Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Choi, Bong Gi; Oh, Byeong Chan; Choi, Seung-Kyum; Kim, Sung-Yul

doi:10.3390/en13071650

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…In analyzing the charging behavior of PEV users, the dependence of charging session consumption on various user and session features is explored using a data-driven energy prediction framework. Accurate prediction of session charging demand has many possible applications, including scheduling [58][59][60], grid stability [61,62], and smart grid integration [63,64]. By formulating the energy prediction as a multiple regression problem, several statistical machine learning regression methods are applied to predict how much energy the PEV user will consume after plugging-in.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

et al. 2020

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Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

et al. 2020

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“…Methods for finding optimal locations of charging stations can focus on long distance and regional travel [15], [16] or on infrastructure within a city [17], [18]. Except for finding the optimal locations of electric vehicle charging stations (eCS), it is also necessary to specify their capacities.…”

Section: Introductionmentioning

confidence: 99%

Capacity Optimization of EV Charging Networks: A Greedy Algorithmic Approach

Jovanović

Bayhan

Bayram

2022

2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE)

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In the recent years, there has been a steady increase in the use of electrical vehicles (EV). Their further adoption is becoming more dependent on the quality of service provided by the charging infrastructure. In this paper, the focus is on optimizing the charging infrastructure from the point of minimizing the service drop modelled using the standard M/M/c/c loss queue. To be exact, a mathematical model is proposed for the problem of optimizing capacities at individual stations in an EV charging network. The novelty is in considering the relation of capacity of a charging station to its arrival rate. Due to the non-linearity of the problem, a greedy algorithm combined with a local search is developed for finding near optimal configurations of the system. The new model is evaluated using real-world data for population density and existing charging infrastructure for metropolitan areas. The conducted computational experiments, show that charging networks optimized using the proposed model, significantly better reflect the state-on-the-ground than standardly used models, while maintaining a low service drop rate.

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“…Finally, the calculation results of this method verify that the proposed models and algorithms are feasible. Choi et al [16] estimated EVs charging needs and selected priority fast chargers. The paper adopted a new method to simulate the number of chargers in multiple charging zones for EVs to provide a road data basis for reducing charger usage.…”

Section: Introductionmentioning

confidence: 99%

Charging Station Planning of Electric Vehicle in Battery Swapping Scene

Shangguan

Qi²

2022

J. Phys.: Conf. Ser.

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In response to the national green development concept, the electric vehicles quantity continues to surge. However, the cost of electric vehicle charging facilities is hindering the development of electric vehicles. Considering charging facility cost and fuel consumption cost of logistics vehicles, we propose the charging planning for electric vehicle batteries. First, according to the user’s mileage, we simulate the maximum demand for electric vehicle batteries in each time. Then, based on the electric vehicle battery charging planning, we establish an optimization model for the number of battery charging equipment and the fuel consumption cost of logistics vehicles. Finally, through specific examples we show the optimal number of delivery is 4 from the battery charging station to the battery swapping station. The battery charging equipment are 368 units and the fuel consumption cost of the logistics vehicle is 8040 RMB per day. In addition, the number of logistics vehicle departures is reduced by 3 compared to the same amount of delivery, which significantly improves the stability and efficiency of the battery swapping scene.

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Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Cited by 9 publications

References 25 publications

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Capacity Optimization of EV Charging Networks: A Greedy Algorithmic Approach

Charging Station Planning of Electric Vehicle in Battery Swapping Scene

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