Our Projects

Immanuel's project investigates a pathway to making water injection for combustion engines mass market proof. Water injection for combustion engines has been implemented a number of times into limited production motor vehicles to enable higher engine performance, mainly with forced induction. In these cases, the technology was used to lift the knock limit by decreasing combustion temperatures. However, water injection enables better thermal efficiency with lower combustion temperatures which can decrease particulate, CO, CO2 and HC emissions together with fuel consumption. Nowadays, a large portion of engines utilise forced induction which at certain times requires extra cooling where the engine is made to run rich. Having lower combustion temperatures removes this need. Furthermore, as mentioned, with lower combustion temperatures, engines could run higher compression ratios which would make engines smaller, hence reducing rotational masses and improving fuel consumption. These potential benefits reflect the current drive toward more efficient and cleaner combustion engines. Electrification is one of the pathways being implemented to fight global warming but combustion engines are set to be part of the drivetrains available in the next few decades. One of the reasons why water injection has not made its way into mass production vehicles is the need for the consumer to refill the water tank after relatively short intervals with distilled water from the grocery store. This is impractical and not acceptable for consumer satisfaction. This project aims to produce a solution where the water vapour in the exhaust gas is condensed to liquid form, cleaned and stored to then be injected back into the engine to result in a closed cycle. There are several issues that need to be addressed when proposing such a solution. Among those are water pH values, moulding, freezing and impurities within the condensed water.

Through the sponsoring company, a natural non-toxic additive is in development and may be used for the purpose of the project to potentially eliminate some of the issues with closed cycle water injection. The proposed solution should then be capable to run several thousand kilometres without a refill of the additive, similar to the AdBlue principle in diesel engines. Depending on the progress of the research, a prototype of the whole system is possible where a side effect of the water injection may be that the currently imposed GPF filter for gasoline engines could be removed due to water injection reducing the particulate emissions. This would be a positive side effect since less aftertreatment would result in a weight and efficiency benefit.

Jac's PhD research will deliver a green bond designed to finance low-carbon bus operations. This document will provide asset specifications (e.g., issue price, face value, coupon rate, and an expected credit rating), and evidence of alignment to the ICMA green bond principles and UK taxonomy. To achieve this aim, we will investigate the extra cost and default risk associated with fully electrified fleets. By looking at costs from a fleet perspective, we include the monetary impact of electric vehicle range and charging requirements on vehicle scheduling.

​​Hydrogen fuel cell vehicles are a potential technology to help alleviate climate change, providing reduced harmful emissions than petrol and diesel cars. In their fuel cell stack, hydrogen gas is reacted with oxygen to produce electricity and water. The electricity produced goes on to power electric motors, as in battery electric vehicles, but hydrogen vehicles have the benefit of being able to be refilled with hydrogen far more quickly than an EV can be charged. The water produced simply exits the exhaust pipe as steam.

Hydrogen has emerged as an alternative to traditional fossil fuels for powering internal combustion engines as it is carbon neutral, can be produced in a sustainable way and has high energy density. As such, hydrogen engines offer a direct replacement of fossil fueled ​engines with additional benefits coming from the intrinsic properties of hydrogen - these include the ability to operate at ultra-lean conditions with high efficiency and low emissions. However, one of the key factors to unlocking further efficiency gains while retaining the safe and reliable operation of the engine remains the understanding of the autoignition process of hydrogen-air mixtures and how the autoignition is affected by the engine's intake air composition.

A Bill of Materials (BoM) is a structured document that contains the information on all components and resources needed to build a product.

The validation of the BoM is an essential process performed to establish the accuracy and completeness of product information. This document acts as a vital source of truth not only to determine the correct product composition, but also for multiple business operations within a manufacturer that rely on this information, such as inventory management and servicing.

The complexity of this task is dependent on the quantity and quality of items and information recorded in the BoM. This can be extensive considering the potential product variations and customisation options available to the customer which determine the extent of unique combinations to be included in the validation.

The validation process requires experts with knowledge of the product design (the constituent components and systems, their procurement and interaction within their respective assemblies) to review each item in the BoM for approval or correction. Computational tools that support this validation process exist, such as rule-based systems, although there is still a heavy reliance on the resource of product knowledge experts to audit the BoM.

One technique which has not be explored extensively is the application of artificial intelligence (AI) to improve the efficiency of the process. AI provides the advantage of being able to assist with, augment or autonomously perform data analysis tasks to support with BoM validation.

The implementation of AI to the validation of a Bill of Materials could provide a means to faster and accurate identification of errors present in a wide range of vehicle BoMs, with less human expert resource required and less need to define and manage specific rules to identify all possible issues for multiple possible combinations. This could more effectively find errors resulting in less chance of miss-builds or undesirable extra strain on resources, and provide manufacturers with more confidence in BoM information when planning, designing, manufacturing and managing products.

