Nextrode: Electrode Manufacturing - £12m, 48 months.
Lead partner University of Oxford with University of Birmingham, University College London, University of Sheffield, University of Southampton, University of Warwick, UKBIC and 12 industry partners.
Funder – The Faraday Institution. Project completion 2023.
Vision: To research new methods for manufacturing smarter electrodes and to put them onto the path to commercialisation.
- Support an agile electrode fabrication capability; re-optimise slurry casting parameters, validating at lab, intermediate and production scale
- Enable the production of Li-ion batteries with smart electrodes: reduce degradation rates and increase energy density at high charge/discharge rates
- Demonstrate smart electrode manufacturing technology and performance benefits in a scalable battery format
- Provide a suite of modelling and characterisation tools that link microstructural features to electrochemical performance and design-driven structural optimisation of battery structures, suitable for a broad range of battery formulations
WMG Input: The objectives for WMG are to create an AI based model of the process stages associated with electrode and cell manufacture (e.g. mixing, coating, drying, calendaring), as well as to create a value-chain model for electrode manufacture at different volume scales.
An iterative research approach is being adopted at WMG (akin to established agile software development methods) in which the creation of the AI model for electrode manufacture is decomposed into four tasks: (1) experimental design, (2) data collection and curation, (3) model creation and prediction, (4) optimised electrode manufacture. These tasks are being applied to each manufacturing stage in-turn. Initially the perceived order of study was: coating (1), drying (2), calendaring (3) and mixing (4). The advantage of this scientific approach is: (a) as the research team transitions to each manufacturing stage they will be able to assess the transferability of the AI approach to different processes; (b) it ensures that validation data is available to the project sooner and (c) it facilities greater opportunities for regression testing of the of the AI model(s) as the project progresses and new data/facilities becomes available.