Energy Efficiency Optimisation of Electric Vehicles In Dynamic Driving Scenarios PhD 36 months PHD Programme By Loughborough University |TopUniversities

Programme overview

Degree

PhD

Study Level

PHD

Study Mode

On Campus

Energy Efficiency Optimisation of Electric Vehicles In Dynamic Driving Scenarios PhD


To advance an era of green, clean, and affordable energy, this project will develop a novel efficiency-aware dymamic optimisation framework as a paradigm shift of the advanced electric drive control strategy to deliver promising properties like higher control bandwidth, lower current distortion and lower device switching frequency for electric machines.


With these achievable performance specifications, when applying to vehicle electric motors, this new technique will significantly improve the energy conversion efficiency, and provide much higher power/torque density and smoother speed/current/torque regulation performance, which will substantially increase battery life, enable wider range of driving scenarios, and enhance user comfort and vehicle durability by reducing unwanted noise, vibration, and harshness (NVH) of electric vehicles.


The post holder will have an opportunity to join a leading international team working on cutting edge research. They will have opportunities to collaborate with industrial partners. It is expected that the post holder shall have strong analytical skills and be interested in working on challenging theoretical problems in the engineering context.


The PhD will focus on advanced control theory on advanced control and optimisation theory (e.g., finite control set model predictive control, disturbance observer-based control, deep reinforcement learning control, integer programming and mixed integer programming) with focusing on application to direct electric drives for both permanent magnet machines and electric vehicles.

Programme overview

Degree

PhD

Study Level

PHD

Study Mode

On Campus

Energy Efficiency Optimisation of Electric Vehicles In Dynamic Driving Scenarios PhD


To advance an era of green, clean, and affordable energy, this project will develop a novel efficiency-aware dymamic optimisation framework as a paradigm shift of the advanced electric drive control strategy to deliver promising properties like higher control bandwidth, lower current distortion and lower device switching frequency for electric machines.


With these achievable performance specifications, when applying to vehicle electric motors, this new technique will significantly improve the energy conversion efficiency, and provide much higher power/torque density and smoother speed/current/torque regulation performance, which will substantially increase battery life, enable wider range of driving scenarios, and enhance user comfort and vehicle durability by reducing unwanted noise, vibration, and harshness (NVH) of electric vehicles.


The post holder will have an opportunity to join a leading international team working on cutting edge research. They will have opportunities to collaborate with industrial partners. It is expected that the post holder shall have strong analytical skills and be interested in working on challenging theoretical problems in the engineering context.


The PhD will focus on advanced control theory on advanced control and optimisation theory (e.g., finite control set model predictive control, disturbance observer-based control, deep reinforcement learning control, integer programming and mixed integer programming) with focusing on application to direct electric drives for both permanent magnet machines and electric vehicles.

Admission Requirements

3.2+
6.5+
92+
A 2:1 honours degree (or equivalent international qualification) in engineering, mathematics or science.

12 Feb 2025
3 Years
Oct

International
28,600

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