Multi-scale Modelling
This project has brought together a world-leading, multidisciplinary team to develop fast, highly accurate models, enabling the development of digital twins to speed up battery development and ensure safe operation for longer battery life.
Accurate simulations of batteries will enable battery makers to improve designs and performance without creating expensive prototypes to test every new material or new type or configuration of cells. The project considers a range of length scales, from the nanoscale – where atoms interact – up to the macroscale of a complete pack and its electronic control systems. A range of timescales are also considered from the movements of atoms at the nanosecond, through to long-term degradation occurring over years. Battery simulations and design tools exist at each length and timescale, but they have previously lacked the accuracy required for understanding the phenomena occurring within batteries.
The project’s world-leading research bridges science and engineering, working innovatively alongside UK industry to deliver impact. Its internationally recognised experts are developing new digital and experimental techniques for understanding battery behaviour at the atomistic, continuum and system scales. Fast, accurate models, incorporating the most complete physics and advanced mathematical techniques are being developed to be directly usable for industry, enabling digital twinning of whole cells and packs. Atomistic accuracy will parameterise higher level models and tackle key challenges, such as the complex interactions and activity at the electrolyte-electrode interface. Rapid experimental parameterisation methods are being developed, greatly reducing the time and cost of customising models for specific applications.
The experimentally validated models resulting from the project should be capable of predicting the useful life of lithium-ion batteries, as a function of how they are both made and used.
Timeline with milestones / deliverables (to March 2026)
- Continue to expand on the physics and degradation models in PyBaMM (Python Battery Mathematical Modelling) and examine coupling effects between them.
- Expand on the parameter estimation tool, PyBOP, and continue to link this with PyBaMM.
- Extend the advanced atomistic models of crucial reactions at interfaces and bridge this scale to the continuum scale modelled in PyBaMM, by feeding in estimates for parameters that are difficult to measure experimentally.
- Expand on the common code base for equivalent circuit network models (ECNs), PyECN.
- Grow the modelling ecosystem with other tools, such as PyBOP and PyPROBE.
- Examine the processes that occur during the formation cycles of a newly manufactured cell and how this can set the trajectory for its performance and lifetime.
- Add models for more phase changing materials, such as lithium manganese iron phosphate and electrodes based on silicon or sodium.
- Extend the data set on long-term cell ageing, using rigorously controlled experiments.
- Implement models for advanced state estimation and control.
Project innovations
A common coding framework – PyBaMM – has been established and multiple degradation mechanisms added. It is an open-source model, which is easy to use and provides a high-quality resource for the battery community to explore the mathematical theories with a minimum of coding effort. As of March 2025, PyBaMM has been downloaded over 1 million times worldwide.
Rigorous, standardised parameterisation techniques have been developed. Spin-out About:Energy is providing parameterised models as a service to increase access for industry. A second start-up, Ionworks is bringing the benefits of PyBaMM to a wider audience, including industry, via consultancy and the development of professional user interfaces. The Battery Parameter eXchange is an open standard to support the wider of adoption of physics -based models by the battery industry.
Improvements to atomistic modelling were released as part of ONETEP and an ultrafast solver called DandeLiion has also been developed, which is optimised for speed. The physical models in both PyBaMM and DandeLiion now incorporate thermodynamics, mechanics and long-term ageing.
Duration
1 March 2018 – 31 March 2026
Project funding
£25.7 million
Principal Investigator
Professor Gregory Offer
Imperial College London
Project Leader
Dr Jacqueline Edge
University of Birmingham
Project Manager
Dr Saira Naeem
Imperial College London
University Partners
Imperial College London (Lead)
University of Birmingham
University of Oxford
University of Bristol
University of Portsmouth
University of Southampton
University of Warwick
Research Organisations, Facilities and Institutes
UK Battery Industrialisation Centre (UKBIC)
+ 17 Industrial Partners
