电动汽车充电桩设施检测能力建设的研究

电动汽车是当前汽车研究领域的重点发展方向,为适应电动汽车产业的发展趋势,当前社会基础设施建设开始重点建设能为电动汽车充电的充电桩,由于充电桩的基础设施建设技术还未成熟,在进行基础设施建设的过程中发现,部分充电桩存在安全隐患或无法充电等故障,因此专家开始研究充电桩设施检测技术,通过应用充电桩设施检测技术,构建设施检测能力,继续优化充电桩及相关技术。本文对电动汽车充电桩设施检测的能力建设进行了分析研究,在进行研究的过程中针对充电桩设施检测对象、检测设备、检测方法进行了全面研究,以上多种要素是构建充电桩设施检测能力建设的关键,对于优化及推动充电桩设施检测具有重要作用。     

智慧车联网:有序充电智慧互动

随着社会电气化水平的提高,新能源汽车成为人们的新宠儿. 如何快速寻找充电桩,这个曾经困扰很多车主的问题,已经不再棘手.点开"e充电"APP,附近的充电桩一目了然,充电桩类型、目前使用状态等一一显示. 车主享受到的便捷充电服务得益于国家电网持续建设完善的智慧车联网平台,该平台是全球覆盖面最广、数量最多、服务能力最强的电动汽车运营服务平台.截至2020年年底,智慧车联网平台累计接入各类运营主体充电桩103万个,覆盖全国93％的公共充电桩.     

江苏在全国率先实现充电桩乡镇全覆盖

2021年1月19日,记者从国网江苏省电力有限公司了解到,江苏全省1073个乡镇新建设电动汽车充电桩4838个,在全国率先实现充电桩乡镇全覆盖,破解乡镇居民电动汽车充电难题.     

中国电动汽车百人会“充电安全与效率研讨会”召开 提高电动汽车充电设施的安全性

正随着我国电动汽车市场化进程的加快,政府推出了一系列推动充电基础设施建设的政策和措施,取得了积极的成果,但也出现了一些问题。为了系统研究充电基础设施推广中发生的问题并寻找解决办法,中国电动汽车百人会于4月28日召开了"电动汽车热点问题系列研讨会II:充电安全与效率",邀请了国内外充电设施相关企业代表和研究机构相关专业人员以及国家相关部门的负责人,从技术开发、标准制定、行业准入、市场推广、用户使用等角度,讨论了提高电动汽车充电安全性的技术、充电     

数字·交通

<正>104项10月,2015年全国交通运输职业教育教学指导委员会交通运输职业教育科研项目确定。2015年交通运输职业教育科研项目共立项104项,其中重点项目19项、一般项目85项。100%近日,国务院常务会议部署加快建设电动汽车充电基础设施,并将发布实施《加快电动汽车充电基础设施建设的指导意见》。会议认为,建设电动汽车充电基础设施,是发展新能源汽     

直击充电基础设施痛点,政策加持下如何破茧?

正作为整个新能源汽车最下游的一环——充电基础设施,却是决定着电动汽车跑起来、跑出去的关键一步。但是目前,这一关键之步仍存在诸多痛点。"我们车站只有40多个充电桩,近200辆电动公交车用起来十分紧张。"山东一客运企业负责人向记者抱怨:"充电桩紧缺,为了满足运营需求,车辆一闲下来就得进行补电。"类似的现象在很多地方都有出现。不光是商用电动车,还有普通的乘用电动车,都存在着"充电难"、"等桩难"等问题。     

公共充电站在基础设施建设中的规划与研究

随着我国社会经济发展水平的不断提高,汽车使用量持续攀高。大力发展新能源汽车,能够促进资源充分利用,减少汽车尾气排放,对保障能源安全、促进节能减排、防治大气污染、推动我国能源可持续发展具有重要意义。随着电动汽车的推广使用,电动汽车对充电站的基础设施建设以及服务网络的完善等需求日益紧迫。充电基础设施的建设,用以满足电动汽车的发展需求,并以充电设施、充电系统的适度超前发展引导电动汽车的业务发展。     

2017电动汽车充电基础设施建设发展论坛顺利举办 桩建设,与新能源汽车发展同样重要!

