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Volume 9 Issue 7
Jul.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(7): 1139-1165
K. L. Liu, Z. B. Wei, C. H. Zhang, Y. L. Shang, R. Teodorescu, and Q.-L. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. doi: 10.1109/JAS.2022.105599
Citation: K. L. Liu, Z. B. Wei, C. H. Zhang, Y. L. Shang, R. Teodorescu, and Q.-L. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. doi: 10.1109/JAS.2022.105599
shu

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

doi: 10.1109/JAS.2022.105599
  • 1.

    Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, United Kingdom

  • 2.

    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • 3.

    School of Control Science and Engineering, Shandong University, Jinan 250061, China

  • 4.

    Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark

  • 5.

    School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Funds:  This work was supported by the UK HVM Catapult project (8248 CORE), the National Natural Science Foundation of China (52072038, 62122041)
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    Abstract
  • Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

     

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    • AI-based manufacturing and smart battery to benefit battery health are showcased
    • Advanced AI solutions for battery life diagnostic and ageing prediction are reviewed
    • Control-oriented model and health-conscious charging to enhance battery longevity are presented
    • Current research gaps in the literature and remaining challenges are analyzed
    • Potential directions to achieve more technological breakthroughs are discussed

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