Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.     

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.     

汽车排放污染及净化对策研究

阐述汽车排放污染物的成分及其危害,介绍新排放法规Ⅰ型、Ⅲ型、Ⅳ型试验限值,重点分析汽车的5项排放影响因素及其净化措施。     

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.     

Historic vehicles: an overview from a transport policy perspective

Historic vehicles (HVs) are the heritage of road transport that have surprisingly received little attention in the academic literature. This study presents an overview of the literature on HVs, focusing on the three topics that dominate the policy debate on transport: environmental, safety and congestion impacts. We observed that polluting emissions of HVs are per kilometre much higher (often a factor 5 or more) than those of moderns vehicles. The annual average mileage per vehicle per year of HVs is much lower than other vehicles. The lower active and passive safety levels of HVs are compensated by the way these vehicles are driven, resulting in the risk factors per kilometre being roughly equal or lower than other vehicles. The contribution of HVs to congestion is negligible. However, the transport policy discourse is divided on the topic of HVs. More comprehensive and effective laws and regulation are needed to protect this aspect of the heritage of road transport whilst concurrently avoiding or limiting the problems caused by them.     

铝合金耐压结构设计分析与试验验证

根据水下潜器总体性能要求,按照最小重量原则,结合规范和有限元方法设计工作潜深为500m的铝合金带加强筋形式的圆柱耐压舱,并通过压力筒加压试验,对Ansys有限元静力分析的精确度进行了验证,为耐压结构设计与校核提供重要参考.     

信号控制交叉口左转非机动车过街模式适用性

为分析信号控制交叉口两种左转非机动车过街模式（机动车和非机动车一体化模式以及行人和非机动车一体化模式）的适用条件,在动态交通条件下,定量分析两种模式对典型两相位和四相位信号控制交叉口通行效率和安全性的单独与综合影响。在通行效率方面,选取机动车通行能力作为评价指标;在安全性方面,引入一个可比选交通设计方案的指标——交通当量冲突。通过理论与实例分析,从通行效率和安全性两方面给出了两种模式的适用性。结果表明：两种模式在上述方面均有单独优势;两相位信号控制交叉口,在相应的左转非机动车和机动车交通量条件下两种模式均能同时提高交叉口通行效率和安全性;四相位信号控制交叉口,两种模式均不能同时起到积极作用。     

基于线网全寿命成本控制目标的车辆基地资源共享研究

目前国内各城市都在开展或已经开展资源共享的研究,其中车辆基地资源共享是线网资源共享的重要组成部分。根据《地铁设计规范》规定,车辆基地是保证地铁正常运营的后勤基地。车辆基地的设计范围包括车辆段、综合维修中心、物资总库和培训中心以及必要的办公、生活设施等,是地铁正常运营所必需的设备和设施。通过广泛调研,目前国内大多数城市车辆基地资源共享研究还存在一定的不足,仅集中在车辆大、架修的资源共享,缺乏对车辆基地综合维修中心、物资库等全系统的资源共享研究。创新性地提出基于线网全寿命成本控制目标的车辆基地资源共享研究思路,以沈阳地铁线网车辆基地为例,提出资源共享方案。除大、架修资源共享外,进一步研究物资存储、综合维修、特种车辆等多方面的资源共享,此外,对车辆基地各个单体的配置提出一定的标准化指标。在充分调研国内其他城市地铁建设经验、运营经验以及不同城市车辆基地建设规模的基础上,最终得到沈阳地铁车辆大架修资源共享、物资共享、综合维修共享、特种车辆共享以及场段配置标准化5个方面的研究结论。便于指导沈阳及类似城市地铁在新一轮的建设规划中以及未来线网规划调整中更加合理地分配车辆基地的物资、人力、设备、用地。     

基于行为识别的智能车纵向决策研究

针对智能车纵向决策问题,提出基于环境车辆偏离车道程度识别运动模式的方法;构建动态环境车辆横纵向轨迹预测模型,并求解;构建保持、先行、避让在内的决策集,提出基于预测轨迹的单个车辆决策方法,并基于所有动态环境车辆的决策结果在加速、减速和匀速3 种结果中做出综合决策. 实车实验表明：在直行、换道和转弯运动模式下轨迹预测平均误差分别为0.11,0.29,0.80 m,预测精度较高;复杂动态环境下,本文提供的纵向决策信息提升了智能车行驶的安全性和舒适性.     

道路交通安全水平预测的统计模型

为了评价和预测不同国家的道路交通安全水平,建立通用的统计模型,作者首先分析道路交通事故死亡率与人口规模、机动车保有量、道路里程以及人均GNP之间的相关关系,根据相关性选择影响道路交通事故的因素,并建立万车死亡率与人均GNP之间的函数关系.模型计算结果与校核数据对比分析表明,所提出的模型能够较好地评价和预测一个国家或地区的道路交通安全水平.模型对交通管理者提高交通安全性的科学决策具有重要指导意义.