Underpinning a vibrant UK battery modelling ecosystem

Electrochemical battery models provide researchers and battery developers with meaningful insights into the physical processes internal to the cell. These are crucial for optimising cell use in pack engineering and for state estimation and control engineering within battery management systems. As the pace of commercialising battery technology quickens and new chemistries are introduced, physics-based models that simulate them are struggling to keep pace.

In response to this challenge, PyBaMM – an initiative nurtured within the Multi-Scale Modelling project fostered ecosystem and that has been downloaded 34,000+ times worldwide – has introduced significant advancements to its open-source code in 2023/4. Key improvements include enhanced hysteresis models, vital for accurately describing the silicon content in the negative electrodes of new cells being developed. Additionally, researchers at the universities of Southampton and Portsmouth have developed more chemically descriptive models for lithium iron phosphate (LFP) within the DandeLiion model. These models are adaptable to lithium manganese iron phosphate (LMFP), a cathode material anticipated to facilitate the adoption of lower cost battery technology at scale.

Inspired by the success of PyBaMM, the team is developing other open-source tools that industry is starting to use, such as PyBOP, a parameter optimisation tool, and PyECN, a framework for equivalent circuit models. In this way, the Multi-scale Modelling project and developments to PyBaMM and DandeLiion are underpinning a vibrant UK battery modelling ecosystem including industry partners Ionworks, Elysia and About:Energy. The latter, a spin-out from Imperial and the University of Birmingham involving Multi-scale modelling researchers, is commercialising and directly licensing advancements to PyBaMM and DandeLiion.

The growing industrial adoption of electrochemical models for battery development demonstrates the impact of these advancements, driven by the improved quality of predictive insights. These models are being leveraged to extend battery lifetime, reduce charging times, and shorten development timelines for new cells by end users, including McMurtry Automotive and Eatron Technologies.

Image: About:Energy and DandeLiion teams at a technology development meeting.

Case study published December 2024.