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Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review

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  • Li, Alan G.
  • West, Alan C.
  • Preindl, Matthias

Abstract

Lithium-ion battery (LIB) degradation is often characterized at three distinct levels: mechanisms, modes, and metrics. Recent trends in diagnostics and prognostics have been heavily influenced by machine learning (ML). This review not only provides a unique multi-level perspective on characterizing LIB degradation, but also highlights the role of ML in achieving higher accuracies with accelerated computation times. We survey the state-of-the-art in degradation research and show that existing techniques lay the foundations for a unified ML method – a single tool for characterizing degradation at multiple levels. This could inform optimal management of lithium-ion systems, thus extending lifetimes and reducing costs. We propose a framework for the hypothesized technique using pulse injection, digital-twinning, and neural networks, and identify the challenges and future trends in degradation research.

Suggested Citation

  • Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922004354
    DOI: 10.1016/j.apenergy.2022.119030
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