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

This opportunity is now closed.

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

    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.

    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 fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.

Other information

The Doctoral College at Ulster University

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Key Dates

Submission Deadline
Monday 18 February 2019
Interview Date
March 2019

Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses

Contact Supervisor

Dr Mark Ng

Other Supervisors

Apply online

Visit https://www.ulster.ac.uk/applyonline and quote reference number #344619 when applying for this PhD opportunity