Aims and objectives:

The aim of the research is evaluate the capability of AI methods at improving the efficiency of BoM validation. To meet this aim the following objectives will be set:

• To understand the range of BoM validation practices, and existing systems that are utilised to support the process in industry
• To perform a literature study on the current research surrounding BoM validation and AI methods to validate large datasets
• To define the required knowledge and methods to make decisions during validation from the information obtained
• To experiment with AI methods to support/ automate an existing validation process to understand potential impact

Potential applications and benefits:

The resulting research can potentially inform the development of more intelligent systems to perform the BoM validation process more efficiently, in terms of reduced resource allocation (e.g. time, human effort, and financial resources). This will provide additional benefits:

• Reduced risk of miss builds or stops to production from incorrect part delivery to the production line.
• Reduced waste of unnecessary resources and effort for storing, procuring, and scrapping parts which were not required to build the product.
• Improved confidence in data driven manufacturing processes and planning, through increased ability to validate the full range of product BoM’s and reduced risks of error

Relevance to the EPSRC research council:

James's topic of study aligns with the EPSRC’s interests and investment in the two research areas of AI and manufacturing technologies. James's work will contribute to the outcome of research towards developing intelligent systems that will address an important challenge in manufacturing.

Elisabetta's PhD proposes the use of solid oxide fuel cells (SOFCs) for the direct conversion of hydrogen storage vectors such as ammonia to electrical energy. SOFCs have several advantages over PEM, including, multi-fuel capability, resilience to poisoning from fuel impurities and lower use of precious metal catalysts.  These systems, however, require a “defects and flaws” control on the structural and functional properties of their ceramic electrolytes because of their high variability of the strength, and their relatively low toughness, which are some of the point of interests of this research project.  

Chemical molecules such as ammonia have the potential to be excellent hydrogen storage vectors for aviation fuels. They do not require high pressure containment but still achieve very high hydrogen storage densities arising from the hydrogen stored within their chemical structure. Ammonia is also good at conducting and absorbing heat, making it good for energy acquisition while also avoiding the formation of “coke” and other residues that hydrocarbons leave behind under extreme temperatures. This substance is also much easier to store as a liquid because, under this form, it only needs to be kept at  -33 degC, which is very close to the temperature at cruising altitude.  The release of the hydrogen, however, requires a catalytic conversion and ppm levels of ammonia are a poison for PEM fuel cells, and for this reason, a SOFC is required.

The project proposes the (1) characterisation and (2) optimisation of SOFCs for usage in aerospace electric propulsion applications. Characterisation of the cells will focus on cycle efficiency of different fuels (Ammonia, hydrocarbons, H2), the internal chemistry/catalysers used and their behaviour under certain operating conditions. Optimisation will be on structural integrity of electrolytes and fuel cell weight reduction, power transfer efficiency, and possible thermal management of waste heat.

Physical testing of new vehicle components is time consuming and complex, and is thus, highly energy intensive. Additionally, many of these tests develop faults which are only discovered after the test-run, and subsequently need to be repeated. Virtual testing can reduce the need for physical tests and reduce energy consumption, however, these models rely on physical testing for high quality test data to adapt and optimise the virtual models. Some physical testing will also still be required prior to market release to ensure customer safety. Ellie's project aims to improve the quality of data from physical testing, using machine learning and human factors analysis, whilst minimising wasted energy consumption.

Anomaly prevention – a term coined to complement anomaly detection - describes the combined methodology of human factors analysis and systems mapping; this was devised after testing procedure and human factors were identified as significant contributors to data quality. Anomaly prevention will identify the processes outside of the test bed that negatively impact data quality and suggest mitigations. Anomaly detection – a method of finding unexpected patterns or points in data via statistical or machine learning techniques - presents a solution to detect anomalies in real time during testing, thus, minimising physical testbed time whilst increasing the reliability of data to feed into virtual models. Together, these methods have the potential to reduce the energy intensity of testing and development, whilst simultaneously increasing the speed at which low-carbon technologies can be released into the public domain to aid large scale decarbonisation of the transport sector.

Abdelrahman's PhD will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system. 

The context of research;

In machine learning, regression is often considered a black box methodology used commonly for identification of suitable functions from a hypothesis. Its primary aim is to estimate a predicted function which leads to minimal expected error on future data, and not necessarily to benefit the overall understanding of the derived output-input relationship governed by the physical interpretation of the system. Consequently, such modelling applications are often highly constrained, allowing training solely for thinly dispersed linear models. To this day, the task of forming intelligent algorithms capable of nonlinear interpretations through physical system observation has received very little consideration, it nevertheless forms the foundation of the field of system identification. Typically, approaches for nonlinear system identification include autoregressively modelling time evolution or utilising Volterra series multidimensional convolution integrals. This project will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system through supervised neural network-based machine learning algorithms for system optimization. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, enabling major efficiency and power improvements for future powertrains. It will enable the full utilization of Freevalve’s potential and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.

Electric machines are becoming more prevalent in the automotive industry as they become the main propulsion system in road vehicles with the industry’s shift towards emissions free mobility. With over 15% of new car sales being electric, being able to accurately characterise electric machines virtually is imperative for maximising their performance and efficiency. A key predictor of a model’s ability to replicate transient behaviour is the accuracy of the parameters used to characterise the motor.

Relying solely on the information and specifications provided by the manufacturer to create a robust model is impractical as they often only include information required for the machine’s operation. The overarching aim of this work is to develop a procedure to automate the parameterisation of electric motor models for later use in the vehicle development process.