Tell us about your research

I use artificial intelligence and machine learning algorithms to optimise lithium-ion battery manufacturing processes. This technology is applied to decide the manufacturing process’s control factors, parameters and design variables. Battery manufacturing is a complex process and comprises multiple stages such as mixing, coating, calendaring, drying, assembling and testing. Each step has a considerable number of control and design variables. The current method in industry is to fine-tune these variables through trial-and-error, but this wastes huge amounts of material, resources and energy. I am trying to replace this trial-and-error approach with a more systematic approach to reduce costs and waste. Using machine-learning algorithms will allow factors to be decided based on the desired performance of the battery or desired quality of the intermediate products such as electrodes.

How do you describe why your work is important to non-specialists?

Currently, much work is being done on the part of industry and governments towards a net-zero future. The demand for lithium-ion batteries is increasing –  the capacity of lithium-ion batteries entering the global market could grow from around 185GWh in 2020 to 5,500 GWh in 2030, according to research and consultancy group Wood Mackenzie, which translates to approximately 140 billion regular lithium-ion cells in production each year. By developing this method of systematic optimisation, we aim to lower production costs and support the transition to high-volume manufacturing.

How did you get into battery research?

My background is in control engineering, and I have worked on developing different control algorithms for dynamic systems. I did a PhD in control systems at the Iran University of Science and Technology. I then worked there as a postdoc applying control algorithms and machine learning methods to renewable and clean energy systems. I saw a huge opportunity to connect algorithms and modelling techniques to lithium-ion batteries and decided to pursue this at the University of Warwick.

What accomplishment are you most proud of?

The connections we have made with other universities and across disciplines related to this line of research is in itself an accomplishment that I am very proud of. I am particularly proud of the research we did with the University of Birmingham studying the impacts of electrode slurry during the coating process on the quality of graphite anodes, published in Elsevier’s Energy Storage Materials. Our research received a lot of interest from industrial partners, and initiated further collaboration with other universities. My research on the application of AI for Li-ion batteries earned me an endorsement from the Royal Academy of Engineering as the Global Talent, Future promise in 2021 and TechWomen100 award the same year.

What is a highlight of your career to date or the aspect that gives you greatest job satisfaction?

I love how multidisciplinary and collaborative my work for Nextrode is. It’s cool to meet other partners from different universities and industries and try to look at a particular question or problem from different perspectives.

What opportunities has being part of the Faraday Institution opened up for you?

The Faraday Institution provides the resources and funding necessary for costly research involving cell manufacturing. The organisation has also created a highly collaborative environment through events and projects that bring together different partners from various disciplines. For me personally, I appreciate their support for training. The Faraday Institution gives each of its researchers a budget that they can spend on training, and I’ve used mine to learn new skills such as big data management and analysis, and leadership skills.

How have you found working on the Equality, Diversity and Inclusion (EDI) working group at the Faraday Institution?

I started on the EDI working group around a year ago. We discuss how to improve inclusion and diversity in the battery research community. We investigate how to best support researchers, for example by encouraging them to do specific training or making resources widely accessible. For example, we ensure webinars and seminars are accessible to everyone by taking relevant actions such as adding subtitles to videos or providing relevant follow-up materials.

We have also looked at how to support diversity and inclusion by recruiting from different career stages, supporting early career researchers, and supporting those coming from outside the UK.

What are the biggest challenges you have overcome during your career and how have you gone about doing so?

In the last couple of years of working with industrial partners, I have realised how difficult it is to translate research from an academic to an industrial context. It’s hard to convince industrial partners to move away from what they’re currently doing, as adopting new methods can be costly and risky. We manage this challenge by creating and showcasing pilot-scale methods to demonstrate their benefits to industry.

What advice would you have liked to have given your younger self starting out on your career?

Don’t be shy, and ask as many questions as you can! Collaboration in battery science is crucial because it is a multidisciplinary field that incorporates physics, electrochemistry, chemistry, modelling, and more, so no one researcher can know everything about it. The key is communicating, building links, and asking others for their contribution.

What are your career aspirations?

I love working as an academic at a university. I get to teach, mentor, tutor, and manage projects – there is so much more to this job than just research, and I love the combination. I want to persist in research and continue trying to bridge the gap between academia and industry, and maybe one day achieve a professorship.

What is your favourite thing about batteries?

Batteries are a combination of different phenomena – physical, electrochemical, chemical, and electrical – yet can still be viewed as a whole system with inputs and outputs. It’s interesting that many of the algorithms applicable to other dynamic systems are also applicable to batteries. In my research as a control systems engineer, I see the broader picture of batteries and investigate the inputs and outputs of batteries as a whole for improving their performance.

If people want find out more about your research, where would you point them to?

For a non-expert audience, I recommend the WMG Warwick talk I gave on machine learning – the brain of future Li-ion battery manufacturing.

For a more expert audience, I recommend the talk I gave on artificial intelligence and data science for optimised electrode and battery manufacturing for the Faraday Institution’s November 2021 conference.

For information about my research regarding AI for battery manufacturing I recommend my paper on quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via AI published in Energy & AI, and my paper on machine learning for optimised and clean Li-ion battery manufacturing published in the Journal of Cleaner Production. I would also recommend reading my work with the University of Birmingham regarding the impacts of electrode slurry during the coating process on the quality of graphite anodes, which can be accessed in Energy Storage Materials.

Also see the list of my publications on my Google scholar page.

 

Connect with Mona on Twitter and LinkedIn

 

 

Published October 2022.

 

About the author: Cara Burke is the Faraday Institution’s Science Communications Intern in the summer of 2022. She has just completed her BSc Biological Sciences degree at Imperial College London and is pursuing a career in science communications.