Abstract
To support the rapidly growing adoption of electric vehicles and renewable grid-storage systems, it is desirable to develop lithium-ion batteries with higher energy density and improved rate capability. However, the design of such energy-dense cell chemistries is impeded by a trade-off with cycling and calendar lifetimes. A key goal for the automotive and utilities industries is therefore to better understand the causes and mechanisms of degradation. This will enable improved control and prediction of the state of health of battery systems.As part of the Faraday Institution, the UK’s independent institute for electrochemical energy storage technology, the Battery Degradation Programme is building new understanding of the underlying physical and chemical processes that can lead to degradation in energy-dense NMC811/graphite lithium-ion cells as a model system. Led by the University of Cambridge (Clare Grey PI) in collaboration with 11 UK universities and institutions, the research consortium is working closely with industry partners to create a new hub for lithium-ion battery research and to address key challenges and opportunities in the field. This presentation will give an overview of the research consortium’s membership, key milestones, and technical progress.To date, the consortium has been applying a variety of analytical techniques to study degradation processes in NMC811/graphite cells. For example, electrochemical testing, operando synchrotron X-ray diffraction (XRD) and ex situ solid-state 7Li nuclear magnetic resonance spectroscopy (NMR) were combined to understand how changes in lithium dynamics correlate with the interlayer spacing changes that occur during delithiation.1 The method is now being applied to understand how the dynamics are affected by long-term structural damage to the NMC811 material. New spectroscopic methods are also being developed, including Kerr-gated Raman, which allows sensitive measurements of electrode materials and electrolytes with lower background signal than conventional Raman spectroscopy.2 X-ray computed tomography methods have been developed for in situ and operando studies of particle cracking during charging, cycling, or storage.3 Finally, Gaussian process machine learning is being applied to correlate electrochemical impedance spectral features with degradation patterns and to consider the value of EIS signals in battery management systems.4 Reference s: 1. K. Märker, P. J. Reeves, C. Xu, K. J. Griffith, and C. P. Grey, Chem. Mater., 31, 2545–2554 (2019).2. L. Cabo-Fernandez, A. R. Neale, F. Braga, I. V. Sazanovich, R. Kostecki, and L. J. Hardwick, Phys. Chem. Chem. Phys., 21, 23833–23842 (2019).3. T. M. M. Heenan, C. Tan, A. J. Wade, R. Jervis, D. J. L. Brett, and P. R. Shearing, Mater. Des., 191, 108585 (2020).4. Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming, and A. A. Lee, Nat. Commun., 11, 1706 (2020).
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