The influence of driving patterns on petrol passenger car emissions

Hansen, Jeff; Winther, Morten; Sorenson, Spencer C.

doi:10.1016/0048-9697(95)04641-d

Cited by 48 publications

(21 citation statements)

References 2 publications

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“…In contrast to previous research using dynamometer tests with non-catalyst and platinum catalyst vehicles (Hansen et al, 1995;Jensen, 1995;Joumard et al, 1995;Sjodin and Lenner, 1995), correlations between emission levels and driving parameters such as speed and acceleration were not obvious in our data. This suggests that a complex combination of factors a ect emission levels for well-maintained automobiles with three-way catalyst systems.…”

Section: Elevated Exhaust Emissions Frequencycontrasting

confidence: 55%

“…1). Other researchers have shown an increase in emissions during hard acceleration events (Ross, 1994;Hansen et al, 1995;Joumard et al, 1995;Sjodin and Lenner, 1995). Consistent with this observation, the highest acceleration events in our study had concurrent high HC and CO values, but these hard accelerations were restricted to the very beginning of the driving route.…”

Section: Driver Variability Anova Testsmentioning

confidence: 99%

“…Inspection/maintenance program smog-check inspections provide data on in-use vehicles, but are inadequate indicators of on-road emissions since the tests are performed over only two no-load engine operating conditions (2500 rpm and idle). While more recent research has focused on correlating driving mode with emissions (Hansen et al, 1995;Jensen, 1995;Joumard et al, 1995;Sjodin and Lenner, 1995;Barth et al, 1997;Washington et al, 1997;Young et al, 1997), emissions were usually estimated from emission models rather than measured directly in the ®eld. The few studies that have used on-board gas analyzers (Schurmann and Staab, 1990;Kelly and Groblicki, 1993;Guenther et al, 1996) demonstrated the capabilities of these instruments to measure second-by-second emission levels to aid in repair of malfunctioning emission systems, but made no e ort to correlate driving style and emission levels.…”

Section: Quantifying Real-world Automobile Emissionsmentioning

confidence: 99%

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Characterizing the effects of driver variability on real-world vehicle emissions

Holmén

Niemeier

1998

Transportation Research Part D: Transport and Environment

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AbstractÐRecent studies on real-world automobile emissions measurements have not adequately addressed the question of whether driving style a ects emission levels. In this study, we hypothesized that given the same experimental conditions and a random selection of drivers, the variability associated with individual driving styles (e.g. intensity or duration of acceleration events) would produce statistically signi®cant di erences in measured emissions. To test this driver variability hypothesis, we conducted a ®eld study on 24 drivers in a single vehicle on a speci®ed route under low tra c conditions using on-board exhaust emission and engine operating data analyzers and tested for statistically signi®cant di erences in CO and NOx emissions between drivers. Our data show signi®cant (95% level) variations in carbon monoxide (CO) and oxides of nitrogen (NOx) emissions among the 24 drivers under driving conditions where we have controlled for driving route, tra c density and vehicle type. Since the ANOVA tests showed signi®cant di erences in emissions between drivers but the frequency of driving modes were very similar, this suggests that the intensity of vehicle operation within a give mode, not the modal frequency, explains the emissions variability between drivers. #

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Section: Elevated Exhaust Emissions Frequencycontrasting

confidence: 55%

Section: Driver Variability Anova Testsmentioning

confidence: 99%

Section: Quantifying Real-world Automobile Emissionsmentioning

confidence: 99%

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Characterizing the effects of driver variability on real-world vehicle emissions

Holmén

Niemeier

1998

Transportation Research Part D: Transport and Environment

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“…The on-road movement characteristics are different in different parts of the city and the vehicle speeds play a vital role in estimating the total emissions and in determining the temporal and spatial variation of these emissions in the city (Hansen et al, 1995;Jensen, 1995;Sturm et al, 1996;Ntziachristos and Samaras, 2000;Tong et al, 2000;Kean et al, 2003;Smit et al, 2008;Barth and Boriboonsomsin, 2009). Since, the speed distribution is dependent on the road characteristics and travel behavior, this could not be based on the point surveys.…”

Section: Vehicle Speedsmentioning

confidence: 99%

Benchmarking vehicle and passenger travel characteristics in Delhi for on-road emissions analysis

Goel

Guttikunda

Mohan

et al. 2015

Travel Behaviour and Society

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“…Several works have correlated the driving profile with exhaust emissions estimated from emission models or measured on a chassis dynamometer [1][2][3][4][5][6][7][8]. The influence of the driver behavior on the real world exhaust emissions has been analyzed using onboard exhaust measurement system in [9,10].…”

Section: Introductionmentioning

confidence: 99%

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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The influence of driving patterns on petrol passenger car emissions

Cited by 48 publications

References 2 publications

Characterizing the effects of driver variability on real-world vehicle emissions

Characterizing the effects of driver variability on real-world vehicle emissions

Benchmarking vehicle and passenger travel characteristics in Delhi for on-road emissions analysis

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

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