Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries

By Jorn M. Reniers, David A. Howey, Grietus Mulder. Journal of the Electrochemical Society, 2019

385 citations recorded by the source corpus.

The maximum energy that lithium-ion batteries can store decreases as they are used because of various irreversible degradation mechanisms. Many models of degradation have been proposed in the literature, sometimes with a small experimental data set for validation. However, a thorough comparison betweendifferent model predictions is lacking, making it difficult to select modelling approaches which can explain the degradation trends actually observed from data. Here various degradation models from literature are implemented within a single article model framework and their behaviour compared. It is shown that many different models can be ?fitted to a small experimental data set. The interactions between different models are simulated, showing how some of the models accelerate degradation in other models, altering the overall degradation trend. The effects of operating conditions on the various degradation models is simulated. Thisidentifies which models are enhanced by which operating conditions and might therefore explain specific degradation trends observed in data. Finally, it is shown how a combination of different models is needed to capture different degradation trends observed in a large experimental data set. Vice versa, only a large data set enables to properly select the models which best explain the observed degradation.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/0313987e8d0e05df34d1867dd6d82e1537dfbfd5

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Citations in the corpus

A continuum of physics-based lithium-ion battery models reviewed (2022, methodology): “Oxygen evolution from the positive electrode has been conventionally modelled as oxidation of electrolytes at the positive electrode side using a simple kinetically limited Tafel equation, with this kind of model being proposed in several literature reviews [73, 104].”
The challenge and opportunity of battery lifetime prediction from field data (2021, background): “For example even when a large number of degradation sub-models are included, some aspects of the experimental dataset still cannot be fitted accurately [8].”
Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, background): “Perhaps one of the most comprehensive descriptions of physics based battery degradation models is that of Reniers et al. [3] who presents and compares models with degradation mechanisms including: SEI layer growth, loss of active material, pore blocking, lithium plating, active material dissolution…”
Review—"Knees" in Lithium-Ion Battery Aging Trajectories (2022, neutral): “Reniers et al. 95 illustrated a positive feedback mechanism between the mechanical stress and loss of active material, leading to a snowballing knee.”
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, background): “[1-3], such as the formulation of the solid electrolyte interface, lithium plating during operation, and current collector degradation.”