PhD oppotunities

Project proposals

My proposals for PhD projects come under three main areas. I am always open to discuss novel project ideas as long as it relates to my research interests.


Energy storage system interface for Vehicle-to-grid (V2G) application: 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.

Improving the life-cycle of Electrical Vehicle Battery Packs using wireless or antenna networks: Predicting the life cycle of battery pack system in Plug-in Hybrid Electrical Vehicles (PHEVs) has been the subject of studies toward the large-scale use of Electric Vehicles. With the current technology, a single battery cannot generate the performance we expect for a commercial EV. A group of batteries are used in a parallel, series or matrix form famously called as battery pack to provide the required power. Life cycle prediction of a single Li-ion battery cell is challenging due to the complexity of electrochemical modelling and thermal management. The interconnection of batteries makes the prediction more challenging as the electrical dynamics and thermal characteristics of each battery cell are different from the other cells. 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. There have been numerous approaches suggested in the literature to suppress such ageing propagation. This project investigates the use of wireless/antenna communication system and reconfiguration algorithms to enhance the life time of battery packs.

Improving the life-cycle of Electrical Vehicle Battery Packs using wireless or antenna networks: Predicting the life cycle of battery pack system in Plug-in Hybrid Electrical Vehicles (PHEVs) has been the subject of studies toward the large-scale use of Electric Vehicles. With the current technology, a single battery cannot generate the performance we expect for a commercial EV. A group of batteries are used in a parallel, series or matrix form famously called as battery pack to provide the required power. Life cycle prediction of a single Li-ion battery cell is challenging due to the complexity of electrochemical modelling and thermal management. The interconnection of batteries makes the prediction more challenging as the electrical dynamics and thermal characteristics of each battery cell are different from the other cells. 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. There have been numerous approaches suggested in the literature to suppress such ageing propagation. This project investigates the use of wireless/antenna communication system and reconfiguration algorithms to enhance the lifetime of battery packs.


Wireless energy measurement in home environment: Low-cost monitoring of energy usage in home is still a challenging problem and considering the rise of energy cost is very important for any household. This project is focused on designing and implementing a wireless network so that energy consumption of home appliances becomes minimum. Algorithms like situational awareness (SA) can be employed for the real-time scheduling, power distribution, and automation of wireless sensor network of home appliances/renewables. SA algorithms can provide a vision of the network events before the event occur  in a distributed fashion. Classifying the pattern of energy consumption of a device in real-time is by far difficult and can be considered as a machine-learning problem. As a further step, using the energy usage pattern data, an intelligent system can be implemented to learn the behaviour of home users to reduce the cost of energy. This system would remotely control the power consumption of each appliance instead of human in the network and judicially switch on/off devices. 

  • Real power usage (RMS) of individual home appliances
  • Sensor/Antenna/actuator network design to sense the environment (Devices, Human, Renewables, Batteries) and send the commands to the environment (actuators)
  • Protocols/architecture design of SA system
  • Security: Game theory
  • Ad-hoc network (MAC layer, Physical layer)
    • Routing
    • Design
  • Optimal Control/Adaptive Dynamic Programming for optimising the sensor network
  • Machine Learning to interpret human behaviour towards the devices
  • Intelligent Agents (as a part of SA system) to make decisions for power efficiency (switching on/off devices, controlling the renewable source)

sa

The above diagram shows the generalised model of Situational Awareness for home energy efficiency.


Onboard calibration of Internal-combustion Spark-Ignition (SI) engines for emission reduction: 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. Wireless sensors/actuators network must be designed in a way that it operates in harsh and noisy environment of SI engines.

Novel Control methodologies for emission reduction in Diesel and/or SI engines: Reduction of CO2 emission and other particulates produced from internal-combustion engine is still a challenge. The emission fiasco of Volkswagen in 2015 in cheating the emission production data of their diesel engines (http://www.theguardian.com/business/2015/sep/22/vw-scandal-caused-nearly-1m-tonnes-of-extra-pollution-analysis-shows) proves that we still need smarter calibration/control techniques to restrict the emission within the EU standards. 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 the investigation of novel model-based control strategies to reduce the emission. Different aspects, i.e. modelling, control system development and downsizing Spark-ignition (SI) petrol engine or diesel engine, can be defined in the form of projects. 


Source of funding for PhD studies

In Queen Mary, we provide different sources to fund the PhD studies of home/EU and international applicants. To list them:

There is some useful information here in Queen Mary website to fund a PhD program as well.

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