If you are from industry and interested in any of these projects please contact us by emailing aapscdt@bath.ac.uk

If you are student and are interested in any of these projects then please complete an expression of interest and state which project you are interested in.

Automated simulation management of complex co-simulation test systems for advance propulsion systems

  • Supervisors: Chris Brace
  • Industrial Partners: Slaven Glumac (AVL), Lukas Pichler (AVL), Dietmar Peinsipp (AVL)
  • Start: October 2020

Future complex automotive propulsions systems will be developed combining experimental and simulation techniques capturing the different physical phenomenon of the systems (battery chemistries, combustion, electric drives). At different stages of the propulsion system development phase, we'll use co-simulations as a combination of different mathematical models and also real hardware in Hardware-in-the-loop configurations. However, relying on simulation requires high confidence in the accuracy of the simulation model to make sure we get high quality data and results.

This project aims to create a methodology for monitoring the co-simulation and to terminate it at the earliest possible moment in case an error occurs. This error could be caused by systems errors (software component crash, real-time simulation misses deadline) or by simulation errors (unstable signal, low-signal quality, wrong trigger sequence, incorrect frequency). You'll produce a prototype implementation of your methodology and demonstrate this in the prototype factory at the new Institute for Advanced Propulsion Systems (IAAPS).

Enabling Effective Transdisciplinary working with the automotive sector

Over the past few decades transdisciplinary (TD) has been the subject of increased discourse in the context of large, complex, ill-defined, ‘wicked’ problems. However, there has been less consideration of the potential it offers within the practice of engineering. This research looks to create tools which enable effective TD working within the automotive sector. The Mobility Engineering 2030 FISTA White Paper identifies that changes within the sector mean that interdisciplinary working, involving groups formed from people working in similar disciplines, will not be sufficient. It recognises that in the future there will be a need for transdisciplinary working, which goes beyond the academic disciplines to understand the societal context. For example, legislation, standards, culture. However, achieving effective TD working within organisations is not simple. It requires the creation of tools (e.g. processes and methods) which enable clear communication and knowledge transfer within and beyond an organisation. This PhD will leverage input from the TREND (TRansdisciplinary ENgineering Designers) £1.8m platform grant (Dec 2017 – Dec 2022). The over-arching aim of TREND is to provide tools to assist engineers to work in a transdisciplinary manner and to identify the types of engineers that are transdisciplinary. Identifying what makes engineering teams in the automotive sector transdisciplinary and how to assess their current readiness level to be transdisciplinary is the focus of this PhD activity. The PhD will have a particular focus on ‘common’ characteristics and automotive design team behaviour within and across industry case studies. Mapping findings at various life cycle stages such as designer requirements, use of digital tools etc. for each case study/domain against the manufacturing life-cycle phases. This would be followed by cross case-study analysis. The analysis may use techniques such as input/output system modelling to map the designer requirements at each stage of the manufacturing life cycle, and/or socio-technical analysis could be used to classify and model the designer behaviour. In summary the PhD researcher will be required to create a structured framework to estimate the automotive sectors transdisciplinary readiness level. Specific objectives may include 1. Undertake a literature review to understand the state of TD working within the automotive sector. 2. Engage with stakeholders to gather information which informs the design of the TD readiness tool. 3. Create a TD readiness tool. 4. Validate the proof of concept tool within industry.

Structural batteries mechanical resilience:

Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying. The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:

Micromechanical scale - Mechanical resilience: During charging, ions are absorbed into the fibre (intercalation) which causes the anode to swell. Swelling impacts the residual stress state, mechanical properties and microstructure of the composite material, and may result in microscale fracture. Such physical changes will critically influence the ability of the material to hold charge and carry structural load. Work in this PhD will focus on use of synchrotron techniques to measure fibre scale mechanical properties of both anodes and cathodes during charge cycling, accumulation of microscale damage and understanding of ion intercalation patterns within the anode. Work will progress to understand similar properties under axial fatigue loading. A proposal for synchrotron time has already been made.

