Characteristics of operating mode distributions of light duty vehicles by road type, average speed, and driver type for estimating on-road emissions: Case study of Beijing

2018 ◽  
Vol 23 (2) ◽  
pp. 191-202 ◽  
Author(s):  
Zhiqiang Zhai ◽  
Guohua Song ◽  
Ying Liu ◽  
Ying Cheng ◽  
Weinan He ◽  
...  
Keyword(s):  
Operating Mode ◽  
Average Speed ◽  
Light Duty ◽  
Road Type ◽  
Light Duty Vehicles

Development of Operating Mode Distribution Models for Light-Duty Vehicles on Unrestricted Access Roadways

2020 ◽  
Vol 2674 (10) ◽  
pp. 416-428
Author(s):  
Jia Li ◽  
Hanhui He ◽  
Bo Peng
Keyword(s):  
Real World ◽  
Operating Mode ◽  
Specific Power ◽  
Trajectory Data ◽  
Average Speed ◽  
The Real ◽  
Light Duty ◽  
Unrestricted Access ◽  
Data Points ◽  
Light Duty Vehicles

The key correlating traffic variable for modeling vehicle emissions has evolved from average speed to vehicle-specific power (VSP), and recently to operating mode as defined in Motor Vehicle Emission Simulator (MOVES). The analysis of operating mode and its distribution, however, requires a large amount of data and is time consuming and challenging. This paper attempts to build models between the operating mode distributions and the common traffic variable—average speed—to facilitate the emission estimation. Focusing on light-duty vehicles and unrestricted access roadways, a floating car survey was conducted separately on arterials and collectors in Shaoshan, China. The trajectory data were processed to reveal the characteristics of operating mode distributions. A key finding is that, when the data points of the operating mode of idle are excluded, the VSP distributions of the remaining data points follow logistic distributions and the parameters can be linearly regressed with the average speed. Arterials and collectors feature different operating mode distributions even at the same average speed, and therefore different models were developed. The models were then applied to generate operating mode distributions, which were validated with the real-world data from the test bed and which, when compared with the default values generated by MOVES, fit the real-world condition better.

The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption

2021 ◽  
Vol 18 (3) ◽  
pp. 9071-9081
Author(s):  
Jakub Lasocki
Keyword(s):  
Fuel Consumption ◽  
Combustion Engine ◽  
Engine Performance ◽  
Driving Cycle ◽  
Performance Characteristics ◽  
Pollutant Emission ◽  
Strong Impact ◽  
Light Duty ◽  
Light Duty Vehicles

The World-wide harmonised Light-duty Test Cycle (WLTC) was developed internationally for the determination of pollutant emission and fuel consumption from combustion engines of light-duty vehicles. It replaced the New European Driving Cycle (NEDC) used in the European Union (EU) for type-approval testing purposes. This paper presents an extensive comparison of the WLTC and NEDC. The main specifications of both driving cycles are provided, and their advantages and limitations are analysed. The WLTC, compared to the NEDC, is more dynamic, covers a broader spectrum of engine working states and is more realistic in simulating typical real-world driving conditions. The expected impact of the WLTC on vehicle engine performance characteristics is discussed. It is further illustrated by a case study on two light-duty vehicles tested in the WLTC and NEDC. Findings from the investigation demonstrated that the driving cycle has a strong impact on the performance characteristics of the vehicle combustion engine. For the vehicles tested, the average engine speed, engine torque and fuel flow rate measured over the WLTC are higher than those measured over the NEDC. The opposite trend is observed in terms of fuel economy (expressed in l/100 km); the first vehicle achieved a 9% reduction, while the second – a 3% increase when switching from NEDC to WLTC. Several factors potentially contributing to this discrepancy have been pointed out. The implementation of the WLTC in the EU will force vehicle manufacturers to optimise engine control strategy according to the operating range of the new driving cycle.

