Join the Coventry branch as they look at the role of modelling and software in battery manufacturing.
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Synopsis
Battery manufacturing is made up of various processes including: mixing the material, coating, calendering, and drying of electrodes, cutting and assembly of cathode, anode and separators, filling with electrolyte and finally formation and testing.
The large number of parameters involved in each step of Lithium-ion (li-ion) electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final products which are electrodes and the cells. Control and optimisation of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint.
This optimisation has been performed via trial-and-error traditionally which is associated with a huge waste and not in line with the net zero future goal. Data-driven models offer a solution for this manufacturing control and optimisation problem and underpin future aspirations for manufacturing volumes. In this context, machine-learning approaches when build upon the experimental data can serve well for the purpose of predicting final battery performance and relating the cell characteristics to the manufacturing settings to be able to determine them.
This talk will cover the li-ion battery manufacturing steps, modelling requirements, challenges, and the impact of the transparency achieved by machine learning optimisation on the battery industry.
About the speaker
Dr Mona Faraji Niri
Dr Mona Faraji Niri is a research-focused Assistant Professor of Battery Modelling at WMG, University of Warwick. She had her PhD in control engineering from Iran University of Science and Technology (IUST), and was a postdoctoral researcher in IUST, and a Senior Lecturer in Pooyesh Institute of Higher Education before joining WMG. Mona is a research fellow of the Faraday Institution, which empowers Britain’s Battery Revolution. She is a Fellow of the Alan Turing Institution in Artificial Intelligence and Data science, and a MIET member. She also holds a Fellowship from the Higher Education Academy (FHEA).
She is specialised in modelling, control and machine learning algorithms and has extensive experience in energy storage systems, li-ion batteries, battery management as well as electric vehicle powertrain. Her research interests also cover areas in optimisation of battery manufacturing processes via machine learning, and artificial intelligence.
Mona has been endorsed as Future Promise in this field by the Royal Academy of Engineering in 2021. She was the recipient of the TechWomen100 UK award in 2021 and recognised as WMG early career researcher of the year for 2022.
Mona's research on the application of AI for li-ion battery manufacturing was selected to receive the postdoctoral research excellence award in 2022 and put her in the shortlist of the IET Sir Henry Royce achievement medal.
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This event is brought to you by: BCS Coventry branch and BCS Leicester branch
