Open AccessCity‐wide emissions modelling using fleet probe vehicles
Author(s) -
Rushton Christopher,
Galatioto Fabio,
Wright James,
Nielsen Erik,
Tsotskas Christos
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5217
Subject(s) - range (aeronautics) , geographic information system , zero emission , driving cycle , control (management) , automotive engineering , air quality index , environmental science , battery (electricity) , transport engineering , computer science , electric vehicle , engineering , meteorology , remote sensing , geography , power (physics) , physics , electrical engineering , quantum mechanics , aerospace engineering , artificial intelligence
Five UK cities will implement clean air zones to improve air quality (AQ), specifically in relation to oxides of nitrogen, and 253 UK local authorities have declared AQ management areas. Extended range electric vehicles can offer zero emission (ZE) operation but cities lack the ability to monitor and control when vehicles can switch from engine to battery. Project Autonomous and Connected vehicles for CleaneR Air (ACCRA) aims to develop and demonstrate a system allowing hybrid vehicle to become part of a city's urban traffic management control system; monitoring the vehicles’ location and operational state, and controlling the strategy to ensure ZE operation through geofenced zones with poor AQ. This study demonstrates a new methodology to model emissions using state‐of‐the‐art instantaneous vehicle emissions modelling, geographic information system (GIS) and a new method for capturing vehicle behaviour using global positioning system. Probe vehicles were used for the pilot study in Leeds, UK, to capture drive‐cycle data and the emissions are modelled. A GIS interface is used to match these emissions to the network. Finally, a high‐resolution emissions map is generated to be used as input for AQ dispersion models and the ACCRA decision‐making engine for the generation of geofencing AQ zones.