High-Resolution Vehicular Emission Inventory Using a Link-Based Method: A Case Study of Light-Duty Vehicles in Beijing

2009 ◽  
Vol 43 (7) ◽  
pp. 2394-2399 ◽  
Author(s):  
Hong Huo ◽  
Qiang Zhang ◽  
Kebin He ◽  
Qidong Wang ◽  
Zhiliang Yao ◽  
...  
Keyword(s):  
High Resolution ◽  
Emission Inventory ◽  
Vehicular Emission ◽  
Light Duty ◽  
Light Duty Vehicles

How Many Trajectories Are Needed to Develop Facility- and Speed-Specific Vehicle-Specific Power Distributions for Emission Estimation? Case Study in Beijing

2019 ◽  
Vol 2673 (11) ◽  
pp. 779-790
Author(s):  
Zeyu Zhang ◽  
Guohua Song ◽  
Zhiqiang Zhai ◽  
Chenxu Li ◽  
Yizheng Wu
Keyword(s):  
Sample Size ◽  
Motor Vehicle ◽  
Specific Power ◽  
Activity Data ◽  
Light Duty ◽  
Emission Estimation ◽  
Vehicle Activity ◽  
Light Duty Vehicles ◽  
Mean Square Errors

Vehicle-specific power (VSP) distributions, or operating mode (OpMode) distributions, are one of the most important parameters in VSP-based emission models, such as the motor vehicle emission simulator (MOVES) model. The collection of second-by-second vehicle activity data is required to develop facility- and speed-specific (FaSS) VSP distributions. This then raises the problem of how many trajectories are needed to develop FaSS VSP distributions for emission estimation. This study attempts to investigate the adaptive sample size for developing robust VSP distributions for emission estimations for light-duty vehicles. First, vehicle activity data are divided into trajectories and categorized into different trajectory pools. Then, the uncertainty of FaSS VSP distribution caused by sample size is analyzed. Further, the relationship between VSP distribution sample size and emission factor uncertainty is discussed. The case study indicates that error in developing FaSS VSP distributions decreases significantly with increased sample size. In different speed bins, the sample size required to develop robust FaSS VSP distributions and estimate emission factors is significantly different. In detail, in each speed bin, for a 90% confidence level, 30 trajectories (1,800 s) are enough to develop robust FaSS VSP distributions for light-duty vehicles with the root mean square errors (RMSEs) lower than 2%, which means errors in calculating fuel consumption and greenhouse gas (GHG) emissions are lower than 5%. However, 35 trajectories (2,100 s) are needed to estimate emissions of carbon monoxide (CO), nitrogen oxide (NOX), and hydrocarbons (HC) with an estimation error lower than 5%.

Asymmetric Logistic Model for Estimation of Mileage-Related Vehicle Depreciation Function of Roadway Characteristics

2020 ◽  
Vol 2674 (12) ◽  
pp. 395-408
Author(s):  
Rami Chkaiban ◽  
Elie Y. Hajj ◽  
Muluneh Sime ◽  
Gary Bailey ◽  
Peter E. Sebaaly
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Transportation Network ◽  
Online Data ◽  
Average Speed ◽  
Vehicle Type ◽  
Light Duty ◽  
Total Vehicle ◽  
Added Benefit ◽  
Light Duty Vehicles

This paper describes an approach for the development of prediction models for the estimation of mileage-related vehicle depreciation that can be used in the estimation of the benefits derived from transportation network improvements. The approach takes advantage of published online data for vehicle valuations. A new asymmetric logistic prediction model for total vehicle depreciation, including initial and mileage-related depreciations, is proposed and fitted to collected valuations data. The added benefit of this prediction model is that it takes into consideration both vehicle age (i.e., years since manufacture) and vehicle usage (i.e., miles of travel). Six small light-duty vehicles (SLDVs), five large light-duty vehicles (LLDVs), three two-axle trucks, one single-unit truck, and two combination trucks were considered in this study. Vehicle fuel sources included gasoline, diesel, gasoline-ethanol blend of up to 85% ethanol (E85), and hybrid-electric, resulting in 26 combinations of vehicle type and fuel source. Additionally, the developed models were adjusted to account for the effects of average speed of vehicle and roadway characteristics (e.g., grade, curvature) on vehicle depreciation. The practicality of the developed models for large sport utility vehicles (SUVs) and midsize cars was illustrated using select examples highlighting the models’ sensitivity to vehicle average speed and roadway characteristics.

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