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.

​​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.

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.

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.

The main aim of the research conducted in Vicentiu's project is to develop an effective system parametrisation process targeting Li-ion battery models and having as a prime example the Equivalent Circuit Model structure referred to as MoBat and proposed by the industrial sponsor of the project, AVL.

The first segment of the project targets an automatic extraction process of information from random measurements (test bed, drive cycle) leading to model parameter values and encompasses a predefined set of instructions iteratively improving the result of the parametrisation and quantifying the accuracy of the model. 

A second research element investigates strategies aiming to reduce system testing efforts by using a prior model of a battery system. In addition to the research conducted using MoBat, the project will also consider the parametrisation of similar models proposed for the virtualisation of the same or different systems, targeting the reconfiguration and transfer of the instructions part of the parametrisation procedure.

Finally, the dissemination of project results is set to be achieved by the PhD thesis, while the impact of the research will be amplified through close collaboration with the development team integrating the results of the project into commercial software - ModelFactory.

Lukas's PhD aims to develop a systematic approach for product-description driven system model quality assessment and testcase generation, to enhance Model-based validation and verification (V&V) activities throughout the development process of electrified powertrains. One modelling language capable of describing these aspects is Systems Modelling Language (SysML).  

In its most rigorous usage mode, SysML-as-Executable-System-Architecture, SysML can be used to develop an executable system architecture making majority of parametric and behavioural specifications of a System Architecture Model (SAM) simulatable and executable. This allows for partially of fully automated generation of system interfaces and system test cases and other artifacts important for the system verification and validation directly from the SAM across various domains and development phases. 

The objectives to deliver this research project are: 

1. To review current V&V practices used at AVL throughout product development to understand their specific requirements (Phase 1)

   • Different modelling approaches exist to describe and model systems using modelling languages such as SysML. As a first phase of this project, it is therefore important, to clearly identify the business’s needs that would best suit the additional demand of the industrial environment and integrate within existing software and methodologies that are being developed in parallel in the organisation to accomplish the full potential of its application.  

2. To develop a systematic approach to a product-description driven SysML model quality assessment to understand the model’s maturity and identify available artefacts based on requirements obtained from obj. a (Phase 2)

   • In order for the SAM to be sufficiently precise and complete to serve as the truth system architecture blueprint for all engineering disciplines and processes involved in the system it must be correct, complete, clear, concise and consistent (Five ‘C’s). Analysis of the necessary level of information within the system model to allow automated generation of required artifacts such as function lists, interface matrix, FMEA & Safety Analysis inputs, testbed interface and configuration information. The second phase of this project will therefore focus on model quality assessment that will result in static model analysis to identify the level of information available and provide further modelling guidelines.  

3. To develop an automated test generation process for available artefacts (from obj. b) to obtain executable test-program (Phase 3)

   • The interactions between operational, functional, structural, behavioural and communication aspects of the system must be modelled in detail to develop and generate a sufficient test program. The third phase of this research project will therefore focus on capturing the relational aspects of the available SAM to analyse to which extent the test program (including all required information such as test scenario, test case, pre-conditions, post-conditions, test data and expected results) can be generated automatically and develop a software module (addon). This will serve as a proof of concept for automated test case and test artifact generation for system functional verification. 

4. To implement a development-role specific model administration access to present subject matter experts with appropriate information (Phase 4)

   • Achieving the Five ‘C’s quality as described in Phase 2 of the project plane is important to ensure sufficient level of SAM maturity to enable partially or fully automated processes. It is therefore necessary to efficiently manage the human-model interface and allow the appropriate engineer/team to effectively contribute to the model development. The fourth phase of this project will therefore focus on the implementation of a domain/role specific access management for various stakeholders.  

5. To identify system and process boundaries and interfaces of the methods developed in obj. b - obj. d to integrate within existing PLM architecture (Phase 5)  

   • As depicted in the first phase of this project plan, the usage and application of MBSE and SysML varies depending on the specific requirements and demands of the industry and the business’s needs. The fifth phase of this project will therefore focus on identification of system and process boundaries of the project outcome within the existing PLM architecture and systems processes.

Antiwear and reduced friction agents are a class of engine oil additives used to both reduce self-inflicted damage from metal-metal contact inside internal combustion engines, as well as acting as friction modifiers, which serves to improve engine efficiency. Zinc dialkyl dithiophosphates are one of the leading materials used as such agents. However, despite their effectiveness, they are known to contaminate catalytic converters - a problematic issue which has led to significant research into finding replacements.

Although the electrification of the transport industry has already started, tribology and the design and formulation of antiwear and antifriction additives play an important role in the optimisation of efficiency of every mechanical device. Extensive use of zinc dialkyldithiophosphates and other materials such as molybdenum disulphide (MoS2) as antiwear and lubricious materials are present across many applications that involve devices with moving mechanical components.

The vision of the EPSRC is to advance the knowledge and technology of scientists to tackle several key areas one of which is climate change. The development of novel lubricious materials aids in the reduction of carbon, not only lowering the effects of climate change but conserving the current environment.