Energy Consumption and Emissions Modeling of Individual Vehicles

2017 ◽  
Vol 2627 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Randall Guensler ◽  
Haobing Liu ◽  
Yanzhi (Ann) Xu ◽  
Alper Akanser ◽  
Daejin Kim ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
High Performance ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Operating Conditions ◽  
Modeling Framework ◽  
Activity Data ◽  
Emissions Modeling ◽  
Vehicle Activity ◽  
Fuel Consumption And Emissions

This study demonstrated an approach to modeling individual vehicle second-by-second fuel consumption and emissions on the basis of vehicle operations. The approach used the Motor Vehicle Emission Simulator (MOVES)–Matrix, a high-performance vehicle emissions modeling system consisting of a multidimensional array of vehicle emissions rates (pulled directly from EPA’s MOVES emissions model) that could be quickly queried by other models to generate an applicable emissions rate for any specified on-road fleet and operating conditions. For this project, the research team developed a spreadsheet-based MOVES-Matrix calculator to simplify connecting vehicle activity data with multidimensional emissions rates from MOVES-Matrix. This paper provides a walk-through of the calculation procedures, from basic vehicle information and driving cycles to second-by-second emissions rates. The individual vehicle emissions modeling framework was incorporated into Commute Warrior, a trademarked travel survey application for Android smartphones, to provide real-time fuel consumption and emissions rate estimates from concurrently obtained GPS-based speed data.

scholarly journals Vehicle Emission Computation Through Microscopic Traffic Simulation Calibrated Using Genetic Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 67-80 ◽  
Author(s):  
Yun Wei ◽  
Ying Yu ◽  
Lifeng Xu ◽  
Wei Huang ◽  
Jianhua Guo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Vehicle Emissions ◽  
Traffic Simulation ◽  
Urban Region ◽  
Vehicle Emission ◽  
Activity Data ◽  
Microscopic Traffic Simulation ◽  
Emission Models ◽  
Vehicle Activity

Abstract Vehicle emission calculation is critical for evaluating motor vehicle related environmental protection policies. Currently, many studies calculate vehicle emissions from integrating the microscopic traffic simulation model and the vehicle emission model. However, conventionally vehicle emission models are presented as a stand-alone software, requiring a laborious processing of the simulated second-by-second vehicle activity data. This is inefficient, in particular, when multiple runs of vehicle emission calculations are needed. Therefore, an integrated vehicle emission computation system is proposed around a microscopic traffic simulation model. In doing so, the relational database technique is used to store the simulated traffic activity data, and these data are used in emission computation through a built-in emission computation module developed based on the IVE model. In order to ensure the validity of the simulated vehicle activity data, the simulation model is calibrated using the genetic algorithm. The proposed system was implemented for a central urban region of Nanjing city. Hourly vehicle emissions of three types of vehicles were computed using the proposed system for the afternoon peak period, and the results were compared with those computed directly from the IVE software with a trivial difference in the results from the proposed system and the IVE software, indicating the validity of the proposed system. In addition, it was found for the study region that passenger cars are critical for controlling CO, buses are critical for controlling CO and VOC, and trucks are critical for controlling NOx and CO2. Future work is to test the proposed system in more traffic management and control strategies, and more vehicle emission models are to be incorporated in the system.

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%.

Fusion of Vehicle Weight and Activity Data for Improved Vehicle Emission Modeling

2015 ◽  
Vol 2503 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Kanok Boriboonsomsin ◽  
Guoyuan Wu ◽  
Peng Hao ◽  
Matthew Barth
Keyword(s):  
Los Angeles ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Operating Mode ◽  
Vehicle Emission ◽  
Average Weight ◽  
Activity Data ◽  
Vehicle Weight ◽  
Weight Data

Vehicle weight is one of several factors that affect vehicle emissions. Vehicle weight is especially important when modeling emissions from heavy-duty trucks (HDTs). The motor vehicle emission simulator (MOVES) model provides a way to account for vehicle weight during the construction of vehicle emissions inventories. To date, vehicle weight has not received much attention, although reliable vehicle weight data have become increasingly available in the past several years with the deployment of weigh-in-motion (WIM) technology. This study developed a method for fusing vehicle weight data from WIM stations and traffic data from vehicle detector stations (VDS) to obtain HDT activity data for input in MOVES as vehicle operating mode distributions. The study identified trucks recorded by a WIM station that were likely to travel over a VDS during a specified time period. The measured weight data of the trucks were fused with the second-by-second speed and acceleration values in truck trajectories that were created based on the knowledge of truck traffic speed at the VDS. The study used freeways in Los Angeles County, California, as a case study. The case study showed that the distributions of vehicle operating mode were quite different between the proposed method and the existing method, which assumed an average weight value for all HDTs in the same class. In the example case illustrated in this paper, the proposed method resulted in 78% higher nitrogen oxide emissions and 30% higher particulate matter emissions than the existing method.

