Abstract
The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.
Highlights
The green city concept is one of the realities of the future
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A novel estimation model is proposed and applied to the real-world data of the New York City taxi fleets in order to obtain the total energy consumption of electric vehicles (EVs) for different time intervals, in the scenario that all the yellow taxis are replaced with a specific type of electric vehicle
Summary
The green city concept is one of the realities of the future. The interactions between cars, houses, trains, or any other component of the smart city, are not the only contributing factor in the formulation of this concept; from an energetic point of view, it is necessary that the components use green energy not to pollute the environment [1,2]. The emission reduction targets motivate manufacturers to invest more in electric mobility and lead to rapid growth in electric vehicles (EVs). Electric mobility is one of the most critical elements that should be considered and investigated more deeply. Even in the era of electric vehicles, the problem of range anxiety has pushed back people from adopting this technology. People have developed a low level of trust in the EVs’ battery capacity, causing uncertainty for reaching their destination [3]
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