Virtual vehicle validation

  • Supervisors: Chris Brace
  • Industrial Partners: Quick Release
  • Start: October 2020

The automotive industry, along with other high-tech sectors, needs to move away from their reliance on physical prototypes. Today these prototypes are used to validate the performance of the vehicle against the design requirements. This is an expensive and time-consuming process but there are still many areas of real-world operation that are not possible to validate using traditional methods.

The vision is to move to a future state where there will be an increased reliance on virtual validation. The need is for better, faster and cheaper techniques that harness the power of advanced simulation and data analysis. The stakes are high – leaving things as they are is not an option, the industry needs a better way of working. But if the new tools proved to be inadequate the risks are huge, product recalls resulting from errors in vehicle validation could bankrupt a car maker.

There are 3 main facets to this type of validation, of which the first 2 are already areas of investigation, but the third is an essential enabler and often overlooked:

  1. The ability to use technology to virtually test the components, systems and vehicles in a manner that replicates physical testing
  2. The correlation of the virtual testing to the physical experiments to the level of fidelity that will be required for engineering sign off
  3. The novel digital processes that underpin these two objectives. These processes need to describe the way we build, validate and use the vast datasets that all the other steps rely on; the way we design and build the digital models we use; and the complex tests that we perform using these models.

This PhD opportunity will explore these topics in partnership with industry and will result in new techniques with significant potential to change the way that the industry works.

AI approaches to automate Bill of Materials Validation.

  • Supervisors: Chris Brace
  • Industrial Partners: Quick Release
  • Start: October 2020

A Bill of Materials (BoM) is a document that lists all of the components and resources needed to build a product, in this case a vehicle. Each car has around 15,000 components. If any of these components are missing or incompatible the factory cannot build the vehicle. The problem is made more complex because vehicle makers offer an almost infinite variety of model variations and customisation options. Each of these needs a complete and accurate BoM if the manufacturing process is to succeed. Therefore, each BoM must be validated to ensure it is correct before the vehicle can be built. Techniques exist to automate this validation process, but there is still a heavy reliance on expert knowledge to ensure that nothing is missed or duplicated.

Using AI techniques, it may be possible to understand the variant configuration of each buildable combination and thus eradicate miss-builds and provide vehicle makers with the good information across the whole product line-up which will allow for more accurate planning in terms of assembly as well as financial control. There is a rich dataset of historical BoMs available which can be used to help with this process, as well as access to human experts whose knowledge may able to be represented in an automated procedure. It is likely that the most effective approach will combine these two approaches.

Reduced order models for battery management systems

Lithium-ion batteries are prevalent sources of electric energy for a variety of applications, ranging from portable electronic devices like mobile phones, tablets and laptops to Electric Vehicles and Hybrid EVs. Compared to alternative energy storage technologies, Li-ion batteries have excellent energy-to-weight ratio, no memory effect and very low self-discharge rate in idle state. These favourable properties together with the continuously decreasing production costs have established Li-ion batteries as the unique contender for automotive as well as aviation applications. In the automotive sector, the increasing demand for EVs and HEVs is pushing manufacturers to the limits of contemporary automotive battery technology. These applications form a very challenging task since operating of EVs and HEVs demands large amounts of energy and power to ensure long range and high performance, whilst the battery cells must operate safely, reliably, and durably for a time scale of the order of a decade or more.

Typically, a battery pack for an electric vehicle consists of a large number of the battery cells, physical packaging (including bus bars, casing and connectors), and Battery Management System (BMS). A BMS is composed of hardware and software controlling the charging-discharging states, guaranteeing reliable and safe operation. The BMS also handles additional operations, such as cell balancing and thermal management of the pack. The design of a sophisticated BMS is necessary to ensure long life and high performance because battery behaviour varies in time. Additionally, the BMS is crucial for safe usage because Li-ion batteries may explode or ignite if overcharged.

The goal of this project is to develop mathematical techniques manifesting themselves through efficient numerical algorithms for the PDE/variational model that are able to inform the design of appropriate reduced complexity models that can be incorporated into the software of the Li-ion cell’s BMS, allowing the adjustment of operating conditions accordingly to ensure maximal calendar battery life, bypassing premature ageing and the dangers of thermal run-away.