CatCharger: Deploying In-Motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic

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 data set, 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 multiobjective 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.

I. Introduction

Electric vehicle (EV) industry has been burgeoning in recent years because of the quick depletion of fossil fuels [1], [2]. In countries like China, India, and the USA, governments are establishing new policies to replace gasoline-based vehicles with electric ones [3]–[5]. Due to the limit of battery capacity, the driving range of most EVs is still quite limited (e.g., 100 miles) [2]. Hence, most EVs must be recharged frequently during service time and their recharge time generally takes more than 30 min. However, to fulfill metropolitan transit demands, EVs, especially public service EVs, are expected to be continuously operable without recharging downtime [6]. Driven by this expectation, multiple wireless power transfer (WPT) techniques for in-motion EV charging have been proposed [6]. Specifically, the wireless chargers are deployed in certain road segments to serve as charging lanes. As long as an EV drives through a charging lane, its State of Charge (SoC) can be charged dynamically [7], [8]. However, a grave challenge remains unsolved: how to determine the deployment plan of in-motion wireless chargers (i.e., charger locations and charger lengths) for a metropolitan road network that minimizes the deployment cost while maintaining the continuous operability of EVs on the roads. By operability, we mean that an EV’s SoC is maintained above some level throughout its driving.