I am always open to discuss novel project ideas which is related to my research interests and projects. My proposals for PhD projects are as follows:
Project: Condition monitoring system for electric vehicles battery packs using current density images of Li-ion pouch cells
Description: Improving the performance and lifetime of electric vehicles Lithium-ion battery packs (LiBs) is still a challenging problem. This will be achieved by obtaining a more accurate state-of-charge (SoC) estimation and reliable temperature monitoring. In this project a multi-physics sensor array will be developed using disruptive magnetometer sensors for the online monitoring of the in-situ current flow within individual Li-ion pouch cells. High-resolution sensor images are desired in real-time for the battery management systems (BMS) and conditional monitoring system (CMS) to allow higher life-cycle with instant cell-equalisation, predict thermal runaway, estimate accurate SoC/SoH measurement, avoid long-term degradation, improve the energy density, stabilise the power density and reduce the overall cost of the battery pack. This is a multi-disciplinary and industrial-based project. Please look at my projects and labs pages for further details.
Required background knowledge: Battery Management Systems, Battery modelling, Power electronics, Sensor systems, Signal Processing, Matlab/Simulink, Real-time simulation, embedded programming.
Project: Multi physics sensor array for Silicon Carbide (SiC)-based power electronic converter prognostics in electric vehicles
Description: Power electronic converters (PECs) underpins modern electric and hybrid vehicles allowing efficient energy transfer between the vehicle battery system and the drive motors. To simplify construction, reduce costs and increase reliability, manufacturers are seeking ever-tighter system integration. However, this level of integration poses a number of significant challenges including interlinked heat transfer paths, bond wire lift-off and unwanted thermal stresses. In this projects, we investigate a multi-physics sensor fusion technique to provide accurate prognostics for highly integrated SiC-based PECs. An intelligent vehicle management system will be implemented to adjust the available power and cooling requirements based on the real-time estimation of the true age and safe operating area of the PEC based on the online conditions and records of previous ageing. A sensing platform, i.e. an array of multi physical sensors, needs to be developed to quantify the health status of power electronic modules by generating an image of measurements which consist of temperature, electrical quantities (V, I, Z) and mechanical displacement (wire bond movement/device deformity) on the power module. This is a multi-disciplinary and industrial-based project. Please look at my projects and labs pages for further details.
Required background knowledge: Power electronics, Sensor systems, Signal Processing, power system toolbox in Matlab/Simulink, Real-time simulation.
Project: Energy storage system interface for Vehicle-to-grid (V2G) application
Description: The use of Electrical Vehicle (EV) battery pack to supply power to the grid (V2G) increases reliability and consistency in the grid as the renewable source, e.g. wind, solar, undergoes its natural fluctuations. Furthermore, power quality can be increased with having battery storage for charging and discharging electricity to the grid. V2G operation is generally using power electronic converters (dc-dc & VSC) and inverters to act as a bidirectional charger capable of charging and discharging the battery on demand while complying with grid standards. Commercial bidirectional chargers typically use conventional 2‐level silicon-based PWM converter topologies able to switch at relatively low frequencies. As a result, compared to the size of the battery or EV, they are relatively bulky and suffering from significant power losses. This project is focused on modelling and designing more efficient power converters to reduce the size of bidirectional chargers and reduce the power losses. This is investigated via developing novel converter topologies and control strategies for the rapid response (low latency with high switching frequency) to the grid demand.
Required background knowledge: Power electronics, Battery modelling, power system toolbox in Matlab/Simulink, Real-time simulation.
Project: Improving the life-cycle of Electrical Vehicle Battery Packs using wireless sensors or antenna networks
Description: Predicting the life cycle of Lithium-ion battery pack system in battery electrical vehicles (BEVs) is still a challenging problem for a vehicle manufacturers. With the current technology, nonlinear behaviour estimation and life cycle prediction of a single cell in real-time is not possible due to computational burden of the complex electrochemical models of batteries and the simplicity of the current equivalent circuit models (ECMs) suitable for control and online monitoring. The interconnection of cells in the pack makes such estimation even more difficult as the electrical dynamics and thermal characteristics of individual cells are different. This may introduce random variability and the fact that ageing of a single cell can propagate and reduce the life of the whole battery pack which is known as ageing propagation. This project investigates a hardware/software solution using online (wireless) sensory system and advanced battery models to provide critical battery parameters, i.e. temperature, internal resistance, degradation, to the battery management system (BMS), and enhance the life-cycle of the battery packs in electric vehicles.
