Advanced Automotive Propulsion Systems

A battery management systems (BMS) is essential to ensure the safety and longevity of electric vehicle operation. The BMS is responsible for monitoring the state of the battery, implementing suitable charging and discharging strategies (including cell-balancing and communication with charging infrastructure), and interfacing with thermal management systems to maintain safe operational conditions. There are various methods for battery state estimation including direct estimation (which is easy to implement but has relatively low accuracy), data-driven models (which offer increased accuracy but depend on the quality and quantity of available data), and physics-based models (which can be highly accurate but require intensive computational power).

The aim of this research is to develop reduced-order mathematical (physics-based) models for lithium-ion batteries, which retain important information about the battery's physical state whilst being suitable to be used within battery management systems, given their limited computational resources.

To achieve this aim, the following objectives will be met:

1) - Identify a suitable full-order model (system of differential equations) for the battery, which reasonably describes all the main processes within the battery. The initial considered model for ionic electrodiffusion in the liquid electrolyte is the Poisson-Nernst-Planck (PNP) system, which can be coupled with the Navier-Stokes equations to obtain the Navier-Stokes-Nernst-Planck (NSNP) system, modelling the convective effects of the fluid flow as well.

2) - Conduct analysis to determine relevant key properties of the model (such as conservation of mass, energy dissipation, etc).

3) - Design efficient numerical method(s) to solve the system, given initial and boundary conditions. This allows one to determine the battery state and its evolution over time, and the numerical schemes should ideally possess many of the same key properties identified in the previous objective. In the first instance, a discontinuous Galerkin finite element method approach will be taken, which removes the requirement for continuity across element boundaries compared to standard Galerkin finite element methods.

4) - Conduct numerical experiments to verify the accuracy of the numerical approximation(s) for the full model.

5) - Develop reduced-order models of the continuous system introduced in the first objective by following simplification strategies based on problem-specific knowledge. The reduced-order models should still give a reasonable approximation to the full model.

6) - Conduct analysis on the reduced-order models to determine key properties retained (ideally the models will be designed to retain most if not all of the properties of the full model).

7) - Design efficient numerical method(s) for the reduced-model(s), following the same ideas as in the third objective.

8) - Conduct numerical experiments to verify the accuracy of the numerical approximation(s) for the reduced-order model(s).

9) - Use machine learning approaches to improve accuracy (if deemed necessary).

The benefits of this project include increased accuracy in battery state estimation, with a logical model progression (due to the fundamental physics-based nature of the work) and path to even higher accuracies in the scenario where increased computing power is available within the BMS. This can lead to improved safety for users of the vehicle and longer battery lifetimes and range due to more informed management. These points together could lead to increased uptake of electric vehicles as a result of diminished range anxiety and safety fears, aiding the reduction of fossil-fuel powered vehicles on the road.

Impact Summary

This proposal has been developed from the ground up to guarantee the highest level of impact. The two principal routes towards impact are via the graduates that we train and by the embedding of the research that is undertaken into commercial activity. The impact will have a significant commercial value through addressing skills requirements and providing technical solutions for the automotive industry - a key sector for the UK economy.

The graduates that emerge from our CDT (at least 84 people) will be transformative in two distinct ways. The first is a technical route and the second is cultural.

In a technical role, their deep subject matter expertise across all of the key topics needed as the industry transitions to a more sustainable future. This expertise is made much more accessible and applicable by their broad understanding of the engineering and commercial context in which they work. They will have all of the right competencies to ensure that they can achieve a very significant contribution to technologies and processes within the sector from the start of their careers, an impact that will grow over time. Importantly, this CDT is producing graduates in a highly skilled sector of the economy, leading to jobs that are £50,000 more productive per employee than average (i.e. more GVA). These graduates are in demand, as there are a lack of highly skilled engineers to undertake specialist automotive propulsion research and fill the estimated 5,000 job vacancies in the UK due to these skills shortages. Ultimately, the CDT will create a highly specialised and productive talent pipeline for the UK economy.

The route to impact through cultural change is perhaps of even more significance in the long term. Our cohort will be highly diverse, an outcome driven by our wide catchment in terms of academic background, giving them a 'diversity edge'. The cultural change that is enabled by this powerful cohort will have a profound impact, facilitating a move away from 'business as usual'.

The research outputs of the CDT will have impact in two important fields - the products produced and processes used within the indsutry. The academic team leading and operating this CDT have a long track record of generating impact through the application of their research outputs to industrially relevant problems. This understanding is embodied in the design of our CDT and has already begun in the definition of the training programmes and research themes that will meet the future needs of our industry and international partners. Exchange of people is the surest way to achieve lasting and deep exchange of expertise and ideas. The students will undertake placements at the collaborating companies and will lead to employment of the graduates in partner companies.

The CDT is an integral part of the IAAPS initiative. The IAAPS Business Case highlights the need to develop and train suitably skilled and qualified engineers in order to achieve, over the first five years of IAAPS' operations, an additional £70 million research and innovation expenditure, creating an additional turnover of £800 million for the automotive sector, £221 million in GVA and 1,900 new highly productive jobs.

The CDT is designed to deliver transformational impact for our industrial partners and the automotive sector in general. The impact is wider than this, since the products and services that our partners produce have a fundamental part to play in the way we organise our lives in a modern society. The impact on the developing world is even more profound. The rush to mobility across the developing world, the increasing spending power of a growing global middle class, the move to more urban living and the increasingly urgent threat of climate change combine to make the impact of the work we do directly relevant to more people than ever before. This CDT can help change the world by effecting the change that needs to happen in our industry.