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A Statistical Model of Vehicle Emissions and Fuel Consumption

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dc.contributor.author Cappiello, Alessandra
dc.contributor.author Chabini, Ismail
dc.contributor.author Lue, Alessandro
dc.contributor.author Zeid, Maya Abou
dc.contributor.author Nam, Edward K.
dc.date.accessioned 2002-09-17T19:42:53Z
dc.date.available 2002-09-17T19:42:53Z
dc.date.issued 2002-09-17T19:42:53Z
dc.identifier.uri http://hdl.handle.net/1721.1/1674
dc.description.abstract Many vehicle emission models are overly simple, such as the speed dependent models used widely, and other models are sufficiently complicated as to require excessive inputs and calculations, which can slow down computational time. We develop and implement an instantaneous statistical model of emissions (CO2, CO, HC, and NOx) and fuel consumption for light-duty vehicles, which is simplified from the physical loadbased approaches that are gaining in popularity. The model is calibrated for a set of vehicles driven on standard as well as aggressive driving cycles. The model is validated on another driving cycle in order to test its estimation capabilities. The preliminary results indicate that the model gives reasonable results compared to actual measurements as well as to results obtained with CMEM, a well-known load-based emission model. Furthermore, the results indicate that the model runs fast and is relatively simple to calibrate. The model presented can be integrated with a variety of traffic models to predict the spatial and temporal distribution of traffic emissions and assess the impact of ITS traffic management strategies on travel times, emissions, and fuel consumption. en
dc.description.sponsorship Ford Motor Company through the Ford-MIT Alliance. en
dc.format.extent 590971 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject instantaneous emissions modeling en
dc.subject dynamic traffic en
dc.subject vehicle emissions en
dc.subject fuel consumption en
dc.subject emission models en
dc.title A Statistical Model of Vehicle Emissions and Fuel Consumption en


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  • Ford-MIT Alliance
    Institute-wide collaboration focusing on statistical engineering, virtual education, and the environment

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