Required background knowledge: Battery modelling, Electro-chemistry, Wireless sensors, Antenna communication, Matlab/Simulink, Real-time simulation.
Project: Wireless energy measurement in home environment
Description: Low-cost monitoring of energy usage and providing energy efficiency by a home energy management system (HEMS) is still a challenging problem considering the rise of household energy cost. In this direction, designing and implementing a wireless network so that energy consumption of home appliances becomes minimum is by far difficult. Algorithms like situational awareness (SA) can be employed for the real-time scheduling, power distribution, and automation of wireless sensor network (WSNs) of home appliances/renewables. Such algorithms can provide a vision of the network events before the event occur in a distributed fashion.
The candidates interested in this project can narrow down their project in one (or more) of the following areas:
– Classifying the pattern of real power usage (RMS) of individual home appliances in real-time using machine-learning and data-driven approach.
– A machine learning (ML) system to learn the behaviour of home users using energy usage pattern data
– An intelligent Agent as a part of SA-based HEMS system to make decisions instead of human’s switching on/off devices and control the home renewable sources (wind/solar + batteries)
– Design of sensor/Antenna/actuator network to sense the environment (Devices, Human, Renewables, Batteries) and send the commands to the environment (actuators)
– Communication protocols design of SA-based system architecture
– Developing security algorithms for the SA-based HEMS using Game theory
– Developing a comprehensive (MAC layer, Physical layer) design for the Ad-hoc network
– Solving the Routing problems in the communication platform of SA-based HEMS
– Developing Optimal Control/Adaptive Dynamic Programming (ADP) approaches for optimising the WSNs used for HEMS
Required background knowledge: The knowledge/skills needed depends on the narrowed-down route the candidates intend to choose, and it can be discussed upon request.
Project: Onboard calibration of Internal-combustion (IC) engines for emission reduction and performance boost in hybrid electric vehicles
Description: The calibration process of IC engines can cost £1M and take 18 months (in a study by Jaguar Land Rover) of hundreds of engineers’ work using thousands of maps to calibrate a new engine and make it ready to comply with emission constraints. This project investigates a novel network of wireless sensors/actuators for onboard calibration of engines. ‘on-board’ here means that the engine calibration is carried out while engine is running. Collected data from the wireless sensors and actuators, e.g. temperature, pressure, throttle position, should be classified and processed intelligently using multi-sensory fusion algorithms. Wireless sensors/actuators network must be designed in a way that it operates in harsh and noisy environment of SI engines.
Required background knowledge: Mathematical modelling, Control system design, dynamic simulation using Matlab/Simulink, Wireless sensor networks (WSNs), Sensory fusion algorithms, signal processing,Real-time hardware-in-the-loop (HIL) simulation.
Project: Novel Control methodologies for emission reduction in heavy-duty vehicle diesel engines
Description: Reduction of CO2 emission and other particulates produced from diesel engine is still a challenging problem specially for heavy-duty diesel engines used in large vehicles. The emission fiasco of Volkswagen in 2015 in cheating the emission production data of their diesel engines proves that we still need smarter calibration/control techniques to restrict the emission based on the standards agreed on the Paris accord for climate change. To achieve this, we need more accurate real-time 1D or 0D models of engine to predict emission in realistic scenarios. Then these models can be used for engine downsizing and the investigation of novel model-based control strategies to reduce the emission.
Required background knowledge: Mathematical modelling, Control system design, Dynamic simulation using Matlab/Simulink, Real-time hardware-in-the-loop (HIL) simulation.
Funding for PhD studies
Since the availability of funding is important to many students seeking PhD position, besides special scholarships from research projects/doctoral training centres, QMUL provide independent different sources to fund the PhD studies based on their eligibility, background, and nationality. These funding sources are briefly as follows:
– EECS school studentship
– HEC Pakistan (for Pakistani applicant)
– China Scholarship Council (for Chinese applicants)
– Islamic Development Bank (for applicants from 57 member countries)
– EECS Industrial-partnership studentship (open to all applicants)
– EPSRC industrial CASE Studentships (open to home students)
– MAT program
There is some useful information here in Queen Mary website to fund a PhD program as well.