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ÉñÂí¸£ÀûӰƬ – Collaborative EPSRC Doctoral Landscape Award

Qualification: Doctor of Philosophy in Engineering (PhD)

Eligibility: UK Students

Award value: Home fees and tax-free stipend, plus £5,000 RTSG. See advert for details

Supervisors: Dr Oleh Kiselychnyk and Prof Jihong Wang

Deadline: 31 August 2026

Clamping Force Estimation and Nonlinear Optimal Robust Control for Electromechanical Braking of Electric Vehicles:

One of the current challenges in electric vehicle (EV) technology is the development of electromechanical braking (EMB) systems to replace conventional hydraulic brakes and to integrate them with primary regenerative braking. EMBs are highly sensitive to EV harsh operating conditions. This project addresses this challenge by developing AI-based EMB clamping force estimators and robust control algorithms, validated in real-time simulations and experimentally in collaboration with industry

The project:

This project looks at the development of electromechanical friction wheel braking (EMB) systems to replace existing hydraulic brakes. This reduces weight, maintenance costs and environmental pollution. Critically important, this enables the development of faster friction (secondary) braking systems that meet Level 5 EV automation requirements, including 100 ms response time and 0.1 MPa control accuracy, for efficient integration with regenerative (primary) braking via propulsion motors, in accordance with Automotive Safety Integrity Level D.

The EMB consists of an electric motor, a power electronic converter, a gear and a motion conversion mechanism to press the brake pads against the wheel disc and to generate the clamping force (CF) to be controlled. Onboard CF direct measurement is impossible due to space limitations, vibrations, and temperatures reaching 500 degrees Celsius. CF tracking control includes three stages: clearance elimination, clamping and release. In industry, it is implemented via three-loop CF cascaded control with internal motor velocity and current loops. Current research gaps in EMB systems include improving modelling accuracy by accounting for harsh operating conditions, enhancing reliability, maximising efficiency, and developing self-commissioning algorithms.

The project aims to address these gaps by developing:
• Robust and fault-tolerant CF estimator overperforming Level 5 EV automation expectations under EMB parameters variations and sensors malfunctions.
• Robust and efficient CF control with Level 5 EV automation tracking accuracy under harsh operating conditions, and automated identification algorithms for self-commissioning.

Physics-informed machine learning will be used for the estimator development. The EMB will be based on a permanent magnet synchronous motor (PMSM). A corresponding model accounting for harsh operating modes will be established and used for the design of the robust CF control. Validation will include offline MATLAB simulations, Model-in-the-Loop (MiL) real-time simulations, Hardware-in-the-Loop (HiL) real-time simulations, test bench experiments, EV onboard verification, and comparison with the best conventional approaches.

The project will be done in collaboration with an international automotive manufacturer which has its R&D Centre located in the UK. There will be two industrial supervisors. They will ensure that the estimation and control algorithms comply with automotive standards and are compatible with automotive protocols. They will lead EMB test bench development and provide the necessary training to the PhD student. The HiL simulations will also be conducted on the dSpace platform and the company’s electronic control unit.

Scholarship:

The award will cover the UK tuition fee level, plus a tax-free stipend, currently £21,805, paid at the prevailing UKRI rate for 3.5 years of full-time study. This award also includes a £5,000 research training support grant.

Eligibility:

This studentship is available to home students only.

Home students are eligible to apply. The candidate should have a 2.1 or 1st Bachelors or Masters degree in: Electrical or Electromechanical or Electronic Engineering, Mechatronics, Control, Mechanical or Automotive Engineering, Computer Science, Applied Mathematics. Applicants with a strong interest in automotive electromechanical systems from other related Engineering disciplines/ Physics are also encouraged to apply.

How to apply:

Candidates should submit an expression of interest by sending a CV and supporting statement outlining their skills and interests in this research area to . If this initial application is successful, we will invite you to submit a formal application.

Candidates must fulfil the ÉñÂí¸£ÀûӰƬ entry criteria and obtain an unconditional offer before commencing enrolment.

Should your application for admission be accepted, you should be aware that notification of acceptance for the PhD does not constitute an offer of financial support. Successful scholarship candidates will receive an official communication from the School of Engineering to confirm their award.