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

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.