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The rapid growth of the battery industry has created an urgent need for faster process development, reduced experimental burden, and improved manufacturing efficiency. Mathematical modelling provides a powerful framework for understanding and optimising processes across the battery manufacturing chain.
This talk will explore how modelling can support battery manufacturing across a range of levels of complexity, from high-fidelity multiphysics simulations to reduced-order models, numerical solutions of governing transport equations, and simpler analytical tools used directly by engineers. Using examples from electrode manufacture, it will demonstrate how different approaches can be applied according to the question being addressed, balancing accuracy, computational effort and practical utility. The talk will show how these complementary techniques can be used to gain process insight, guide experimentation, explore large design spaces and support decisions from early-stage development through to industrial scale-up.
Drawing on examples from industrial practice, the talk will illustrate how modelling can shorten development timelines, reduce reliance on costly experimental programmes, and enable more effective scale-up of battery manufacturing processes. It concludes with a discussion of emerging opportunities for applied mathematics in next-generation battery production and digital manufacturing.