Shenzhen 518118, China)

  • Yubo Lian

    (BYD Automotive Engineering Research Institute, Shenzhen 518118, China)

  • Andrew F. Burke

    (Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA)

Abstract

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Suggested Citation

  • Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4403-:d:1159268
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