PhD Studentship: Multiscale Scientific Machine Learning Digital Twins for High-Torque Electric Propulsion under Extreme Duty Cycles

Electrifying heavy-duty maritime vessels, mining equipment and off-highway machinery is central to Net Zero and Clean Energy goals. Unlike conventional automotive traction systems, electric propulsion for these applications must often deliver sustained high torque at low rotational speed during demanding operations such as bollard pull, winching and heavy excavation. Under these extreme duty cycles, motor behaviour is governed by processes that evolve over very different timescales: fast electromagnetic dynamics...

Opportunity details

Electrifying heavy-duty maritime vessels, mining equipment and off-highway machinery is central to Net Zero and Clean Energy goals. Unlike conventional automotive traction systems, electric propulsion for these applications must often deliver sustained high torque at low rotational speed during demanding operations such as bollard pull, winching and heavy excavation. Under these extreme duty cycles, motor behaviour is governed by processes that evolve over very different timescales: fast electromagnetic dynamics determine torque response and saturation behaviour, electromechanical and load-history effects shape transient performance, and slow thermal dynamics govern the gradual build-up and dissipation of heat over extended operating periods. These processes are strongly coupled, since changes in temperature alter electrical resistance, efficiency and torque capability, making the overall behaviour difficult to predict with physics-based models or conventional machine learning alone. Characterising these operating regimes experimentally is genuinely difficult. Testing high-power motors repeatedly to their thermal limits is energy-intensive, costly and potentially damaging, with risks including insulation degradation, excessive winding temperatures and permanent-magnet demagnetisation. Detailed physics-based simulations can be too slow to run for long, variable duty cycles, while conventional machine-learning models often generalise poorly beyond the conditions they were trained on, particularly for long-horizon thermal behaviour. This project will develop a multiscale Scientific Machine Learning (SciML) digital-twin framework predicting thermal trajectories, torque capability and efficiency variation in low speed, high-torque propulsion systems, investigating physics...

Eligibility

The project welcomes applicants from electrical/mechanical engineering, control systems, applied mathematics or computer science, and offers interdisciplinary training in electric machines, SciML, thermal modelling and digital-twin validation.

Requirements

The project welcomes applicants from electrical/mechanical engineering, control systems, applied mathematics or computer science, and offers interdisciplinary training in electric machines, SciML, thermal modelling and digital-twin validation.

Funding and benefits

Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..

How to apply

Unlike conventional automotive traction systems, electric propulsion for these applications must often deliver sustained high torque at low rotational speed during demanding operations such as bollard pull, winching and heavy excavation.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.