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Given the current concerns regarding the environment and global warming, reducing the use of fossil fuels and replacing them with renewable energy sources is becoming increasingly important. Government’s ambition is that nearly all cars and vans on our roads are zero emission by 2035 supported by “Automated and Electric Vehicles Act, 2018”. Electric vehicles are expected to play a dominant role in decarbonising the transport sector. Electric vehicles have a number of limitations which make their adoption challenging. The greatest of these is the fact that these vehicles have limited driving range meaning that they must be recharged frequently where this recharging can require a significant amount of time.
In this project we will develop novel methods for optimizing the routes taken by electrical vehicles toward minimizing detours required for recharging and the corresponding delays caused by this. Delays caused by recharging can be minimized by aligning these events as best possible with existing pauses in the transportation process. For example, if an electrical vehicle carrying goods needs to be reloaded, it may be recharged while this reloading is taking place. The methods developed in this project will be general in nature but for the purposes of this project we will focus on optimizing the transportation logistics of medium to large businesses and organizations. Transportation is usually a significant part of the cost of a product and therefore it is important that it is optimized to support the adoption of electrical vehicles.
Optimizing the above electrical vehicle routing problem is provably extremely hard making it difficult to solve exactly. Therefore, in most cases one can only hope to find a relatively good solution through the use of heuristic optimization methods. In this context, a heuristic optimization method is an optimization method which does not provably always perform well but empirically performs well in many cases. Traditionally such optimization methods are manually designed using a combination of domain knowledge and experimentation. In this work we will use machine learning methods which use large volumes of data to learn useful heuristic optimization methods. This approach is motivated by recent applications of machine learning to related optimization problems which have shown to achieve state of the art results.
Keywords: Electrical Vehicles, Vehicle Routing, Operations Research, Machine Learning, Optimization.
Dr P Corcoran
Dr A Gagarin
Prof L Cipcigan
Early-stage aircraft structural design must explore a large design space to ensure optimal aerodynamic and load bearing performance. This is especially important as we enter an age of novel aerostructures with enhanced capabilities such as morphing wings. At this stage (as opposed to the subsequent detailed design stage), designers must also cope with uncertainties that exist due to lack of design maturity, knowledge about aerodynamic loads and model predictions. The PhD project aims to use machine learning to develop a data-driven robust aircraft wing design toolbox in collaboration with Airbus. Machine learning is seeing a rapid uptake in manufacture and design due to its ability to predict optimal design/operational conditions given countless possibilities and covariates. Machine learning will be a key enabler for the project to properly trade optimum performance against the risks of achieving the performance and meeting the constraints. A Bayesian machine learning approach will be used within a data-driven framework for robust design and optimization of aircraft structures under a set of stringent design constraints imposed by considerations of weight penalty, flexibility in flight conditions, flight envelopes and risk minimization
The student will benefit from an excellent research training environment provided by the Applied and Computational Mechanics group at Cardiff University’s School of Engineering and also by Airbus. Airbus is a world leader in wing design, engineering and manufacturing and is a key centre for the design, testing and integration of fuel systems and landing gear. Airbus will host and train the student at their research facility in Filton (Bristol) for about 15% of the time of the PhD studentship. The student will also receive training in the use of high performance computing facilities which will provided at both Cardiff and Airbus.
Keywords: Aircraft structural design, optimisation, uncertainty, Bayesian machine learning.
Dr A Kundu
Prof D Kennedy
It is estimated that in 2018, total UK greenhouse gas emissions were 43.5 per cent lower than in 1990 and 2.5 per cent lower than 2017. Electric vehicles (EVs) are an important part of meeting global goals on reducing CO2 emission. In order to achieve UK’s target - to reduce emissions by 80% by 2050, more research and innovation can be done to prominently facilitate the development of EVs.
This PhD research project aims to collaborate with IPFT Fuels Limited to build a prototype of its conductive EV charger at the Power Electronics (PE) Laboratory at Cardiff University (CU) so as to accelerate the industrial application of EV charging devices in the market. The project aims at addressing the dilemma of cost vs. size, weight, efficiency and reliability based on cost and performance models of the EV charges. The project will validate the technology component and/or basic subsystem of IPFT’s product in the PE Lab. The project will achieve the Technology Readiness Level 5 (TRL 5) required by the Automotive Council and therefore, enable the technology for vehicle-to-grid (V2G) and Mobility as a Service (MaaS).
The successful applicant will be co-supervised by Prof. Jun Liang and Prof. Liana Cipcigan, who have extensive experience in relevant research areas and postgraduate student supervision. The PhD candidate will obtain in-depth knowledge and cross-platform training from the research teams at CU and IPFT. The PhD candidate will have the access to the devices in the PE Lab, such as the wireless EV charging facilities, DC power source, power system emulator and real-time digital simulator. The PhD candidate will design and integrate technological components of the charger with these supporting elements from IPFT so that all system specifications can be simulated and validated within the PE lab at CU. Training on scientific knowledge and skills, as well as on complementary personal and transferable skills (such as technical report and academic paper writing and research project management) will be provided. The research student will undertake at least a three-month secondment provided at IPFT to carry out research and experimental work and will also work with researchers from University of Surrey who have collaborated with IPFT for the technical study of IPFT’s EV charger.
Keywords: Electric Vehicles, Conductive Charging.
Dr J Liang
Prof L Cipcigan