Battery degradation leads to irreversible reductions in capacity and power capability. Some degradation mechanisms can cause safety hazards, such as internal short circuits and thermal runaway. For energy storage to be adopted at scale it is essential to both diagnose present capacity and power capability and predict future behaviour, as well as identifying safety risks originating from particular modes of degradation. We are developing new approaches to diagnosis and prognosis by conducting long-term degradation experiments under realistic conditions and developing new models and tools to diagnose and predict degradation in real applications.
Zihao Zhou, Antti Aitio, Sam Greenbank, David Howey
Prof Peter Bruce (Oxford)
Prof Mike Osborne (Oxford)
- Faraday Institution Multiscale Modelling project, years 4-5
- “Data-driven battery state of health diagnostics and prognostics”, EPSRC iCASE Award with Siemens
- “Catalysing energy access in Africa through smarter energy storage management”, EPSRC/InnovateUK Award ref. EP/R035822/1
- “Battery degradation including accelerated ageing”, EPSRC iCASE Award with Jaguar Land Rover
- “Improved diagnostic tools and methods to determine the state of charge and health in lithium-ion battery packs and systems for grid energy storage”, extension to EPSRC project ref. EP/K002252/1.
- “Integration of electrochemical energy storage in sustainable energy systems”, sponsored by VITO
- “Diagnostics and prognostics of degradation in li-ion batteries”, EPSRC iCASE Award with Jaguar Land Rover
Recent publications and datasets
- S. Greenbank and D.A. Howey, “Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life”. IEEE Transactions on Industrial Informatics, in press, 2021.
- J.M. Reniers, G. Mulder, D.A. Howey, “Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries”, Journal of the Electrochemical Society, vol. 166, issue 14, pages A3190-A3200, 2019.
- R.R. Richardson, M.A. Osborne, D.A. Howey, “Battery health prediction under generalized conditions using a Gaussian process transition model”, Journal of Energy Storage, in press
- C.R. Birkl, D.A. Howey, “Oxford Battery Degradation Dataset 1”, 2017
- R.R. Richardson, M.A. Osborne, D.A. Howey, “Gaussian process regression for forecasting battery state of health”, Journal of Power Sources vol. 357, pp. 209-219, 2017
- C.R. Birkl, Roberts, M., McTurk, E., Bruce, P.G. and Howey, D.A., “Degradation diagnostics for lithium-ion cells”, Journal of Power Sources vol. 341, pp. 373-386, 2017