Optimal design of electric vehicle public charging system in an urban network for Greenhouse Gas Emission and cost minimization

In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.     

Heterogeneous hazard model of PEV users charging intervals: Analysis of four year charging transactions data

Public charging infrastructure represents a key success factor in the promotion of plug-in electric vehicles (PEV). Given that a large initial investment is required for the widespread adoption of PEV, many studies have addressed the location choice problem for charging infrastructure using a priori simple assumptions. Ideally, however, identifying optimal locations of charging stations necessitates an understanding of charging behavior. Limited market penetration of PEV makes it difficult to grasp any regularities in charging behavior. Using a Dutch data set about four-years of charging transactions, this study presents a detailed analysis of inter-charging times. Recognizing that PEV users may exhibit different charging behavior, this study estimates a latent class hazard duration model, which accommodates duration dependence, unobserved heterogeneity and the effects of time-varying covariates. PEV users are endogenously classified into regular and random users by treating charging regularity as a latent variable. The paper provides valuable insights into the dynamics of charging behavior at public charging stations, and which strategies can be successfully used to improve the performance of public charging infrastructure.     

大城市中心区实施拥堵收费的影响评价研究

文章在分析大城市中心区拥堵收费实施条件与方式的基础上,定性分析实施拥堵收费对居民出行行为以及中心区动静态交通的影响,最后提出基于模糊评价理论的中心区拥堵收费影响评价模型.     

The role of volume-delay functions in forecasting and evaluating congestion charging schemes: the Stockholm case

This paper uses observations from before and during the Stockkholm congestion charging trial in order to validate and improve a transportation model for Stockholm. The model overestimates the impact of the charges on traffic volumes while at the same time it substantially underestimates the impact on travel times. These forecast errors lead to considerable underestimation of economic benefits which are dominated by travel time savings. The source of error lies in the static assignment that is used in the model. Making the volume-delay functions (VDFs) steeper only marginally improves the quality of forecast but strongly impacts the result of benefit calculations. We therefore conclude that the dynamic assignment is crucial for an informed decision on introducing measures aimed at relieving congestion. However, in the absence of such a calibrated dynamic model for a city, we recommend that at least a sensitivity analysis with respect to the slope of VDFs is performed.     

大指挥官PHEV充电系统安规测试与案例分析

随着电动汽车市场占有率逐渐增大,电动汽车安全一直是国家和企业关注的焦点。GB18384电动汽车安全要求,从储能系统、车辆操作安全和故障防护,以及人员触电防护3方面做了详细的要求。本文着重阐述非传导充电系统如何做安规测试以及案例分析。     

基于电动出租车数据的充电桩选址聚类方法比较

为有效降低出租车运营企业及经营者的经济成本,通过分析出租车的卫星轨迹数据,比较和选取用于电动出租车充电桩选址规划的聚类方法。以上海市电动出租车充电站的选址规划为研究对象,分别基于孤立森林和聚类算法设计异常值检测方法,对相关时段的出租车卫星数据进行清理以及数据可视化处理;比较层次聚类（Agglomerative Clustering）、高斯混合模型（Gaussian Mixture Model, GMM）、K-means聚类、Mean-Shift聚类以及谱聚类（Spectral Clustering） 5种算法的聚类效果,并选取K-means算法作为充电桩选址规划参考算法。从城市区域划分及企业运营角度确定充电桩选址方案,为未来上海市区电动出租车充电桩的数量和容量配置提供设计依据。     

齐齐哈尔市中心城区电动汽车及充电设施需求预测研究

在目前能源危机和环境保护的双重制约下,发展电动汽车已经成为解决能源环境问题的新途径。为适应未来电动汽车快速发展的需要及充电设施的合理规划布局,电动汽车及充电设施的需求预测就显得尤为重要。文中分析了影响齐齐哈尔市电动汽车发展规模的影响因素,结合国内其他省市的经验,考虑齐齐哈尔市实际情况,对齐齐哈尔市中心城区的电动汽车和充电设施发展规模进行了合理预测。     

Electric vehicle charging station locations: Elastic demand,station congestion,and network equilibrium

Battery-only electric vehicles (BEVs) generally offer better air quality through lowered emissions, along with energy savings and security. The issue of long-duration battery charging makes charging-station placement and design key for BEV adoption rates. This work uses genetic algorithms to identify profit-maximizing station placement and design details, with applications that reflect the costs of installing, operating, and maintaining service equipment, including land acquisition. Fast electric vehicle charging stations (EVCSs) are placed across a congested city's network subject to stochastic demand for charging under a user-equilibrium traffic assignment. BEV users’ station choices consider endogenously determined travel times and on-site charging queues. The model allows for congested-travel and congested-station feedback into travelers’ route choices under elastic demand and BEV owners’ station choices, as well as charging price elasticity for BEV charging users.Boston-network results suggest that EVCSs should locate mostly along major highways, which may be a common finding for other metro settings. If 10% of current EV owners seek to charge en route, a user fee of $6 for a 30-min charging session is not enough for station profitability under a 5-year time horizon in this region. However, $10 per BEV charging delivers a 5-year profit of $0.82 million, and 11 cords across 3 stations are enough to accommodate a near-term charging demand in this Boston-area application. Shorter charging sessions, higher fees, and/or allowing for more cords per site also increase profits generally, everything else constant. Power-grid and station upgrades should keep pace with demand, to maximize profits over time, and avoid on-site congestion.     

高速公路合理收费标准确定方法研究

随着投入运营的时间增加,高速公路运营管理中的矛盾日益突出,如何确定科学合理的道路收费率,成为道路经营者普遍关注的问题。在收费标准基本模型的基础上提出了收费标准方案评估模型,利用判断矩阵选择最佳方案,并制定了相应的收费标准的动态调整方法。     

Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet.This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.