Due to the environmental impact of fossil fuels and high variability in their prices, there is rising interest in adopting electric vehicles (EVs) by both individuals and governments. Despite the advances in vehicle efficiency and battery capacity, a key hurdle is the inherent interdependence between EV adoption and charging station deployment–EV adoption (and hence, charging demand) increases with the availability of charging stations (operated by service providers) and vice versa. Thus, effective placement of charging stations plays a key role in EV adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a “mixed-packing-and-covering” optimization framework that models competing concerns of the service provider and EV users, which is also extended to optimize government grant allocation to multiple service providers; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data. In most cases, and especially when budget is scarce, our heuristic achieves an improvement of 10-20 over a naive heuristic, both in finding feasible solutions and maximizing demand.
A useful charging station placement solution needs to take into account the charging demand, budget, coverage, and waiting time at the stations. Existing work has not considered all of these factors simultaneously. For example, placement of charging stations based on predicted demand has been studied in @cite_24 @cite_9 @cite_6 @cite_13 @cite_23 @cite_28 , but they do not simultaneously consider constraints on budget, coverage, and waiting times. Coverage requirements in charging station placement has been modeled as path cover in @cite_2 , and reachability cover in @cite_26 over the city road network, but they do not consider the demand satisfied by the deployment and the waiting time faced by the users.
@cite_26 @cite_28 @cite_9 @cite_6 @cite_24 @cite_23 @cite_2 @cite_13
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The short cruising range due to the limited battery supply of current Electric Vehicles (EVs) is one of the main obstacles for a complete transition to E-mobility. Until batteries of higher energy storage density have been developed, it is of utmost importance to deliberately plan the locations of new loading stations for best possible coverage. Ideally the network of loading stations should allow driving from anywhere to anywhere (and back) without running out of energy. We show that minimizing the number of necessary loading stations to achieve this goal is NP-hard and even worse, we can rule out polynomial-time constant approximation algorithms. Hence algorithms with better approximation guarantees have to make use of the special structure of road networks (which is not obvious how to do it). On the positive side, we show with instance based lower bounds that our heuristic algorithms achieve provably good solutions on real-world problem instances. One of the most critical barriers to widespread adoption of electric cars is the lack of charging station infrastructure. Although it is expected that a sufficient number of charging stations will be constructed eventually, due to various practical reasons they may have to be introduced gradually over time. In this paper, we formulate a multi-period optimization model based on a flow-refueling location model for strategic charging station location planning. We also propose two myopic methods and develop a case study based on the real traffic flow data of the Korean Expressway network in 2011. We discuss the performance of the three proposed methods. Certain examples provide systems and methods to identify placement for an electric charging station infrastructure. Certain examples provide systems and methods to generate a deployment plan for one or more electric vehicle charging stations. An example method includes gathering data for a specified geographic area and forecasting a demand for electric vehicles for the specified area. The example method includes modeling driving patterns in the specified area using available data and improving a charging infrastructure model based on the driving pattern and demand forecast information for the specified area. The example method includes generating and providing a recommendation regarding an electric vehicle charging infrastructure and deployment strategy for the specified area based on the improved charging infrastructure model. Electric mobility is considered to be an essential building block of a sustainable transportation paradigm. Particularly for the urban metropolis of the 21st century electric vehicles embody a promise of decreased air pollution and increased quality of living. However, city planners need to muster enormous investments into urban charging infrastructure in advance. We develop a decision support system to help city planners use these funds more efficiently and place charging infrastructure where it is truly needed. We construct a regression model to determine factors that influence the utilization of charge points in Amsterdam, one of the most "electrified" cities in the world. We include data on more than 50,000 points of interest, for instance shopping malls and museums, as well as charging data from 273 charging points with 427 individual outlets in the city of Amsterdam. From these influences we derive a decision support system that places charge points where they are expected to experience the highest utilization. A system, method and program product for siting charging stations. A mobility detection module collects traffic data from vehicle sensors distributed in an area. A map matching module maps detected traffic in the area. A vehicle flow module temporally characterizes mapped traffic flow. An electric vehicle (EV) requirements (EVR) evaluator determines an optimal number of charging stations and respective locations for siting the charging stations. Transportation electrification is one of the essential components in the future smart city planning and electric vehicles (EVs) will be integrated into the transportation system seamlessly. Charging stations are the main source of energy for EVs and their locations are critical to the accessibility of EVs in a city. They should be carefully situated so that an EV can access a charging station within its driving range and cruise around anywhere in the city upon being recharged. In this paper, we formulate the Electric Vehicle Charging Station Placement Problem, in which we minimize the total construction cost subject to the constraints for the charging station coverage and the convenience of the drivers for EV charging. We study the properties of the problem, especially its NP-hardness, and propose an efficient greedy algorithm to tackle the problem. We perform a series of simulation whose results show that the greedy algorithm can result in solutions comparable to the mixed-integer programming approach and its computation time is much shorter. Compared to conventional cars, electric vehicles (EVs) still suffer from considerably shorter cruising ranges. Combined with the sparsity of battery loading stations, the complete transition to E-mobility still seems a long way to go. In this paper, we consider the problem of placing as few loading stations as possible so that on any shortest path there are sufficiently many not to run out of energy. We show how to model this problem and introduce heuristics which provide close-to-optimal solutions even in large road networks. Access to electric vehicle (EV) charging stations will affect EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. Parking information from more than 30,000 records of personal trips in the Puget Sound, Washington, Regional Council's 2006 Household Activity Survey is used to determine public (nonresidential) parking locations and durations. Regression equations predict parking demand variables (i.e., total vehicle hours per zone or neighborhood and parked time per vehicle trip) as a function of site accessibility, local job and population density, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs to a mixed-integer programming problem developed to determine optimal location assignments for EV charging stations. The algorithm minimizes EV users' costs for station access while penalizing unmet demand. This useful specification is used to determine the top locations fo...