A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms. Issue 2 (15th June 2022)

• Record Type: Journal Article
• Authors: Rajamoorthy, Rajasekaran; Arunachalam, Gokulalakshmi; Kasinathan, Padmanathan; Devendiran, Ramkumar; Ahmadi, P.; Pandiyan, Santhiya; Muthusamy, Suresh; Panchal, Hitesh; Kazem, Hussein A; Sharma, Prabhakar
• Is Part Of: Energy sources. Volume 44:Issue 2(2022)
• Page Start: 3555
• Page End: 3575
• Publication Date: 2022-06-15
• Subjects: Electric Vehicle (EV) -- Grey Wolf Optimizer (GWO) -- charging schedules -- Sail Fish Optimization (SFO) -- transport system; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2022.2067268
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered.