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In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless Power Transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale dataset, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks) and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the Kernel Density Estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multi-objective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost.
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Electric Vehicle Charging Station Placement Method for Urban Areas
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 studied each feature 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.
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- Award ID(s):
- 1810537
- PAR ID:
- 10107901
- Date Published:
- Journal Name:
- IEEE Transactions on Smart Grid
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 1949-3053
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TSG.2019.2907262
