欧洲电动车市场大幅度增长

欧洲作为世界第二大电动车市场正在迎头追赶中国。中美电动车销量下滑的同时,欧洲电动车销量逐年增长,德国今年有望超过美国成为世界第三大电动车市场。德国媒体报道,位于德国贝尔吉施格拉德巴赫的德国CAM汽车管理中心最新研究显示。     

郑州交运五大建议

建议上级部门呼吁政府加大纯电动车重视力度。郑州市作为国家推广纯电动车试点城市之一。推广使用纯电动车，一切取决于充、换电站的投资建设与布局。充、换电站的投资建设与布局不先行，没有充、换电站作为保障，纯电动车推广使用将会很被动。
　　建议政府推出相关政策，改变经营者观望态度，支持推广使用天然气车辆工作。商务部、财政部连续两年联合下发报废车辆补贴通知，始终没有将公路客运车辆列入其中。只有加快老旧车辆更新，才能有效使天然气车辆或电动车辆替代柴、汽油车辆。郑州交运做了很多工作，计划让经营者提前更新车辆，但阻力重重。     

“公交最慢”让公交优先情何以堪？

如果下班回家成为一件很耗时耗力的事，人们的快乐指数自然会打折扣、目前，广西《南国早报》记者兵分三路．使用电动车、公交车、小车在南宁街头进行体验，同样的路程，电动车最快，用时35分钟；小车次之，用时70分钟，公交车最慢，用时73分钟.     

纯电动公交车示范项目

在本项目中,临沂市公交总公司采用了铝合金全承载车身、三相异步电机驱动系统、磷酸铁锂动力电池组的纯电动车作为公交车,依靠当地电动汽车生产企业提供的车辆技术支持,从2010年2月起,先后在5条公交线路上进行了SDL6120EVG纯电动车的示范运行。通过合理使用电池、强化电池保养、加强驾驶员培训等管理手段。到目前为止,单车平均运行2万多公里,车辆运行平稳,节能环保效果良好。     

中国电动汽车人:2017,撸起袖子加油干

正麦肯锡合伙人理查德·福斯特曾提出,汽车产业已经进入了创造性破坏时期,守成者将面临巨大的挑战。正如中国电动汽车百人会理事长陈清泰所说:"今天的电动车之热,并非由于传统能源的枯竭,而是因为它有很强大的正外部性。"刚刚过去的2016年,很多企业由"试试看",转向了"铁了心地干",把电动车发展放到公司战略的中心地位,由燃油车的引进、消化、吸收,转向开放条件下的自主创新,对研发重视的程度、研发投入的力度前所未有。     

宇通携新能源 “双雄”闪耀世界电动大会

<正>宇通客车参加了11月4日的深圳电动车巡游以及11月5日至8日的世界电动车展会,该展会全称世界纯电动车、混合动力车和燃料电池车大会暨展览会,是由世界电动车协会主办,目前全球规模最大、规格最高、最具影响力的电动汽车和新能源汽车大会和展览     

井筒与输油管线的物理防蜡技术综述

文中通过对大量相关文献的分析总结,对目前各大油田常用的物理防蜡技术的发展历史、工作原理、适用环境以及使用条件进行了归纳、介绍,并对这些防蜡技术在实验室中的实验结果和近年来油田使用后的效果进行了分析总结.这些防蜡技术的分类介绍为各大油田在实践中面临不同采油条件和原油组分时筛选防蜡方法提供了理论性的依据,也为防蜡技术的进一...     

加快农村客运基础设施建设构建完善的农村客运网络——广西壮族自治区贺州市交通局

广西壮族自治区贺州市地处桂粤湘三省(区)的结合部,既是西南地区东进粤港澳的一个重要“东通道”,又是西部地区接受粤港澳经济辐射和承接东部地区产业转移的前沿。为发挥其优越的区位条件和丰富的资源条件,2005年以来,贺州市通过开展二轮共六年交通基础设施建设大会战,进一步加     

大型机场电动汽车应用研究

随着世界范围内"绿色机场"概念的兴起,为了解决机场内传统车辆的应用所带来的污染问题,中国政府倡议逐步将机场的使用车辆改换为纯电动汽车。但由于我国发展纯电动汽车起步较晚,国内并未有可供直接参考的成熟范例。本研究以机场现有的传统车辆及相关设施为研究对象,经过近半年的时间,对首都机场区域进行现场调研,参考国外机场中纯电动汽车及其他新能源汽车的改换案例、充电场站的建设情况及电动车电池的处理方式等,结合国内其他机场有关纯电动车辆改换等方面的经验,探索适用于首都机场的纯电动汽车改换与充电场站建设的初步方案与运营模式。研究结果以飞行区为例,提出了初步的纯电动汽车改换方案,并对充电场站的建设与运营指明了可供选择的方向,为进一步研究打下了基础。     

