快速路平曲线半径对交通排放的影响分析

本文主要研究了快速路平曲线半径与交通特征参数对车速和交通排放的影响。根据合肥市四条典型快速路平曲线路段实测数据,建立了断面车速预测模型,并选择COPERT Ⅳ排放模型对合肥市轻型机动车排放因子进行测算。基于模型模拟数据,分析了行驶速度与平曲线半径对排放因子的影响,拟合了污染物CO排放因子与速度的函数关系。最后基于断面车速预测模型,建立了合肥市平曲线路段轻型车污染物排放量预测模型,并进行实例计算。     

香港典型山区道路汽车行驶工况污染物排放研究

通过安装车载测试系统收集香港港岛山区路段正常行驶工况下尾气中的CO、NOx、HC等污染物和油耗并辅助计算机软件进行分析。研究得出,山区道路设计、地形地貌和驾驶习惯对车辆油耗以及CO、NOx和HC排放有直接关系。可以通过坡道加宽、坡道延长、减少坡道红绿灯等措施减少车辆在山区道路行驶过程中速度变化频率,从而减少油耗以及CO、NOx和HC排放。     

公路与水路运输节能减排量化分析和比较

通过研究汽车与船舶的油耗特点,结合燃油种类与CO_2排放量的关系,建立了计算公路运输与水路运输单位耗油量与单位CO_2排放量的数学模型。通过敏感性分析,研究车速或航速与载货率对车辆或船舶单位CO_2排放量的影响。以从厦门港到盐田港的干散货运输为例,做了节能减排计算分析,并将结果与用其他方法获得的结果对比。在此基础上,对公路运输与水路运输节能减排做了敏感性分析研究,可以计算出能够体现水路运输节能减排优势的临界货运量。结果表明本文中介绍的方法能够为核算、比较公路运输与水路运输的节能减排效果提供更科学的量化参考。     

汽车生产企业CO2排放评价方法研究

目前广泛采用的汽车生产企业车均CO2排放量评价方法没有区分车辆类型及其整备质量大小,故其难以准确评价不同类型及其整备质量车辆的CO2排放.为了提高评价汽车产品生产过程产生的CO2排放水平的准确性,提出了一个新的评价参数—单位能耗CO2排放因子,以弥补车均CO2排放量评价方法的不足.结果表明:单位能耗CO2排放因子可由汽车产品生产过程中的能源消耗量和其产生的CO2排放量二者的拟合直线求出,其值不受汽车企业生产汽车产品型号、种类和工艺等的影响;其值越小,CO2排放量越少;单位能耗CO2排放因子的大小与能源和耗能工质中清洁能源使用比例密切相关,清洁能源使用比例越大,其值越小.     

城市道路交通标志设计参数研究

基于城市道路中车辆行驶模型的复杂多样性,研究驾驶员的行驶特性和视觉特征,建立相应的行驶模型和视觉模型,计算在不同的道路条件、不同车速下满足城市道路交通需求的交通标志设计的几个主要参数.     

城市道路汽车行驶工况及排放污染物的测试和分析研究

为了适应欧Ⅲ排放法规的需要,针对西安市区城市道路上正常行驶汽车的工况及排放情况进行研究,通过采用GPS测速系统和IPEXD便携式排放测量仪,快速、准确的获取汽车正常行驶过程中的即时速度及瞬间排放污染物等客观数据,进而具体分析汽车在城市道路上的行驶工况及排放情况,最终计算得出西安市城市道路上行驶汽车的相应工况、即时速度与瞬间排放之间的关系。其中,本文主要研究以汽油为燃料的车辆在城市道路条件下行驶时尾气排放中的HC、CO、NOX三种污染物,客观分析并确定日常城市道路条件下汽车行驶工况对其排放的相关影响。     

轻型汽车二氧化碳排放与控制措施的探讨

文章阐述了发动机燃烧过程中二氧化碳(CO)2的生成原理,介绍了国内外对机动车CO2排放的法规控制情况。结合欧盟提出的机动车CO2排放120g/km的目标,对目前市场上常见的轻型在用车辆进行了CO2的排放抽样检测,并对检测结果进行了数据分析。最后对国内外针对CO2排放控制措施进行了探讨。     

利用MOBILE6.2模型预测高速公路路段综合排放因子

采用美国环保局(EPA)的MOBILE6.2模型,结合滨州市的实际情况,对滨州市的两个高速公路站,黄河大桥站和无棣站,通过车辆数目、车型进行统计分析,从而得到两个高速公路站点排放因子数据,其计算结果能为高速公路机动车污染控制提供依据,同时也提出了一种新的使用模型来计算高速公路路段排放因子的方法。     

燃油添加剂对汽车排放性能影响分析研究

国内对于燃油添加剂效果的分析研究较少,文章为了探讨某型燃油添加剂对在用车排放和性能的影响,依据GB18285-2005《点燃式发动机汽车排气污染物排放限值及测量方法》,针对不同行驶里程的汽车,选用双怠速法检测汽车使用某型号燃油添加剂前后的排放变化,并对油耗和动力性进行简单测评。结果表明,使用燃油添加剂和选择合适标号燃油能有效降低汽车污染物排放及油耗,对车辆使用节能减排具有较为重要意义。     

道路交通油耗与排放的微观测算模型综述

道路交通的能耗排放比重大,增长快。微观油耗和排放模型是评价交通管理措施的节能减排效果的重要手段。本文从模型结构、输入变量、测算原理等方面对各类机动车油耗排放模型及其研究进展进行了分析。分析发现,基于机动车功率需求变量的模型开发方法是目前主流方法,尤其是基于机动车比功率的测算方法,是未来油耗排放模型的研究与应用方向。     

碳达峰背景下雄安电动货车减碳量测评研究

随着核电、风电等清洁能源的发展,纯电动车辆将成为绿色交通的主要发展方向。本文基于油耗法的方法原理构建雄安新区电动货车减碳测算模型,通过减碳测算模型估算雄安新区电动货车的减碳量,并对其产生的经济效益进行估算和社会效益进行分析。研究结果表明,雄安新区电动货车的年减碳量可观,减排效益明显。积极推进雄安新区物流运输工具能源消费结构的转型升级,不仅符合雄安新区建设的发展要求,而且对于加快区域碳达峰目标的实现具有至关重要的作用。     

Experimental evaluation of Heavy Duty Vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions

Exhaust emissions and fuel consumption of Heavy Duty Vehicles (HDVs) in urban and port areas were evaluated through a dedicated investigation. The HDV fleet composition and traffic driving from highways to the maritime port of Genoa and crossing the city were analysed. Typical urban trips linking highway exits to port gates and HDV mission profiles within the port area were defined. A validation was performed through on-board instrumentation to record HDV instantaneous speeds in urban and port zones. A statistical procedure enabled the building-up of representative speed patterns. High contrasts and specific driving conditions were observed in the port area. Representative speed profiles were then used to simulate fuel consumption and emissions for HDVs, using the Passenger car and Heavy duty Emission Model (PHEM). Complementary estimations were derived from Copert and HBEFA methodologies, allowing the comparison of different calculation approaches and scales. Finally, PHEM was implemented to assess the performances of EGR or SCR systems for NO_X reduction in urban driving and at very low speeds.The method and results of the investigation are presented. Fuel consumption and pollutant emission estimation through different methodologies are discussed, as well as the necessity of characterizing very local driving conditions for appropriate assessment.     

Driving cycles that reproduce driving patterns,energy consumptions and tailpipe emissions

