"Knees" in lithium-ion battery aging trajectories

Abstract

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of "internal state trajectories" (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee "pathways", including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation -- some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

Submitted to the Journal of the Electrochemical Society

References in corpus (7)

• Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life
• Predicting the impact of formation protocols on battery lifetime immediately after manufacturing
• Electrochemical kinetics of SEI growth on carbon black, I: Experiments
• Predicting battery end of life from solar off-grid system field data using machine learning
• Electrochemical kinetics of SEI growth on carbon black, II: Modeling
• Design of bi-tortuous, anisotropic graphite anodes for fast ion-transport in Li-ion batteries
• Estimation of Li-ion degradation test sample sizes required to understand cell-to-cell variability

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