地层注浆加固对隧道与地下水相互作用过程中的“双赢”影响效应分析

目前山岭隧道大多采用的"半包"防水方式,可较为有效地降低衬砌背后水压荷载作用,但同时造成地下水资源的大量流失。文章通过建立隧道瞬态渗流模型,分析了"半包"防水条件下隧道施工及运营过程中隧道排水流量及水位变化规律,以及运营过程中衬砌背后水压力分布规律;研究了帷幕注浆和径向注浆两种方式不同工况下不同注浆范围及参数对排水流量、水位变化及衬砌水压荷载的影响效应。数值分析结果和工程实践表明,地层注浆加固既可降低衬砌背后水压荷载,又可减少地下水资源流失量,从而可以实现隧道与地下水相互作用过程中的"双赢"影响效应。文章最后对"半包"防水条件下仰拱是否设置排水系统进行了分析探讨。     

Electric vehicles for greenhouse gas reduction in China: A cost-effectiveness analysis

There have been ongoing debates over whether battery electric vehicles contribute to reducing greenhouse gas emissions in China’s context, and if yes, whether the greenhouse gas emissions reduction compensates the cost increment. This study informs such debate by examining the life-cycle cost and greenhouse gas emissions of conventional vehicles, hybrid electric vehicles and battery electric vehicles, and comparing their cost-effectiveness for reducing greenhouse gas emissions. The results indicate that under a wide range of vehicle and driving configurations (range capacity, vehicle use intensity, etc.), battery electric vehicles contribute to reducing greenhouse gas emissions compared with conventional vehicles, although their current cost-effectiveness is not comparable with hybrid electric vehicles. Driven by grid mix optimization, power generation efficiency improvement, and battery cost reduction, the cost-effectiveness of battery electric vehicles is expected to improve significantly over the coming decade and surpass hybrid electric vehicles. However, considerable uncertainty exists due to the potential impacts from factors such as gasoline price. Based on the analysis, it is recommended that the deployment of battery electric vehicles should be prioritized in intensively-used fleets such as taxis to realize high cost-effectiveness. Technology improvements both in terms of power generation and vehicle electrification are essential in improving the cost-effectiveness of battery electric vehicles.     

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

Shared autonomous vehicles (SAVs) are the next major evolution in urban mobility. This technology has attracted much interest of car manufacturers aiming at playing a role as transportation network companies (TNCs) and carsharing agencies in order to gain benefits per kilometer and per ride. It is predicted that the majority of future SAVs would most probably be electric. It is therefore important to understand how limited vehicle range and the configuration of charging infrastructure will affect the performance of shared autonomous electric vehicle (SAEV) services. In this study, we aim to explore the impacts of charging station placement, charging types (including normal and rapid charging, and battery swapping), and vehicle battery capacities on service efficiency. We perform an agent-based simulation of SAEVs across the Rouen Normandie metropolitan area in France. The simulation process features impact assessment by considering dynamic demand responsive to the network and traffic.Research results suggest that the performance of SAEVs is strongly correlated with the charging infrastructure. Importantly, faster charging infrastructure and placement of charging locations according to minimized distances between demand hubs and charging stations result in a higher performance. Further analysis indicates the importance of dispersing charging stations across the service area and its impacts on service effectiveness. The results also underline that SAEV battery capacity has to be selected carefully such that to avoid the overlaps between demand and charging peak times. Finally, the simulation results show that the performance indicators of SAEV service are significantly improved by providing battery swapping infrastructure.     

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

In this paper, we study battery capacity design for battery electric vehicles (BEVs). The core of such design problems is to find a good tradeoff between minimizing the capacity to reduce financial costs of drivers and increasing the capacity to satisfy daily travel demands. The major difficulty of such design problems lies in modeling the diversity of daily travel demands. Based on massive trip records of taxi drivers in Beijing, we find that the daily vehicle miles traveled (DVMT) of a driver (e.g., a taxi driver) may change significantly in different days. This investigation triggers us to propose a mixture distribution model to describe the diversity in DVMT for various driver in different days, rather than the widely employed single distribution model. To demonstrate the merit of this new model, we consider value-at-risk and mean-variance battery capacity design problems for BEV, with respect to conventional single and new mixture distribution models of DVMT. Testing results indicate that the mixture distribution model better leads to better solutions to satisfy various drivers.     

Stochastic frontier analysis of excess access to mid-trip battery electric vehicle fast charging

This study aims to explore how factors including charging infrastructure and battery technology associate the way people currently charge their battery electric vehicles, as well as to explore whether good use of battery capacity can be encouraged. Using a stochastic frontier model applied to panel data obtained in a field trial on battery electric vehicle usage in Japan, the remaining charge when mid-trip fast charging begins is treated as a dependent variable. The estimation results obtained using four models, for commercial and private vehicles, respectively, on working and non-working days, show that remaining charge is associated with number of charging stations, familiarity with charging stations, usage of air-conditioning or heater, battery capacity, number of trips, Vehicle Miles of Travel, paid charging. However, the associated factors are not identical for the four models. In general, EVs with high-capacity batteries are initiated at higher remaining charge, and so are the mid-trip fast charging events in the latter period of this trial. The estimation results also show that there are great opportunities to encourage more efficient charging behavior. It appears that the stochastic frontier modeling method is an effective way to model the remaining charge at which fast-charging should be initiated, since it incorporates trip and vehicle characteristics into the estimation process to some extent.     

