Lithium-ion battery degradation: how to model it
arXiv:2112.02037 · doi:10.1039/D2CP00417H
Abstract
Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models of battery degradation explicitly consider the interactions between more than two degradation mechanisms, and none do so within a single electrode. In this paper, the first published attempt to directly couple more than two degradation mechanisms in the negative electrode is reported. The results are used to map different pathways through the complicated path dependent and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open source modelling environment uniquely developed to allow new physics to be implemented and explored quickly and easily. Crucially it is possible to see 'inside' the model and observe the consequences of the different patterns of degradation, such as loss of lithium inventory and loss of active material. For the same cell, five different pathways that can result in end-of-life have already been found, depending on how the cell is used. Such information would enable a product designer to either extend life or predict life based upon the usage pattern. However, parameterization of the degradation models remains as a major challenge, and requires the attention of the international battery community.
arXiv commentFirst version submitted to Energy and Environmental Science on 15th November 2021. Second version submitted to Physical Chemistry: Chemical Physics on 25th January 2022
References in corpus (1)
Cited by in corpus (9)
- Electrochemical impedance spectroscopy beyond linearity and stationarity - a critical review
- A Single Particle Model with Electrolyte and Side Reactions for degradation of lithium-ion batteries
- Degradation model of high-nickel positive electrodes: Effects of loss of active material and cyclable lithium on capacity fade
- Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature
- Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
- Learning Optimal Forms of Constitutive Relations Characterizing Ion Intercalation from Data in Mathematical Models of Lithium-ion Batteries
- Accounting for the Effects of Probabilistic Uncertainty During Fast Charging of Lithium-ion Batteries
- Sequential Operation of Residential Energy Hubs using Physics-Based Economic Nonlinear MPC
- Lithium-ion battery degradation: Introducing the concept of reservoirs to design for lifetime