Deep learning for energy-efficient running of battery-electric trucks – Funded
This PhD will use advanced control techniques in tandem with state-of-the-art deep learning methods to develop a controller for battery-electric trucks. As we move toward electrification of transport to combat climate change, we will increasingly rely on electrified heavy goods vehicles. These will likely run on electric roads for much of the time, and use their batteries to travel into city centres or distribution centres. How these vehicles prioritise their energy usage is important in determining how far they can travel on one charge. Apply here.
Adaptive learning control for torque ripple minimisation of electric machines
Torque ripple is the unwanted oscillation of torque about a setpoint that affects the efficiency and controllability of electric machines. This mechatronics PhD will build on work done under Loughborough’s ViVID project. You will use state-of-the-art adaptive control techniques to reduce the torque ripple present in the output of traditional electric motors. This will involve real-time implementation of complex control methodologies, possibly including deep learning or artificial intelligence.
Intelligent control for energy-efficient battery-electric trains
Electrification is the future of net-zero rail transport. Therefore, it is important to increase the energy efficiency of trains to reduce any wasted energy that could be used elsewhere. In this PhD, you will investigate the application of modern control techniques to this problem. Then, we will apply these techniques to computer models and determine how much energy can be saved by using advanced control.
Exploring electric vehicle reliability using network theory
As electric vehicles become more commonplace, the reliability of their components will become more important. Reducing critical failures will reduce the likelihood of stranded vehicles. This PhD will use advanced network theory (building on advances in matroid theory and network theory) to analyse the reliability of components within electric vehicles. Then, we will apply this theory to determine how we can make electric vehicles more reliable, and reduce the likelihood of them stranding drivers and passengers.