Collaborators: Dr. Rached Dhaouadi (Petrofac Research Chair in Renewable Energy, AUS), Dr. Habibur Rehman (AUS), Dr. Mostafa Shaaban (AUS), Dr. Shayok Mukhopadhyay (AUS)
With the rapid increase in world population and continuous increase in the number of circulating vehicles, it is found that more than 70 percent of environmental pollution is caused by gas emission from vehicles. The growing global concern about greenhouse gases and environmental pollution has, therefore, raised the awareness of academic institutions, government bodies and other stockholders and intensified the search for green energy solutions. Development of electric vehicles and hybrid-electric vehicles has become an important strategy to cope with climate change and energy crisis.
Electrical vehicles technology has rapidly evolved in recent years. On the other hand, solar energy systems provide nowadays a unique, simple and elegant method of harnessing the sun's energy to provide electric power to electric and hybrid-electric vehicles thus taking the world a step closer to a greener community. Wireless charging is also expected to play a major part in the roll-out of electric vehicles, allowing for easy charging of vehicles.
Installing solar systems above surface and car parks is becoming increasingly popular. The area above a car park is an otherwise unexploited site that can be effectively used to generate renewable energy. Solar carparks can enhance the car-parking experience in a number of ways, as well as improving the economic and environmental performance of the asset.
This project focuses on the design and development of a solar charging station for car parking lots with wireless power transfer capability. The focus is on selecting the optimal charging strategy to maximize the energy from the PV panels. Since the solar irradiance is at its peak during the day, the objective is to design an approach to fully maximize the harnessed energy and to use the excess energy for storage in batteries and supercapacitors.
Collaborators: Dr. Rached Dhaouadi (Petrofac Research Chair in Renewable Energy, AUS), Dr. Shayok Mukhopadhyay (AUS)
This research focuses on using advanced adaptive control, and estimation techniques for identifying the parameters of supercapacitors. The methods developed have been tested on several supercapacitors, and they show promising results.
Collaborators: Dr. Shayok Mukhopadhyay (AUS), Dr. Habibur Rehman (AUS)
This research focuses on using universal adaptive stabilization techniques for identifying the parameters of Li-ion battery models. The team has developed a drive system to test heavy duty Li-ion batteries, and the in-house battery bank available is now rated at 400V, 6.6A (with peak current capability of around 100A).
Collaborators: Dr. Habibur Rehman (AUS), Dr. Shayok Mukhopadhyay (AUS)
This research focuses on using field-oriented control of induction motors, targeted towards electric vehicle drive-train applications. Investigation is carried out into different type of inverters/controllers required for smooth drive-train operation when coupled with a large capacity battery bank. Battery state of charge estimation, and management is also considered. Fractional order speed control techniques have also been recently tested on this motor-drive system.
Collaborators: Dr. Shayok Mukhopadhyay (AUS)
This work focuses on using advanced path planning algorithms like rapidly exploring random trees (RRTs) for exploration. In standard RRT path planning implementations, the map along with obstacles have to be known. In this work it is assumed that the environment is unknown, and upon encountering an obstacle the robot(s) update their map. This work enhances exploration performance by using multiple RRTs at the same time. Local trees focus on exploring the immediate vicinity, making sure tight corners are not missed during exploration, whereas global trees make sure to complete map coverage. This work has been verified in ROS (robot operating system) simulations, and also with real life implementation for two-dimensional exploration. This work produced a custom ROS package available at http://wiki.ros.org/rrt_exploration for researchers to use.
Collaborators: Dr. Mamoun Abdel-Hafez (AUS), Dr. Mohammad Jaradat (AUS), Dr. Shayok Mukhopadhyay
This is an interdisciplinary between the electrical and mechanical departments at AUS, and involves the mechatronics graduate program. Several prototypes of in-pipe inspection robots were developed and tested. The final version of the robot uses pressure sensors along with artificial neural network techniques to detect leaks in pipes, the robot also is able to localize leaks. This project won the UAE AI and Robotics for Good, National category Award (AED – 1Million), in 2017.