Mahmoud Ibrahim Hassanin Mohamed

The project aims to advance Electric Propulsion Drive System (EPDS) Digital Twin (DT) technology for Software Defined Electric Vehicles (SDEVs), with a focus on achieving DT adaptive and intelligent levels. It addresses the need for efficient testing and evaluation of electric propulsion systems in line with EU clean energy transition goals. Leveraging the rapid development of DT technology, the project seeks to contribute to SDV technology through enhanced modeling, data gathering, IoT integration, and system optimization. Key challenges include lifecycle management, data processing, and real-time communication between physical and virtual systems. The project encompasses advanced modeling, data gathering, IoT, and communication infrastructure, system integration, optimization, and technology demonstration.

The project will develop a smart, adaptive electric drive that increases the energy efficiency and reliability of electric vehicles. The innovative solution combines artificial intelligence-based control with advanced sensor technology, allowing the drive to adapt in real-time to changes in traffic and road conditions. The project will produce a laboratory prototype, a user-friendly software solution for data processing, and comprehensive documentation that will simplify the implementation of the system.

The objective of the EEV5040 Industrial Automation and Drives activity is to introduce students to the importance of industrial automation and electric drives and the latest trends in these fields (including IoT). As a result of the development activities, students will acquire in-depth knowledge of electric drive management, model-based design methodology and IoT applications in industrial automation in the future. They are able to create, adapt and analyse electric motor control systems and solve real problems in this field. These results influence the quality of industry-specific ICT teaching, providing students with the practical skills and knowledge essential to today's industrial automation. Within the framework of the development project, the subject EEM0040 Machine vision is also amended, where the traditional machine vision curriculum is added to it by adding the hyperspectral technology component. The aim of the development activity is to combine concepts of machine vision with hyperspectral data processing. Within the subject, students will develop practical skills in the use of hyperspectral cameras, from camera setup to processing of various hyperspectral images. They acquire knowledge of the specifics of hyperspectral data, such as the wavelength spectrum and its relationship to the properties of materials. In addition, they learn the methods of machine vision, which allow to identify different objects and characteristics from hyperspectral images.