This study presents the Energy Based Micro-trip (EBMT) method, which is a new method to construct driving cycles that represent local driving patterns and reproduce the real energy consumption and tailpipe emissions from vehicles in a given region. It uses data of specific energy consumption, speed, and percentage of idling time as criteria of acceptable representativeness. To study the performance of the EBMT, we used a database of speed, fuel consumption, and tailpipe emissions (CO₂, CO, and NO_x), which was obtained monitoring at 1 Hz, the operation of 15 heavy-duty vehicles when they operated within different traffic conditions, during eight months. The speed vs. time data contained in this database defined the local driving pattern, which was described by 19 characteristic parameters (CPs). Using this database, we ran the EBMT and described the resulting driving cycle by 19 characteristics parameters (CPs*). The relative differences between CPs and CPs* quantified how close the obtained driving cycle represented the driving pattern. To observe tendencies of our results, we repeated the process 1000 times and reported the average relative difference (ARD) and the interquartile range (IQR) of those differences for each CP.. We repeated the process for the case of a traditional Micro-trip method and compared to previous results. The driving cycles constructed by the EBMT method showed the lowest values of ARDs and IQRs, meaning that it produces driving cycles with the highest representativeness of the driving patterns, and the best reproduction of energy consumption, and tailpipe emissions.     

Estimating fuel consumption and emissions based on reconstructed vehicle trajectories

Microscopic emission models are widely used in emission estimation and environment evaluation. Traditionally, microscopic traffic simulation models and probe vehicles are two sources of inputs to a microscopic emission model. However, they are not effective in reflecting all vehicles' real‐world operating conditions. Using each vehicle's spot speed data recorded by detectors, this paper provides a new method to estimate all vehicles' real‐world activities data. These data can then be used as inputs to a microscopic emission model to estimate vehicle fuel consumption and emissions. The main task is to reconstruct trajectory of each vehicle and calculate second‐by‐second speed and acceleration from the activities data. The Next Generation Simulation dataset and the Comprehensive Modal Emissions Model are used in this study to calculate and analyze the emission results for both lane‐level and link‐level. The results showed that using the proposed method for estimating vehicle fuel consumption and emissions is promising. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic

Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre-timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang-singular-bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub-optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro-simulations. The results show that SAS-equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS-equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.     

Eco-driving: An economic or ecologic driving style?

In this work the trade-off between economic, therefore fuel saving, and ecologic, pollutant emission reducing, driving is discussed. The term eco-driving is often used to refer to a vehicle operation that minimizes energy consumption. However, for eco-driving to be environmentally friendly not only fuel consumption but also pollutant emissions should be considered. In contrast to previous studies, this paper will discuss the advantages of eco-driving with respect to improvements in fuel consumption as well as pollutant gas emissions. Simulating a conventional passenger vehicle and applying numerical trajectory optimization methods best vehicle operation for a given trip is identified. With hardware-in-the-loop testing on an engine test bench the fuel and emissions are measured. An approach to integrate pollutant emission and dynamically choose the ecologically optimal gear is proposed.     

Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment

This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO₂ emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.     

Electric vehicles’ energy consumption estimation with real driving condition data

The use of electric vehicles (EVs) is viewed as an attractive option to reduce CO₂ emissions and fuel consumption resulted from transport sector, but the popularization of EVs has been hindered by the cruising range limitation and the charging process inconvenience. Energy consumption characteristics analysis is the important foundation to study charging infrastructures locating, eco-driving behavior and energy saving route planning, which are helpful to extend EVs’ cruising range. From a physical and statistical view, this paper aims to develop a systematic energy consumption estimation approach suitable for EV actual driving cycles. First, by employing the real second-by-second driving condition data collected on typical urban travel routes, the energy consumption characteristics analysis is carried out specific to the microscopic driving parameters (instantaneous speed and acceleration) and battery state of charge (SOC). Then, based on comprehensive consideration of the mechanical dynamics characteristics and electric machine system of the EVs, a set of energy consumption rate estimation models are established under different operation modes from a statistical perspective. Finally, the performance of proposed model is fully evaluated by comparing with a conventional energy consumption estimation method. The results show that the proposed modeling approach represents a significant accuracy improvement in the estimation of real-world energy consumption. Specifically, the model precision increases by 25.25% in decelerating mode compared to the conventional model, while slight improvement in accelerating and cruising mode with desirable goodness of fit.     

Development of real-world driving cycle: Case study of Pune,India

The critical component of all emission models is a driving cycle representing the traffic behaviour. Although Indian driving cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban driving cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the driving cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the driving cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The driving cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing driving cycles.