The fast development of electric vehicles (EV) and EV chargers introduces many factors that affect the grid. EV charging and charge scheduling also bring challenges to EV drivers and grid operators. In this work, we propose a human-centric, data-driven, city-scale, multivariate optimization approach for the EV-interfaced grid. This approach takes into account user historical driving and charging habits, user preferences, EV characteristics, city-scale mobility, EV charger availability and price, and grid capacity. The user preferences include the trade-off between cost and time to charge, as well as incentives to participate in different energy-saving programs. We leverage deep reinforcement learning (DRL) to make recommendations to EV drivers and optimize their welfare while enhancing grid performance.
more »
« less
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 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.
more »
« less
- Award ID(s):
- 2136948
- NSF-PAR ID:
- 10310328
- Date Published:
- Journal Name:
- IEEE Internet of Things Journal
- ISSN:
- 2372-2541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Supply energy to battery-powered sensor devices by deploying wireless chargers is a promising way to prolong the operation time of wireless sensor networks, and has attracted much attention recently. Existing works focus on maximizing the total received charging power of the network. However, this may face the unbalanced energy allocation problem, which is not beneficial to prolong the operation time of wireless sensor networks. In this paper, we consider the individual energy requirement of each sensor node, and study the problem of minimum charger placement. That is, we focus on finding a strategy for placing wireless chargers from a given candidate location set, such that each sensor node’s energy requirement can be met, meanwhile the total number of used chargers can be minimized. We show that the problem to be solved is NP-hard, and present two approximation algorithms which are based on the greedy scheme and relax rounding scheme, respectively. We prove that both of the two algorithms have performance guarantees. Finally, we validate the performance of our algorithms by performing extensive numerical simulations. Simulation results show the effectiveness of our proposed algorithmsmore » « less
-
null (Ed.)Wireless Charging Highways (WCHs) have been introduced by industry and academia to enable charging-while-driving for electric vehicles (EVs) and to combat range anxiety. While detailed planning and performance evaluation of such systems are crucial due to high cost and long life expectancy, most existing works assume a perfect communication environment. In this paper, we introduce a joint capacity model that takes into account both power and communication resources for WCH construction planning, and optimal day-to-day operation. The vehicle-to-infrastructure (V2I) communication and grid power capacities, along with the EV’s average service rate are formulated following technology requirements, EV speed-density characteristics, and the EV’s energy needs and consumption. In addition, a two-dimension Markov chain-based model is designed to capture the WCH power and connectivity dynamics. The proposed model can be used to calculate the system’s Quality of Service (QoS) and profit, provide design insights, and assess the impact of speed regulation, or admission control on the WCH lane. Finally, the performance of the proposed model is evaluated using real US highway data with the results demonstrating its ability to accurately capture the service provision dynamics, and to identify trade-offs between system parameters.more » « less
-
Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.more » « less
-
more » « less
Abstract This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JIOT.2021.3121756
