Coot-feedback artificial tree-based energy-aware electric vehicle charge scheduling algorithm

• Published in: Peer-to-peer networking and applications 2025-02, Vol.18 (1), p.1-17
• Main Authors: K, Mekala, S, Sumathi, M, Belsam Jeba Ananth
• Format: Article
• Language: English
• Subjects: Algorithms; Batteries; Charge efficiency; Communications Engineering; Computer Communication Networks; Electric charge; Electric vehicle charging; Electric vehicles; Energy consumption; Energy management; Engineering; Feedback; Information Systems and Communication Service; Lateness; Networks; Scheduling; Signal,Image and Speech Processing
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-024-01816-6

Electric vehicles (EVs) perform a significant part in transportation, which reduces ecological pollution as well as fuel costs and the usage of electric vehicles is raised in future. Despite this, the number of vehicles available in the real world is greater than the available charging stations. Scheduling the charging period of the huge fleet of EVs creates a major issue owing to charging station conditions. In addition, the charging time of batteries as well as the power restraints makes the system more complex. Hence, the energy-aware EV charge scheduling algorithm is designed based on the proposed Coot Feedback Artificial Tree (Coot-FAT) approach, which is the amalgamation of the Coot algorithm and Feedback Artificial Tree (FAT) approach. The EV is simulated, where the charging stations are installed in a parking area for every user. At that, the EV charge scheduling process is implemented using the developed Coot-FAT procedure based on fitness function by contemplating several constraints, including tardiness as well as energy consumption, which is used to effectively minimize tardiness and charging delay of the EVs. Then, the developed method achieved rates of minimum average energy efficiency, minimum tardiness, and the maximum number of charge vehicles is 2024.8 Kw/h, 0.18, and 44, respectively.