正3月29日,2017中国国际清洁能源博览会电动汽车充电基础设施建设发展论坛在中国国际展览中心成功举办。与会各方在会中发表了对于电动汽车充电基础设施建设发展的相关建议与看法。近日,国家能源局、国资委、国管局近日联合下发关于《加快单位内部电动汽车充电基础设施建设》的通知,其中提出,到2020年公共机构新建和既有停车场要规划建设配备充电设施比例不低于10%;中央国家机关及所属在京公共机构比例不低于30%;在京中央企业比例力争不低于30%。2016年12月,北京市地方标准《电动     

基于聚类分析法的高速公路服务区电动汽车充电设施布局研究

交通运输行业推广应用电动汽车的有利于推动节能减排,高速公路服务区充电基础设施布局建设的严重滞后是制约推广应用的因素之一。文章分析了高速公路服务区及充电设施特征,提出了高速公路充电基础设施布局的思路和方法,结合广东实际给出了布局方案,提出了加快发展的意见建议。     

城市充电基础设施规划的思考

城市交通是实现碳达峰、碳中和的重要领域，电动汽车是节能减排的重要抓手，优化充电基础设施布局是当务之急。本文针对当前充电基础设施发展存在的问题，提出城市充电基础设施的规划方法，宏观层面分析城市组团功能确定建设优先级，中观层面提出“点—线—面”结合的规划方法和合理的服务半径，建设充电基础设施网络，微观层面以工作、居住、游憩、交通四大基本城市活动，满足不同场景充电需求。最后提出落实用地保障、建设时序等保障措施。     

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.     

Public charging infrastructure for plug-in electric vehicles: What is it worth?

Lack of charging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Public charging infrastructure has tangible and intangible value, such as reducing range anxiety or building confidence in the future of the PEV market. Quantifying the value of public charging infrastructure can inform analysis of investment decisions and can help predict the impact of charging infrastructure on future PEV sales. Estimates of willingness to pay (WTP) based on stated preference surveys are limited by consumers’ lack of familiarity with PEVs. As an alternative, we focus on quantifying the tangible value of public PEV chargers in terms of their ability to displace gasoline use for PHEVs and to enable additional electric (e−) vehicle miles for BEVs, thereby mitigating the limitations of shorter range and longer recharging time. Simulation studies provide data that can be used to quantify e-miles enabled by public chargers and the value of additional e-miles can be inferred from econometric estimates of WTP for increased vehicle range. Functions are synthesized that estimate the WTP for public charging infrastructure by plug-in hybrid and battery electric vehicles, conditional on vehicle range, annual vehicle travel, pre-existing charging infrastructure, energy prices, vehicle efficiency, and household income. A case study based on California’s public charging network in 2017 indicates that, to the purchaser of a new BEV with a 100-mile range and home recharging, existing public fast chargers are worth about $1500 for intraregional travel, and fast chargers along intercity routes are valued at over $6500.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.     

电动汽车充电桩电能计量相关问题研究

根据中央关于加快"新基建"的决策部署,国内将大力推动充电桩建设。作为实施强制管理的计量器具,充电桩的准确计量可确保电能交易公平公正,支撑国家经济社会高质量发展,满足人民低碳绿色出行需求。文中针对电动汽车充电桩电能计量相关问题进行了探讨,首先分别采集了运行全过程中工频交流输入侧和直流输出侧电压与电流的实时波形曲线,其次利用matlab软件和FFT运算对波形数据进行了详细分析,最后验证了现有符合国家标准的交/直流电能表均可满足充电桩计量要求。     

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. This study integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.     

Siting charging stations for electric vehicle adoption in shared autonomous fleets

The transportation sector is undergoing three revolutions: shared mobility, autonomous driving, and electrification. When planning the charging infrastructure for electric vehicles, it is critical to consider the potential interactions and synergies among these three emerging systems. This study proposes a framework to optimize charging infrastructure development for increasing electric vehicle (EV) adoption in systems with different levels of autonomous vehicle adoption and ride sharing participation. The proposed model also accounts for the pre-existing charging infrastructure, vehicle queuing at the charging stations, and the trade-offs between building new charging stations and expanding existing ones with more charging ports.Using New York City (NYC) taxis as a case study, we evaluated the optimum charging station configurations for three EV adoption pathways. The pathways include EV adoption in a 1) traditional fleet (non-autonomous vehicles without ride sharing), 2) future fleet (fully autonomous vehicles with ride sharing), and 3) switch-over from traditional to future fleet. Our results show that, EV adoption in a traditional fleet requires charging infrastructure with fewer stations that each has more charging ports, compared to the future fleet which benefits from having more scattered charging stations. Charging will only reduce the service level by 2% for a future fleet with 100% EV adoption. EV adoption can reduce CO₂ emissions of NYC taxis by up to 861 Tones/day for the future fleet and 1100 Tones/day for the traditional fleet.     

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.     

Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet

Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that (1) collective vehicle parking “hotspots” are good indicators for charging demand; (2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; (3) with current grid mix, emissions of CO₂, PM, SO₂, and NO_x will increase with taxi electrification; and (4) power demand for public taxi charging has peak load around noon, overlapping with Beijing’s summer peak power.     

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.     

Locating charging infrastructure for electric buses in Stockholm

Charging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.