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Modelling the Effect of Road Grade on the CO2 and NOx Emissions of a Passenger Car through a Real World-Urban Traffic Network

机译:通过真实世界 - 城市交通网络模拟道路等级对乘用车二氧化碳和氮氧化物排放的影响

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摘要

A Portable Emission Measurement System (PEMS) was utilised to record the on-road Carbon Dioxide (CO2) emission of a EURO 4 petrol vehicle over 48 test runs through an urban-traffic network. The tests were conducted over a 780 metre micro-scale road segment between Headingley and the City of Leeds, UK, with measurement on both the inbound (Section A) and outbound lanes (Section B). The monitored test runs were conducted under a range of traffic flow conditions from heavily congested to free-flowing traffic. Vehicle exhaust emission simulations using an instantaneous power-emission model have the capability to generate estimates of real-world vehicle emissions over micro-scale road sections. The Technical University of Graz’s (TUG) Passenger car and Heavy duty Emission Model (PHEM) was used to calculate a CO2 emission estimate for each of the 48 test runs through Sections A and B. The model CO2 emission estimates were then compared to the real-world PEMS emission measurements, to determine the accuracy of the modelling methodology. Whilst instrumented vehicles can adequately capture second-by-second (1Hz) absolute position and vehicle speed there is significant instrument error in the measurement of real-world elevation using a Global Positioning System (GPS) as part of a PEMS set-up. These errors make it very difficult to accurately calculate a 1Hz road grade with GPS systems. However, as road grade can have an important influence on engine power demand and hence fuel consumption and exhaust emission it is essential to include a representative road grade estimate for micro-scale emission estimation. Rather than using a GPS recorded elevation, this study developed a simple road grade estimation methodology which employs Geographic Information System (GIS) software to interpolate the elevation at each second of PEMS data from a 5-metre resolution Digital Terrain Map (DTM) derived from Light Detection And Ranging (LiDAR) data. The method applies an algorithm to compute the road grade from the LiDAR-GIS elevation values and vehicle speed, and alleviates errors resulting from absolute position measurement inaccuracy of the GPS at low speed. The addition of the LiDAR-GIS road grade to the PHEM modelling was found to improve the accuracy of the PHEM estimate of the PEMS measured real-world CO2 emission. From the 48 test runs the average PHEM estimate (including road grade) of the real-world measured CO2 emission through Section A was 93%, and through Section B was 94%. Of the total 96 test runs over Section A and B 91% of the PHEM estimates were between 80% and 110% of the PEMS recorded value. In further analysis, an assessment of the effect of road grade on both CO2 and NOx emission was conducted. Sections A and B were combined for each test run to form Segment AB, which has a net flat road grade. The PEMS recorded speed profiles for each of the test runs through sections A and B were input into PHEM and emission estimates generated under four road grade scenarios. The scenarios were formed by decreasing and exaggerating the LiDAR-GIS road grade for each second of data, multiplying it by coefficients of 0 (flat), 0.5 (half the grade), 1, and 2 (double the grade). The results indicate that assuming a flat profile in PHEM would result in an average underestimate of the segment emission by 2.7% for CO2 and 7.0% for NOx when calculated with road grade, and by 7.9% for CO2 and 20.4% for NOx were the road grade doubled. The method developed in this study provides a simple methodology for calculating 1Hz road grade, and has been shown to improve the modelling of CO2 emission for this data set. This research suggest that using the PHEM model with a LiDAR-GIS calculated road grade provides a practical method for accurately estimating real-world micro-scale emission. On-road emission monitoring by PEMS is scheduled to be introduced for Euro 6c type approval from September 2017. In order to accurately determine road load during the real-world test procedure it will be important to develop a suitable methodology for calculating a 1Hz road grade.
机译:利用便携式排放测量系统(PEMS)记录了EURO 4汽油车在城市交通网络中进行的48次试运行中的道路上二氧化碳(CO2)排放。测试是在Headingley和英国利兹市之间的一条780米的微型路段上进行的,同时测量了入站车道(A节)和出站车道(B节)。监视的测试运行是在从严重拥挤到自由流动的一系列交通流条件下进行的。使用瞬时功率排放模型的车辆尾气排放模拟能够生成微观尺度路段上真实车辆排放的估计。格拉茨技术大学(TUG)乘用车和重型排放模型(PHEM)用于计算通过A节和B节进行的48次试验中每一次的CO2排放估算。然后将模型的CO2排放估算与实际排放量进行比较。世界上的PEMS排放测量,以确定建模方法的准确性。仪器仪表的车辆可以完全捕获每秒(1Hz)的绝对位置和车速,而在全球定位系统(GPS)作为PEMS设置的一部分的真实海拔高度测量中,仪器存在明显的误差。这些错误使使用GPS系统精确计算1Hz道路坡度变得非常困难。但是,由于道路坡度可能对发动机功率需求以及燃料消耗和废气排放有重要影响,因此必须包括代表性的道路坡度估算以进行微尺度排放估算。这项研究未使用GPS记录的高程,而是开发了一种简单的道路坡度估算方法,该方法使用了地理信息系统(GIS)软件从5米分辨率的数字地形图(DTM)提取PEMS数据在每一秒的高程。光检测和测距(LiDAR)数据。该方法应用了一种算法,可根据LiDAR-GIS高程值和车速计算道路坡度,并缓解了低速GPS的绝对位置测量误差所导致的误差。发现在PHEM建模中增加了LiDAR-GIS道路坡度,可以提高对PEMS测量的实际CO2排放量进行PHEM估算的准确性。在48次测试中,通过A区实际测量的二氧化碳排放量的平均PHEM估算值(包括道路坡度)为93%,B区为94%。在A节和B节中进行的总共96次测试中,PHEM估计值的91%在PEMS记录值的80%至110%之间。在进一步分析中,评估了道路坡度对CO2和NOx排放的影响。对于每个测试运行,将A节和B节合并在一起,以形成AB段,该段的道路平坦。通过A段和B段的每个测试运行的PEMS记录的速度曲线输入到PHEM中,并在四种道路坡度情况下生成排放估算。这些场景是通过降低和夸大每一秒数据的LiDAR-GIS道路坡度,再乘以0(平坦),0.5(一半坡度),1和2(两倍坡度)的系数来形成的。结果表明,假设按照道路坡度计算,PHEM中的平面轮廓将平均低估路段排放量,对于二氧化碳和二氧化碳分别为2.7%和7.0%,对于道路,二氧化碳分别为7.9%和20.4%成绩翻了一番。本研究中开发的方法为计算1Hz道路坡度提供了一种简单的方法,并且已被证明可以改善此数据集的CO2排放建模。这项研究表明,将PHEM模型与LiDAR-GIS计算出的道路坡度结合使用,可为准确估算现实世界中的微尺度排放提供一种实用的方法。计划从2017年9月开始,通过PEMS进行道路排放监控,以通过6c欧式认证。为了在实际测试过程中准确确定道路负载,开发一种合适的方法来计算1Hz道路坡度将非常重要。

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    Wyatt DW; Hu L; Tate JE;

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  • 年度 2014
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  • 正文语种 en
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