What is the MSM project addressing?

The performance and lifetime of a battery in an electric vehicle (EV) depends not only on the underlying chemistry and physics of the chosen chemistry, but also on how cells are combined into a pack large enough to power an EV and the mechanisms controlling the local environment of each cell within that pack.

Link between simulations at different length-scales

There is a hierarchy of modelling scales, from those looking at atomistic interactions in materials, to those considering the behaviour of whole cells, packs and systems. Higher-level models can be made more accurate by incorporating the outputs of models at lower length scales.

Model parameterisation and validation

The accuracy of a model depends on the precision of its input parameters. In order to harness the accuracy of physics-based models, many parameters need to be carefully measured. The MSM project is developing new techniques to make this process simpler and faster.

Simulating complex cell degradation mechanisms

If we can model the processes which lead to cell degradation and failure, we can predict its remaining useful lifetime and avoid dangerous operation. The MSM project develops models for these processes and uniquely combines the models, reflecting the reality of multiple degradation mechanisms occurring simultaneously.

Predictive design tools

Being able to design cells and packs through digital simulations bypasses the enormous cost of prototyping, allowing engineers to predict how their ideas can change performance and lifetime.


Our open-source code has been designed in a flexible way, allowing new models to be developed, existing models to be customised easily and for different models to be run for the same cell, individually or in combination.


We develop models to address the key challenges facing the industry today, such as understanding fast-charging behaviour, with a view to reducing the damage it causes, or models of manufacturing processes help battery makers to optimise their production lines.

Problem summary

To simulate an EV battery pack, we need to consider a range of length scales, from the nanoscale, where atoms interact, right up to the macroscale of a complete pack and its electronic control mechanisms. In addition, a variety of time scales need to be considered, in order to assess atomic processes at the nanosecond through to long-term degradation occurring over years. Battery simulations and design tools exist at each length- and time-scale, but they are not linked together and often lack the accuracy required for understanding the unique phenomena occurring within batteries.

To advance current models and develop design tools which can accurately predict the performance and lifetime of existing and future batteries requires a fully integrated and tightly coordinated programme, drawing together the key modelling approaches capabilities into a multi-scale approach, across length and time scales.

In addition, few existing models consider the joint effects from different physical regimes, such as temperature and mechanics. The coupling between these regimes is poorly understood, but simulations which incorporate multiple effects are likely to provide more accurate predictions of as well as important insights into battery behaviour.

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