Principal Investigator Prof. Dr. Ahmet Yazıcı

Institution(s): ESOGU

Strategic Target Name: Development of an Autonomous Management System that has a federated health management and distributed reputation framework for sustainability in order to provide energy or time-efficient response to real-time demands for Electric Vehicle Fleets.

Field of Activity of the Project: Artificial intelligence and optimization algorithms; Internet of things application of blockchain technologies; Technologies for automatic error detection and health monitoring; Models, methods and tools that improve software quality

Project Summary: The project aims to develop a sustainable autonomous fleet management system that will respond to real-time demands for electric vehicle fleets. To achieve this goal, four issues will be addressed:

1- A learnable dynamic mission planning and monitoring system that can respond to real-time demands by taking into account the battery health-charge status of the vehicles in the fleet,

2- Developing a distributed reputation framework that calculates the reliability value of fleet management system components and shares it with relevant partners while preserving their integrity,

3- Developing prognostic and health management monitoring with federated learning for the management system and end units,

4- Developing software quality assurance, verification and validation tools for the Fleet Management System and its components.

It is planned to carry out original studies at TRL 3-6 level. A pilot application is planned at ESOGÜ campus scale by integrating the targeted studies with the IoT platform, taking into account the relevant standards.

The charging time-location planning problem in single use of electric vehicles becomes more important in the field for electric vehicle fleet management. In order for the fleet management system to be usable in the field, it is not enough to just plan in terms of mission and charge, without human intervention; It is important for sustainability that it has features such as selection of parameters of seasonality-based planning algorithms, approval of system stakeholders, and automatic detection of errors by monitoring system health. Such comprehensive digital platforms will make the use of electric vehicles in cities more possible.

Project Team:

Researcher ESOGÜ Prof. Dr. İnci Sarıçiçek Research Areas: Industrial Engineering, Optimization Theory and Methods, Production Planning and Control, Supply Chain and Logistics Management, Engineering and Technology
Researcher ESOGÜ Prof. Dr. Kemal Özkan Research Areas: Computer Vision, Artificial Intelligence, Computer Learning and Pattern Recognition
Researcher ESOGÜ Doç. Dr. Metin Özkan Research Areas: Control and System Engineering, Robotics and Mechatronics Systems, Software
Researcher ESOGÜ Doç. Dr. Eyüp Çınar Research Areas: Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Learning
Researcher ESOGÜ Doç. Dr. Uğur Yayan Research Areas: Computer Learning, Neural Networks, Software Engineering, Robotics
Employee ESOGÜ Eyyüb Baha Karasu
Employee ESOGÜ Burak Kanber
Researcher PhD Candidate ESOGÜ Özge Arslan
Researcher PhD Candidate ESOGÜ Metin Yılmaz
Researcher PhD Candidate ESOGÜ Mehmet Bilgehan Taş
Researcher MS Student Öğrencisi ESOGÜ Gökhan Çetiner
Researcher Undergraduate Student ESOGÜ Hikmet Can Urhan
Researcher Undergraduate Student ESOGÜ Naser A. M. Odeh
Researcher Undergraduate Student ESOGÜ Enes Sevim

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