Environmental assessment of new vehicle technologies with improved confidence

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Environmental assessment of new vehicle
technologies with improved confidence

• Presenter Paul Goodman
• University of Leeds

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Pollutants of Interest

• Carbon dioxide (CO2) greenhouse gas
• Nitrogen dioxide (NO2)
• Local Air Quality 199 of 222 Local Authority
  AQMAs
• Particulates, carbon monoxide (CO)
• Noise
• New technologies/strategies need to address
  climate change, noise and air quality
• co-ordinated controls needed
• Can we predict their effects?

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Overview

• Three main FUTURES activities

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Real-world emissions

• Current speed vs. emissions approaches from drive
  cycles are limiting. New approaches
• Use in-vehicle FTIR (Fourier Transform Infra-Red
  spectroscopy) and OBS measurements
• Produce micro-scale data from real-world driving
• Rapid-response, high-frequency measurements

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Vehicle age

• Analysis of recent vehicles showed a lower
  influence of traffic conditions on emissions due
  to better control of air/fuel ratios, compared to
  older models
• By comparing EUROIV to EUROI vehicle, the CO2
  mass emissions fluctuated approximately between
  0.5 to 3 g/s for EUROIV and 1 to 10 g/s for EUROI
  vehicles.
• Sample data (EUROII car) compliance with EURO II
  standards for CO HCs, but failure for NOx

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Driver behaviour

• Same vehicle, same traffic conditions, same route

Driver 1 Inefficient CO 5.68g CO2
2218g Fuel 651g
Driver 2 Efficient CO 1.8g CO2
1667g Fuel 500g
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Noise Electric vehicles
Smith Newton
G-Wiz
2 6 dBA reduction
2 14 dBA reduction
Thanks to GoinGreen, Smith Electric Vehicles and
Transport Research Laboratory
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Noise emissions

• Small electric vehicles gave a 4-7dBA reduction
  in noise levels at low speeds, but the benefit
  decreases at higher speeds
• Larger electric vehicles showed greater potential
  reductions, potentially gt10dBA over their diesel
  counterparts
• Analysis of previous TRL data for bio-diesel, LPG
  and CNG vehicles did not show any clear pattern
  to emissions reductions

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Environmental modelling

• Computer models commonly used to aid air-quality
  management and noise mapping
• Many different input parameters which are not
  known with certainty
• How much confidence can we place in their
  results?
• How can we best utilise and improve the models?
• Use sensitivity analysis - rank importance of
  input parameters in determining predicted
  concentrations

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Micro-scale modelling
Building layouts
Traffic network information
Meteorology
CFD flow model
Traffic micro-simulation model
Vehicle speeds
Driving and vehicle characteristics
Flow and turbulence
Vehicle acceleration
Dispersion or propagation model
Instantaneous emissions model
Emissions
Pollution concentrations
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Sensitivity analysis

• Many runs of modelling system made with random
  selection of input parameters from their possible
  ranges
• Produces output distribution of NOx emissions and
  predicted roadside concentrations
• Global sensitivities used to assign cause of
  variability in outputs to each parameter

Response of NO2 to changes in NONO2 ratio in
exhaust
saturation
NO2
NONO2 ratio
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York case study

• Flow in street canyons leads to high road side
  concentrations of NO2 and CO
• Low sensitivity to model internal parameters
  suggesting model is robust
• Background wind direction important parameter for
  predicting flow concentrations

• Saturation means that significant reductions in
  demand required to reduce emissions and roadside
  concentrations

• Roadside concentrations of NO2 strongly sensitive
  to NO2 fraction in exhaust
• New technologies resulting in increased primary
  NO2 must effectively control overall NOx
  emissions to counteract effects

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Final Words

• Coherent monitoring and modelling framework
  developed
• System, Driver and Vehicle interactions
• Real-world emissions
• Micro-scale
• Urban topography
• Urban canyon dispersion
• Extensible for future vehicles
• Opportunities for policy assessment?