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Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic

Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic

Daniel Rivera-Rojo, Carlos Martinez, Diego Almazo, Uzziel Caldiño, Abdiel Ramirez, Valdemar Tejeda
Copyright: © 2019 |Pages: 12
ISBN13: 9781522581314|ISBN10: 1522581316|EISBN13: 9781522581321
DOI: 10.4018/978-1-5225-8131-4.ch023
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MLA

Rivera-Rojo, Daniel, et al. "Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic." Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities, edited by Alberto Ochoa Ortiz-Zezzatti, et al., IGI Global, 2019, pp. 411-422. https://doi.org/10.4018/978-1-5225-8131-4.ch023

APA

Rivera-Rojo, D., Martinez, C., Almazo, D., Caldiño, U., Ramirez, A., & Tejeda, V. (2019). Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic. In A. Ochoa Ortiz-Zezzatti, G. Rivera, C. Gómez-Santillán, & B. Sánchez–Lara (Eds.), Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities (pp. 411-422). IGI Global. https://doi.org/10.4018/978-1-5225-8131-4.ch023

Chicago

Rivera-Rojo, Daniel, et al. "Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic." In Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities, edited by Alberto Ochoa Ortiz-Zezzatti, et al., 411-422. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8131-4.ch023

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Abstract

The purpose of this chapter is to understand a multivariable optimization associated with the path of a group of vehicles integrated in an ecological community and determine the optimal route involve speed, storage, and travel resources. Time of charge for determining the cost benefit have partnered with a travel plan associated with the charge point in a smart city, which has as principal basis the orography restriction related with the energy consumed. Although this problem has been studied on several occasions, the literature failed to establish ubiquitous computing for interacting with the various values associated with the achievement of the group of vehicles and their cost-benefit of each member of the community, comparing their individual trips for the group and determining the quantity of energy required for each one. There are several factors that can influence in the achievement of a group trip. For this chapter, the authors propose to use bat algorithm, which has proven to be efficient for the convergence of several issues.

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