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Funded PhD Opportunity

Benchmark Simulation Model for An Electric Vehicle — Strategy Evaluation for Control, State Estimation, and Fault Diagnosis

Subject: Engineering


Summary

Problem Statement

The automotive industry is moving towards electrification and autonomous driving. The use of standalone internal combustion engines (ICE) is slowly but surely giving way to hybrid drivetrains or pure electric systems. However, most of the reported works found in the literature only focus on the modelling, control, and management of the battery system in hybrid and electric vehicles. There is a lack of coverage on the modelling and control of the electric drivetrains. Studies performed on the electric drivetrains will allow researchers, engineers, and automotive manufacturers to understand the systems better. As a result, better controller, estimation, and fault diagnosis schemes can be designed to enhance performance, safety, and reliability.

Proposed Innovative Solution

The first aim of this project is to design and develop a benchmark model of the electric drivetrains in MATLAB/Simulink. This benchmark model will be used as the base Model-In-The-Loop (MIL) testing platform for controller design and other studies on the electric drivetrains. This benchmark model can also be published and distributed as an open-source software to be used by the research community. This in turn will generate healthy citations to this research at Ulster and potentially open up new opportunities for research collaborations with other institutions or even the automotive industry.

The project will then proceed to design a controller such that the drivetrains system is able to follow the reference from industry-standard driving cycle profiles. A driving cycle profile provides information on the desired speed of travel of the vehicle to mimic the different driving conditions in the real world, e.g. urban, extra-urban, motorway, etc. In general studies, driving cycle profiles are used on ICE vehicles to measure fuel efficiency as well as the engine power output during operation. When applied to electric vehicles, the driving cycle profiles can be used to measure the efficiency of power delivery to the electric motor whilst cater to different driving conditions as well as the range specifications, e.g. how far can the batteries allow the vehicle to travel before they need to be charged? This study will also allow for more understanding on the effectiveness of the energy recovered via the regenerative braking system.

This project will then study the design of advanced fault diagnosis schemes to detect anomalies in the electrical signals or failures in the hardware of the system. Once detected, these anomalies and failures have to be located promptly such that the necessary corrective measures can be taken to enhance safety and reliability.

The fault diagnosis schemes can be designed using classical model-based methods and/or modern machine learning and artificial intelligence techniques. Having efficient and effective fault diagnosis schemes will prove beneficial, especially for autonomous vehicles where the vehicles need to be aware of their ‘health’. Knowing what went wrong can enable the vehicles to reprogram themselves to operate at lower performance capacities until they are brought into the workshop for repair and maintenance.


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)

Funding

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 18 February 2019

Interview Date
March 2019


Applying

Apply Online  


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Dr Mark Ng


Other supervisors

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Subject: Engineering

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