scholarly journals PECULIARITIES OF DETERMINATION OF FUEL CONSUMPTION AND EMISSIONS OF HARMFUL SUBSTANCES OF ENGINES OF VEHICLES OPERATING ON GAS FUEL

2021 ◽  
pp. 25-35
Author(s):  
I.V. Gritsuk ◽  
D.S. Pohorletskyi ◽  
D.S. Adrov ◽  
А.V. Bilai
Keyword(s):  
Phase Transition ◽  
Heat Treatment ◽  
Fuel Consumption ◽  
Training System ◽  
Environmental Indicators ◽  
Operating Conditions ◽  
Gas Fuel ◽  
Fuel Consumption And Emissions ◽  
Thermal Preparation ◽  
Thermal Training

The article considers the features of the method of research of fuel economy and environmental performance of vehicles with engines converted to run on gas fuel, which are equipped with a thermal training system based on a thermal accumulator phase transition, which is based on the implementation of system interaction of three interconnected components: information, analytical and energy. The schematic diagram of the system of thermal preparation and the information system of estimation of ways of maintenance of thermal preparation of vehicles in the conditions of operation by means of system of thermal preparation on the basis of an onboard complex (Intelligent transportation system) are presented. The peculiarity of the proposed system is that the subsystems create a common information field of the vehicle monitoring system with the heat treatment system, but operate separately from each other, based on the characteristics of the tasks they perform. Improved is the method for determining and calculating fuel consumption and emissions in exhaust gases of vehicles with engines converted to run on gas fuel, equipped with a thermal accumulator phase transition in the processes of pre-start and post-start heat treatment based on the selected model of the engine "Neutralizer". To ensure thermal preparation of vehicles with engines converted to run on gas fuel, equipped with a thermal training system based on a thermal accumulator of the phase transition, a cycle of thermal preparation in operating conditions has been developed. The influence of the thermal preparation system with the heat accumulator of the phase transition of a vehicle with an engine converted to run on gas fuel on the fuel efficiency indicators and environmental indicators in the pre-start and post-start-up processes is established.

scholarly journals Taxi fuel consumption and emissions estimation model based on the reconstruction of driving trajectory

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770870 ◽  
Author(s):  
Jiancheng Weng ◽  
Quan Liang ◽  
Guoliang Qiao ◽  
Zhihong Chen ◽  
Jian Rong
Keyword(s):  
Fuel Consumption ◽  
Urban Areas ◽  
Operating Conditions ◽  
Bench Test ◽  
Trajectory Data ◽  
Estimation Model ◽  
Driving Cycles ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Fuel Consumption And Emissions

Monitoring operating vehicles’ fuel consumption and emissions is necessity for evaluating fuel saving and emissions reduction. Taxis are one of the key objects needed energy consumption monitoring in passenger transport system. However, the traditional data collection methods for vehicle fuel consumption and emissions had high cost and inconvenient maintenance. This study aims at proposing an approach to estimate taxi fuel consumption and emissions based on the global position system (GPS) trajectory data. The bench test experiment was first conducted with three different driving cycles: cruising, acceleration and deceleration, and the composite driving cycle including these two. Then, models to calculate fuel consumption and emission based on the driving trajectory reconstruction were proposed. Therefore, the taxis’ fuel consumption and emissions could be got through GPS trajectory data corresponding to these three driving cycles. The model accuracy were verified that fuel consumption (92%) and CO2 emission (95%) fit the measurements much better than CO, NOx, and HC emission models (60%–70%). Furthermore, taking fuel consumption per 100 km as dependent variable, the relative errors between the model’s outputs and field measurements were 1.9% in urban areas and 11.2% in comprehensive operating conditions (i.e. both urban and suburb areas).

Motor vehicle emissions under reduced ambient temperature idle operating conditions

1987 ◽  
Vol 21 (10) ◽  
pp. 2077-2082 ◽  
Author(s):  
James Baugh ◽  
William Ray ◽  
Frank Black ◽  
Richard Snow
Keyword(s):  
Ambient Temperature ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Operating Conditions ◽  
Motor Vehicle Emissions
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