NOx emissions from diesel light duty vehicle tested under NEDC and real-word driving conditions

This work focuses on the potential for waste energy recovery from exhaust gases in a diesel light-duty vehicle tested under real driving conditions, fueled with animal fat biodiesel, Gas To Liquid (GTL) and diesel fuels. The vehicle was tested following random velocity profiles under urban driving conditions, while under extra-urban conditions, the vehicle followed previously defined velocity profiles. Tests were carried out at three different locations with different altitudes. The ambient temperature (20 ± 2 °C) and relative humidity (50 ± 2%) conditions were similar for all locations. Exergy analysis was included to determine the potential of exhaust gases to produce useful work in the exhaust system at the outlet of the Diesel Particle Filter. Results include gas temperature registered at each altitude with each fuel, as well as the exergy to energy ratio (percentage of energy that could be transformed into useful work with a recovery device), which was in the range of 20–35%, reaching its maximum value under extra-urban driving conditions at the highest altitude. To take a further step, the effects of fuels and altitude on energy recovery with a prototype of a thermoelectric generator (TEG) were evaluated.

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Real-time NOx estimation in light duty diesel engine with in-cylinder pressure prediction

International Journal of Engine Research ◽

10.1177/14680874211015780 ◽

2021 ◽

pp. 146808742110157

Author(s):

Youngbok Lee ◽

Seungha Lee ◽

Kyoungdoug Min

Keyword(s):

Prediction Model ◽

Real Time ◽

Pressure Sensor ◽

Design Stage ◽

Lean Nox Trap ◽

Nox Emissions ◽

Cylinder Pressure ◽

Estimation Model ◽

Light Duty ◽

Light Duty Vehicle

Recently, there have been numerous efforts to cope with automotive emission regulations. Various strategies to reduce engine-out NOx emissions and proper after-treatment systems, such as selective catalytic reduction (SCR) and lean NOx trap (LNT), have been taken into account in the engine research field. In this study, real-time engine-out NOx prediction model was established where zero-dimensional NO and NO2 models were combined with in-cylinder pressure model. During the procedure for estimating NO and NO2 (NOx), a real-time prediction model of in-cylinder pressure was applied so that the inputs to the NOx prediction model could be provided only by the data acquired from the engine control unit (ECU). This implies that an in-cylinder pressure sensor is not necessarily required to properly predict the engine-out NOx in real time. The real-time NOx estimation model was validated through the worldwide harmonized light-duty vehicle test cycle (WLTC) without a pressure sensor, and the total NOx error during the mode was comparable with the total NOx error of the portable NOx sensor. This real-time NOx estimation model can ultimately contribute to minimizing tail-pipe NOx emissions by influencing both emission calibration at the engine design stage and the management of NOx after-treatment systems where NOx conversion efficiency is heavily affected by the NO2/NO ratio.

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THE IMPACT OF DRIVING CONDITIONS ON LIGHT-DUTY VEHICLE EMISSIONS IN REAL-WORLD DRIVING

Transport ◽

10.3846/transport.2020.12168 ◽

2020 ◽

Vol 35 (4) ◽

pp. 379-388

Author(s):

Dong Guo ◽

Jinbao Zhao ◽

Yi Xu ◽

Feng Sun ◽

Kai Li ◽

...

Keyword(s):

Neural Network ◽

Real World ◽

Vehicle Emissions ◽

Emission Rate ◽

Vehicle Emission ◽

Road Test ◽

Light Duty ◽

Emission Models ◽

Light Duty Vehicle ◽

Driving Conditions

To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix.

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Characterization of Exhaust CO, HC and NOx Emissions from Light-Duty Vehicles under Real Driving Conditions

Atmosphere ◽

10.3390/atmos12091125 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1125

Author(s):

Hui Mei ◽

Lulu Wang ◽

Menglei Wang ◽

Rencheng Zhu ◽

Yunjing Wang ◽

...

Keyword(s):

Specific Power ◽

Emission Measurement ◽

Nox Emissions ◽

Emission Rates ◽

Gaseous Emissions ◽

Passenger Cars ◽

Light Duty ◽

Driving Conditions ◽

Hc Emissions ◽

Light Duty Vehicles

On-road exhaust emissions from light-duty vehicles are greatly influenced by driving conditions. In this study, two light-duty passenger cars (LDPCs) and three light-duty diesel trucks (LDDTs) were tested to investigate the on-road emission factors (EFs) with a portable emission measurement system. Emission characteristics of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) emitted from vehicles at different speeds, accelerations and vehicle specific power (VSP) were analyzed. The results demonstrated that road conditions have significant impacts on regulated gaseous emissions. CO, NOx, and HC emissions from light-duty vehicles on urban roads increased by 1.1–1.5, 1.2–1.4, and 1.9–2.6 times compared with those on suburban and highway roads, respectively. There was a rough positive relationship between transient CO, NOx, and HC emission rates and vehicle speeds, while the EFs decreased significantly with the speed decrease when speed ≤ 20 km/h. The emissions rates of NOx and HC tended to increase and then decrease as the acceleration increased and the peak occurred at 0 m/s2 without considering idling conditions. For HC and CO, the emission rates were low and changed gently with VSP when VSP < 0, while emission rates increased gradually with the VSP increase when VSP > 0. For NOx NOx emission rates were lower and had no obvious change when VSP < 0. However, NOx emissions were positively correlated with VSP, when VSP > 0.

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Does California’s EMFAC2017 Vehicle Emissions Model Under-predict California Light-duty Gasoline Vehicle NOx Emissions?

Journal of the Air & Waste Management Association ◽

10.1080/10962247.2020.1869121 ◽

2021 ◽

Author(s):

Gary A. Bishop

Keyword(s):

Vehicle Emissions ◽

Nox Emissions ◽

Light Duty

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Optimal investment strategies for light duty vehicle and electricity generation sectors in a carbon constrained world

Annals of Operations Research ◽

10.1007/s10479-016-2119-2 ◽

2016 ◽

Vol 255 (1-2) ◽

pp. 391-420 ◽

Cited By ~ 3

Author(s):

Boxiao Chen ◽

Erica Klampfl ◽

Margaret Strumolo ◽

Yan Fu ◽

Xiuli Chao ◽

...

Keyword(s):

Electricity Generation ◽

Optimal Investment ◽

Investment Strategies ◽

Light Duty ◽

Light Duty Vehicle

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Low-Level Automated Light-Duty Vehicle Technologies Provide Opportunities to Reduce Fuel Consumption

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118796401 ◽

2018 ◽

Vol 2672 (24) ◽

pp. 60-74 ◽

Cited By ~ 6

Author(s):

Saeed Vasebi ◽

Yeganeh M. Hayeri ◽

Constantine Samaras ◽

Chris Hendrickson

Keyword(s):

Fuel Consumption ◽

Aerodynamic Force ◽

The United States ◽

Route Guidance ◽

Conventional Vehicle ◽

Automated Vehicle ◽

Fuel Savings ◽

Light Duty ◽

Vehicle Systems ◽

Light Duty Vehicle

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

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