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Title: Developing real driving CO2 emission factors for hybrid cars through on road testing and microscale modelling
Author: Riley, Richard James Acklom
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2016
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Vehicle type approval CO2 emission figures form the basis for many countries’ national policy to reduce transport's contribution to anthropogenic climate change. However, it has become increasingly apparent that the vehicle type approval testing procedure used in Europe is not fit for purpose. There is, therefore, a need for representative real world emission factors that can be used to inform consumers, aid policy makers and provide an accurate benchmark from which type approval figures can be compared. In this work, two methods are explored to assess their feasibility to provide robust CO2 emission figures. The first is on-road vehicle activity tracking, using data collected from the vehicle controller area network. This method was chosen as it has the potential to provide large quantities of cheap, reliable data and has been demonstrated by recording over 40 parameters during testing of a third-generation Toyota Prius. This data has been used to analyse the vehicle powertrain control and provide a clear understanding of the control mechanisms that balance the engine and electrical power systems, present a comparison of the emissions of conventional and hybrid taxis giving local policy makers the underlying evidence required to introduce strong policies to reduce urban emissions from taxis and build a microscale emission model for accurate and detailed emission forecasts. The second method is microscale vehicle modelling, defined as very short time step models (1 second or less) that capture vehicle and location specific details within the model. The model requires vehicle speed and road gradient data as input and outputs second-by-second cumulative and total fuel consumed and CO2 emissions. The model has been validated against independent data (chassis dynamometer data collected by Argonne National Laboratory) and is now a powerful tool to help assess the effects of local policies (geofences, changes in the speed limit, incentives for hybrid vehicle uptake) or schemes (eco-driving) on the CO2 emissions from hybrid vehicles. This work has further developed these two methods in two ways. Firstly, by demonstrating the accuracy of controller area network data collected in vehicle activity tracking. Secondly, by demonstrating the precision of emission models built using real-world data, despite the data noise caused by real world conditions. In conclusion, these methods are well suited to providing representative real world CO2 emission factors, especially if the methods are combined. This is because vehicle activity tracking can provide the large amount of data needed for vehicle modelling and a vehicle model can provide situation specific emission factors, which, in contrary to many current emissions factors, are not only dependent on vehicle average speeds.
Supervisor: Tate, James ; Li, Hu ; Wadud, Zia Sponsor: EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available