Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

Jia, Jianmin; Liu, Chenhui; Wan, Tao

doi:10.3390/su11030643

Cited by 23 publications

(12 citation statements)

References 35 publications

(40 reference statements)

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“…Several studies conducted the planning of EV charging stations with assumed traffic flow and network [12][13][14]. Jianmin Jia et al [15] presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data, investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. With the development of information technology, researchers started to explore the trajectory data in the locating problems of charging station on the basis of the floating vehicles, such as taxis, with Global Positioning System (GPS) devices [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles

Hung¹

2022

New Perspectives on Electric Vehicles

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Electric vehicles have become a trend as a replacement to gasoline-powered vehicles and will be a sustainable substitution to conventional vehicles. As the number of electric vehicles in cities increases, the charging demand has surged. The optimal location of the charging station plays an important role in the electric vehicle transit system. This chapter discusses the planning of electric vehicle charging infrastructure for urban. The purpose of this work develops an electric vehicle fast-charging facility planning model by considering battery degradation and vehicle heterogeneity in driving range, and considering various influencing factors such as traffic conditions, user charging costs, daily travel, charging behavior, and distribution network constraints. This work identifies optimal fast-charging stations to minimize the total cost of the transit system for deploying fast-charging networks. Besides, this chapter also analyzes some optimization modeling approach for the fast charging location planning, and point out future research directions.

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Section: Literature Reviewmentioning

confidence: 99%

Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles

Hung¹

2022

New Perspectives on Electric Vehicles

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“…An interesting manuscript "The effect of perceived risk on the purchase intention of electric vehicles: an extension to the technology acceptance model" (Thilina, 2019) seeks to analyze significant market penetration for the sale of electric vehicles accompanied by the analysis (Mo, 2018) of life-cycle cost of ownership including congestion and environmental impacts (Tu, 2019), (Rajeev, et al, 2019), (Hao, 2017), (Philipsen, et al, 2019), (Lopez-Arboleda, et al, 2019), (Almeida, et al, 2019). Considerable emphasis have been placed on improvements in vehicle charging (Wolbertus, et al, 2019) amongst many other underlying technological areas seeking to improve the value proposition to potential buyers (Jager, et al, 2019), , , (Minnerup, et al, 2019), (Muller, 2019) and also make recommendations in both technology (Zha, et al, 2019), , (Zha, et al, 2019), , (Jiyan, et al, 2019), (Pier, et al, 2019), (Agaton, et al, 2019), , (Watanabe, et al, 2019), (Kusaka, et al, 2019), (Zhang, W., et al, 2019), , (Mayer, et al, 2019), (Ricciardi, et al, 2019), , (Yu, Z., et al, 2019), (Senda, et al, 2019), (Marquez-Fernandez, et al, 2019), (Wu, D., et al, 2019), (Wang, H., et al, 2019), (Obayashi, et al, 2019), (Gong, et al, 2019), (Vermeulen, et al, 2019), (Jia, J., et al, 2019), (Li, Q., et al, 2019) and policy incentives (Zhang, X., et al, 2019), (Ortar, et al, 2019), culminating in charging strategies to influence the obvious trade-off between gasoline prices and charging availability , (Wolbertus, et al, 2019),…”

Section: Materials (Literature Review)mentioning

confidence: 99%

Electric Vehicle Sales Catastrophe Averted (?)

Sands¹

2020

MAS

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The literature indicates catastrophic potential for electric vehicles around the year 2019. Amidst the predicted time, this research revisits the prequel analysis with state-of-the-art deterministic artificial intelligence methodologies to posit the potential for catastrophe in the upcoming year. The methodology has proven effective with motion mechanics, electrodynamics, and even financial analysis of sales date in the prequels, since the model commences with simple regression for mathematical model formulation asserting the certainty equivalence principle, followed by derivative modeling and eventually catastrophe analysis of the derivative models. The prequel analysis paradigms are retained in this sequel utilizing both monthly and cumulative sales data in simple least squares algorithms for predictive curve fitting to establish context and help correctly model the mathematical degree of the data. Extrapolation by forward time-propagation established predictions for models of various mathematical degrees (again merely for context). Next, catastrophe analysis (of the derivative form) revealed stable and unstable equilibrium points and then parametric variation was induced to evaluate the resulting behavior of the derivative models, highlighting the importance of the coefficient of the second order term (the acceleration or change of rate of sales as a forcing function). While the forcing function typically embodies both gasoline prices and vehicle charging proliferation, the relative stability of gas prices together with factors such as vehicle-to-grid elevate charging-station proliferation as the primary forcing function of slow-dynamics in catastrophe analysis. This brief manuscript revisits the prequel research to test the validity of those conclusions and with the benefit of the passage of time, reveal how well the mathematical modeling predicted real behavior. The main finding is the predicted potential catastrophe is less likely to occur and recommendations are made to insure catastrophe is averted.

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“…It is necessary to obtain the accurate estimation of EV usage to conduct the regional EV planning on sales market, charging infrastructure, etc. [5]. Multiple sources are utilized to infer the EV usage in recent researches.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Regional Adoption of Electric Vehicle (EV) With Comprehensive Models

et al. 2020

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Adoption of electric vehicles (EVs) has been regarded as one of the most important strategies to address the issues of energy dependence and greenhouse effect. Empirical reviews demonstrate that wide acceptance of EV is still difficult to achieve. This research proposes to investigate the factors that might trigger the wide usage of EVs to support the energy policy. The real-world owners of EV were extracted from the 2017 National Household Travel Survey (NHTS), which provides large-scale individual characteristics. NHTS dataset was processed to establish the comprehensive estimation model for EV adoption with considering vehicle, personal and household factors. Besides the commonly social-economic factors, the gasoline price and car sharing program were found to be significant for EV adoption. Additionally, since the EV owners are only 1.29% of all vehicle owners, this paper introduced the imbalanced dataset technique, which was seldom considered in existing researches. Subsequently, several machine learning methods were utilized to build the prediction model, and the model performance analysis indicates the Decision Tree (DT) model outperforms other models. A regional EV penetration map was also generated for the U.S. to validate the proposed approach. Implications for further research, transport policy and EV market are discussed.

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Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

Cited by 23 publications

References 35 publications

Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles

Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles

Electric Vehicle Sales Catastrophe Averted (?)

Predicting the Regional Adoption of Electric Vehicle (EV) With Comprehensive Models

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