Establishing bonds between vehicle certification data and real-world vehicle fuel consumption – A Vehicle Specific Power approach

Duarte, Gonçalo; Gonçalves, Gonçalo; Baptista, Patrícia; Farias, Tiago L.

doi:10.1016/j.enconman.2014.12.042

Cited by 59 publications

(38 citation statements)

References 9 publications

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“…(), and Duarte et al. (). CMEM was initially developed in the late 1990s with sponsorship from the National Cooperative Highway Research Program (NCHRP) and the U.S. Environmental Protection Agency (EPA) to fulfill the need for microscopic emissions modeling (An et al., ; Barth et al., , , , ; Boriboonsomsin et al., ).…”

Section: Introductionmentioning

confidence: 98%

“…The model essentially summarizes the loads resulting from aerodynamic drag, acceleration, rolling resistance, and hill climbing. This model has been further applied and validated by Jiménez et al (1999), Zhai et al (2008), and Duarte et al (2015). CMEM was initially developed in the late 1990s with sponsorship from the National Cooperative Highway Research Program (NCHRP) and the U.S. Environmental Protection Agency (EPA) to fulfill the need for microscopic emissions modeling Barth et al, 1996Barth et al, , 1997Barth et al, , 1999Barth et al, , 2000Boriboonsomsin et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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In this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well‐recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT‐Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy‐efficient driving behaviors in the soon‐to‐come era of connected and automated vehicles.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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“…In other words, the specific VSP mode depends on a combination of parameters, as do the off-cycle operating conditions. 35…”

Section: Resultsmentioning

confidence: 99%

“…In other words, the specific VSP mode depends on a combination of parameters, as do the off-cycle operating conditions. 35 Emissions comparison under standard vehicle emissions levels. Figure 6 presents the emission factors for China 3 and China 4 vehicles under different driving cycles.…”

Section: Comparison Of Emission Factors Under Various Conditionsmentioning

confidence: 99%

Analysis of emissions from various driving cycles based on real driving measurements obtained in a high-altitude city

Lyu

Bao

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle emissions standards and regulations remain weak in high-altitude regions. In this study, vehicle emissions from both the New European Driving Cycle and the Worldwide harmonized Light-duty driving Test Cycle were analyzed by employing on-road test data collected from typical roads in a high-altitude city. On-road measurements were conducted on five light-duty vehicles using a portable emissions measurement system. The certification cycle parameters were synthesized from real-world driving data using the vehicle specific power methodology. The analysis revealed that under real-world driving conditions, all emissions were generally higher than the estimated values for both the New European Driving Cycle and Worldwide harmonized Light-duty driving Test Cycle. Concerning emissions standards, more CO, NOx, and hydrocarbons were emitted by China 3 vehicles than by China 4 vehicles, whereas the CO2 emissions exhibited interesting trends with vehicle displacement and emissions standards. These results have potential implications for policymakers in regard to vehicle emissions management and control strategies aimed at emissions reduction, fleet inspection, and maintenance programs.

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“…warm-up after start-up) or engine operation at ambient temperatures significantly different from the average (e.g. winter operating conditions) and load and engine rapid changes [4,9,14]. For example, a spark ignition engine equipped with a typical three-function catalytic converter with oxygen sensor operating in a closed fuel delivery control loop emits more CO and HC during warm-up and cold start than in the operating phase when the thermal conditions are static.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the possibility of determining the general characteristics using the operational data of a vehicle engine

2017

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The paper presents an analysis of the possibility of determining the general characteristics using the operational data of an engine of refuse collection vehicle. Data acquisition was done by reading information from the CAN network using FMS standard (Fleet Management System), which is widely used in heavy duty vehicles since year 2002. The paper presents the analysis of the expected measurement uncertainties resulting from the standard PN_ISO 15550-2009. The experimental data were filtered due to the established constraints that define the static states of the engine. On the basis of the obtained measurement data, corresponding to various defined static states, the general characteristics of the engine were made and discussed.

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Establishing bonds between vehicle certification data and real-world vehicle fuel consumption – A Vehicle Specific Power approach

Cited by 59 publications

References 9 publications

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Analysis of emissions from various driving cycles based on real driving measurements obtained in a high-altitude city

Analysis of the possibility of determining the general characteristics using the operational data of a vehicle engine

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