Electric Vehicle Charging Station Placement Method for Urban Areas
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Abstract (arXiv)
For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Some researches consider a general setup, using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in the urban area, where driving ranges is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we add constraint one-by-one to see how to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this study shows the importance of incorporating the protection device upgrade in urban system planning on charging stations.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Reinforcement Learning-based Placement of Charging Stations in Urban Road Networkscited by 2
- 2023Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniquescited by 0
- 2026Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)cited by 0
- 2026Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)24 Sep 2026
- 2023Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques20 Jul 2023
- 2022Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks13 Jun 2022