Abstract
In this paper, we present a new approach to solve the Electric Vehicle (EV) Charging Station (CS) Placement problem. It aims to find the most suitable sites with the adequate type of CS. Using a meta-heuristic approach to tackle this issue seems appropriate since it is defined as an NP-hard problem. Therefore, we have introduced an improved genetic algorithm adapted to our problem by developing a new heuristic to generate initial solutions. Moreover, we have proposed modified crossover and mutation operators to enhance generated solutions. Throughout this work, we have aimed to minimize the total costs consisting of: travel, investment, and maintenance costs. However, we have to respect two major constraints: budget limitation and charging station capacity. The results provided by experimentation show that the proposed algorithm provides better results compared to the most efficient algorithms in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MacDonald, J.: Electric vehicles to be 35% of global new car sales by 2040. Bloomberg New Energy Finan. 25 (2016)
Schmid, A.: An analysis of the environmental impact of electric vehicles. S&T’s Peer Peer 1(2), 2 (2017)
Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence (1975)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, volume 4, pp. 1942–1948. IEEE Computer Society (1995)
Mehar, S., Senouci, S.M.: An optimization location scheme for electric charging stations. In: IEEE SaCoNet, pages 1–5 (2013)
Liu, Z.-f., Zhang, W., Ji, X., Li, K.: Optimal planning of charging station for electric vehicle based on particle swarm optimization. In: Innovative Smart Grid Technologies-Asia (ISGT Asia), 2012 IEEE, pages 1–5. IEEE (2012)
Zhu, Z.-H., Gao, Z.-Y., Zheng, J.-F., Hao-Ming, D.: Charging station location problem of plug-in electric vehicles. J. Transp. Geogr. 52, 11–22 (2016)
Bai, X., Chin, K.-S., Zhou, Z.: A bi-objective model for location planning of electric vehicle charging stations with gps trajectory data. Comput. Indus. Eng. (2019)
Zhang, Y., Zhang, Q., Farnoosh, A., Chen, S., Li, Y.: Gis-based multi-objective particle swarm optimization of charging stations for electric vehicles. Energy 169, 844–853 (2019)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Zhao, W., Wang, L., Zhang, Z.: Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl.-Based Syst. 163, 283–304 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ouertani, M.W., Manita, G., Korbaa, O. (2021). Improved Genetic Algorithm for Electric Vehicle Charging Station Placement. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-2765-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2764-4
Online ISBN: 978-981-16-2765-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)