Intelligent charge scheduling and eco-routing mechanism for electric vehicles: A multi-objective heuristic approach

• Published in: Sustainable cities and society 2021-06, Vol.69, p.102820, Article 102820
• Main Authors: Chakraborty, Nilotpal, Mondal, Arijit, Mondal, Samrat
• Format: Article
• Language: English
• Subjects: Charge scheduling; Eco-routing; Electric vehicle; Heuristic algorithm
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2021.102820

•A detailed mathematical study on the problem of Eco-routing and Charge scheduling for electric vehicles is conducted.•A directed weighted graph based modeling approach is adopted.•A multi-objective heuristic mechanism is proposed to obtain the solutions efficiently.•Experimental evaluation is carried out on real-life data sets. Due to the rising pollution and greenhouse gas emissions resulting from fossil fuel-based transportation systems, researchers and policymakers are pushing for Electric Vehicle (EV) that is envisaged as an efficient, eco-friendly alternative. However, due to their limited range and battery capacity, EVs need frequent charging, which is time-consuming and available at specific locations. Therefore, proper charge scheduling and route management of EVs is essential and significant. This paper addresses this problem by proposing an intelligent heuristic mechanism that ensures that the EVs are always routed through a path that minimizes the energy consumption and the total time to travel. We formulate it as a multi-objective optimization problem considering real-world specifications and constraints and propose a graph-based multi-objective heuristic algorithm (MoHA) to obtain the desired solutions quickly. Further, multiple variants of the proposed algorithm are proposed, and comparative analysis is performed on practical datasets. The proposed algorithm is evaluated based on some of the well-known performance metrics for multi-objective approaches. The results obtained show that the energy-aware-MoHA variant produced 32.39% better results in minimizing energy consumption, and time-aware-MoHA performed better in optimizing average time requirements by 24.32%. Moreover, the initial ordering of the EVs has significant importance on the proposed algorithm's overall performance.