并联混合动力汽车改进模糊控制策略研究

本文提出一种兼顾电池SOC限值方法的混合动力汽车多种群遗传模糊控制策略。引入模糊逻辑控制以增强整车控制系统鲁棒性、实时性;用多种群遗传算法对模糊变量隶属度函数进行优化,使在模糊逻辑控制下整车燃油消耗得到降低;使用电池SOC限值方法避免电池在SOC过低时继续放电。利用matlab平台联合advisor软件进行联合仿真实验,仿真结果表明多种群遗传模糊模糊控制策略能够比advisor软件默认的电机辅住控制策略燃油经济性提高6.96%的情况,SOC限值方法使电池工作在更加合理的SOC值区间范围内,有效保护电池。     

Impact of convenient away-from-home charging infrastructure

This work uses market analysis and simulation to explore the potential impact of workplace and similarly convenient away-from-home charging infrastructure (CAFHCI) in reducing US light duty vehicle (LDV) petroleum use and greenhouse gas emissions. The ParaChoice model simulates the evolution of LDV sales, fuel use, and emissions through 2050, considering consumer responses to different options of electric range extension made available through CAFHCI, fraction of the population with access, and delay in infrastructure implementation. Results indicate that providing a greater fraction of the population access to CAFHCI at level 1 charging rates for a full workday (∼16–20 miles of range extension) may lead to more petroleum use reduction than providing level 2 charging to a lesser fraction. This result holds even considering the fraction of the population without at-home charging. 2050 battery electric vehicle sales increase 40% (85%) if the entire population is guaranteed daily access to one full workday of level 1 CAFHCI (half a workday of level 2, ∼80 miles of range extension). Plug-in hybrid sales increase when CAFHCI enables range extension below 20–40 miles/day, most significantly in households without at-home charging capability. Faster CAFHCI may decrease plug-in hybrid sales as less expensive BEVs become attractive to a greater fraction of the market.     

Charge timing choice behavior of battery electric vehicle users

This paper aims to examine choice behavior in respect of the time at which battery electric vehicle users charge their vehicles. The focus is on normal charging after the last trip of the day, and the alternatives presented are no charging, charging immediately after arrival, nighttime charging, and charging at other times. A mixed logit model with unobserved heterogeneity is applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Estimation results, obtained using separate models for commercial and private vehicles, suggest that state of charge, interval in days before the next travel day, and vehicle-kilometers to be traveled on the next travel day are the main predictors for whether a user charges the vehicle or not, that the experience of fast charging negatively affects normal charging, and that users tend to charge during the nighttime in the latter half of the trial. On the other hand, the probability of normal charging after the last trip of a working day is increased for commercial vehicles, while is decreased for private vehicles. Commercial vehicles tend not to be charged when they arrival during the nighttime, while private vehicles tend to be charged immediately. Further, the correlations of nighttime charging with charging immediately and charging at other times reveal that it may be possible to encourage charging during off-peak hours to lessen the load on the electricity grid. This finding is supported by the high variance for the alternative of nighttime charging.     

Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data

The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline and electricity. Moreover, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist’s daily travel distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3–18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. On the other hand, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.     

Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.     

Effects of battery chemistry and performance on the life cycle greenhouse gas intensity of electric mobility

Lithium traction batteries are a key enabling technology for plug-in electric vehicles (PEVs). Traction battery manufacture contributes to vehicle production emissions, and battery performance can have significant effects on life cycle greenhouse gas (GHG) emissions for PEVs. To assess emissions from PEVs, a life cycle perspective that accounts for vehicle production and operation is needed. However, the contribution of batteries to life cycle emissions hinge on a number of factors that are largely absent from previous analyses, notably the interaction of battery chemistry alternatives and the number of electric vehicle kilometers of travel (e-VKT) delivered by a battery. We compare life cycle GHG emissions from lithium-based traction batteries for vehicles using a probabilistic approach based on 24 hypothetical vehicles modeled on the current US market. We simulate life-cycle emissions for five commercial lithium chemistries. Examining these chemistries leads to estimates of emissions from battery production of 194–494 kg CO₂ equivalent (CO₂e) per kWh of battery capacity. Combined battery production and fuel cycle emissions intensity for plug-in hybrid electric vehicles is 226–386 g CO₂e/e-VKT, and for all-electric vehicles 148–254 g CO₂e/e-VKT. This compares to emissions for vehicle operation alone of 140–244 g CO₂e/e-VKT for grid-charged electric vehicles. Emissions estimates are highly dependent on the emissions intensity of the operating grid, but other upstream factors including material production emissions, and operating conditions including battery cycle life and climate, also affect life cycle GHG performance. Overall, we find battery production is 5–15% of vehicle operation GHG emissions on an e-VKT basis.