DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.

1. Modelling instantaneous vehicle
emissions
Marc Stettler, Rosalind O’Driscoll, Helen ApSimon, Simon Hu, Jiahui Yang, Yiheng
Guo, Justin Bishop, Adam Boies, Nick Molden
m.stettler@imperial.ac.uk | www.imperial.ac.uk/people/m.stettler
Centre for Transport Studies | Department of Civil and Environmental Engineering
Imperial College London
@TransEnvLab_IC
6th April 2017
Institute of Air Quality Management – DMUG 2017

2. Outline
1. Motivation
2. Overview of road transport emissions models
3. Introduction to PEMS data
4. New emissions models
i. Emissions maps from PEMS
ii. Neural networks
5. Preliminary application
2

3. Motivation
• Urban air pollution challenges
• 8% of Europeans1 exposed to harmful
levels of NO2
• Major contribution from transport
• UK cities required to bring air quality into
compliance with regulations
• Shortcomings of standard emissions
models
• Uncertainty propagates to forecasts of
urban air quality
• Limited high temporal & spatial resolution
modelling
• Limited detail in emissions mechanisms
and chemistry
• Real-world diesel emissions are around 5
(1-22) times higher than RDE limit from Oct
3
esa.int
1EEA, Air Quality in Europe – 2014 Report, EEA Report No 5/2014 European Environment Agency, Copenhagen, Denmark (2014)

4. Air quality (NO2) in London
0
20
40
60
80
100
120
140
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Annualaverage[NO2](µg/m3)
Year
Marylebone Knightsbridge Regulatory value
EURO 3 EURO 4 EURO 5 EURO 6
4
NO2 concentrations have not improved even though
vehicle emissions standards have become stricter…

5. Road transport emissions models
1. Average speed
• COPERT
2. Vehicle specific power
• EPA Motor Vehicle Emissions
Simulator (MOVES)
• International Vehicle Emissions
(IVE) model
• Also used for microscopic
prediction
3. Drive cycle (traffic) parameters
• VERTSIT+ (TNO)
• EnViVer plug-in for PTV Vissim
• HBEFA
4. Engine/emissions maps
• PHEM (TU Graz)
• AIRE plug-in for S-Paramics
5. Physics-based
• Comprehensive Model Emissions
Model (CMEM)
• CMEM plug-in for Paramics
6. Function of vehicle speed and
acceleration - f(v,a)
• Luc Int Panis et al. (2006)
7. Black-box
• Neural networks
5
Street-level and up Microscopic (instantaneous)

6. Engine load
6
θ
Flift
Fpropulsion
Fweight
Froll
Fdrag
Fnormal
Fpropulsion = Fdrag + Froll + Fgrade + ma
Fgrade
The propulsive force is equal to the sum of resistive,
gradient and inertial forces:
F = force (N)
m = mass (kg)
a = acceleration (m/s2)
Fgrade = mg sinθ

7. Average speed emissions: COPERT
7
• Emissions are predicted for different vehicle speed (v) using different
functions based on fits to experimental drive cycle emissions data:
http://emisia.com/products/copert
http://www.eea.europa.eu/publications/emep-eea-guidebook-2016
Street-level and up

8. COPERT uncertainties: emissions variability
8
http://emisia.com/sites/default/files/COPERT_uncertainty.pdf
Street-level and up

9. Vehicle specific power: MOVES (EPA)
• Power = Force × velocity
• Vehicle specific power (VSP) is the engine power divided by the vehicle
mass
9
https://www.epa.gov/moves
Street-level and up

10. Statistical regression: VERSIT+ (TNO)
10
http://www.sciencedirect.com/science/article/pii/S1361920907000521
Street-level and up

11. Engine maps: PHEM (TU Graz)
11
http://www.ivt.tugraz.at/index.php?option=com_content&view=article&id=69&Itemid=301&lang=en
Microscopic

12. Physics-based: CMEM (UC Riverside)
12
http://www.cert.ucr.edu/cmem/docs/CMEM_User_Guide_v3.01d.pdf
Microscopic

13. Correlation to speed and acceleration: f(v,a)
• The emissions rate (ER) is a function of vehicle speed (v) and
acceleration (a) – f(v,a)
• Calibration parameters (f) that are specific to each vehicle
13
Int Panis et al. (2006) Modelling Instantaneous Traffic Emission and the Influence of Traffic Speed Limits. Science of
The Total Environment 371(1): 270–85
http://www.sciencedirect.com/science/article/pii/S004896970600636X
ER 𝑛 𝑡 = max[𝐸0, 𝑓1 + 𝑓2 𝑣 𝑛 𝑡 + 𝑓3 𝑣 𝑛 𝑡 2 + 𝑓4 𝑎 𝑛 𝑡 + 𝑓5 𝑎 𝑛 𝑡 2 + 𝑓6 𝑣 𝑛 𝑡 𝑎 𝑛 𝑡 ]
Microscopic

14. Neural networks
• Black-box approach (model structure is learned)
• Non-linear NOx formation processes
• Engine emissions and effectiveness of a series of emissions control
devices is highly complex
• Exhaust gas re-circulation
• Diesel oxidation catalyst
• Diesel particulate filter
• NOx control (selective catalytic reduction or lean NOx trap)
• Ammonia slip catalyst
14
Inputs:
• Speed
• Acceleration
• VSP
• RPM
• Exhaust temperature
Output:
• NOx (g/s)
• Fuel consumption
Microscopic

15. Why do we need to develop more models?
• Compare approaches and continuously improve air quality impact
estimates
• Latest real-world emissions test data indicates:
• Diesel Euro 6 passenger car emissions are ~5 times higher than limit
• Significant variability between manufacturer/model
• Discrepancy between laboratory and track/road testing
• NOx emissions sensitive to
• Acceleration
• Ambient temperature
• Emissions control technology state
• Alternative vehicles/powertrains
• Hybrid electric vehicles
• Range extended electric vehicles
• Connected and autonomous vehicles
15

16. Opportunity to use PEMS data
On-road emissions for >1000 vehicles, 2-3 new
vehicles added each week
Use real-world emissions data to develop instantaneous models:
1. Extract emissions maps from PEMS real-world emissions data (1 Hz)
(i.e. similar to PHEM)
2. Use a neural network technique and evaluate different inputs and data
processing
3. Demonstrate use of instantaneous emissions models to evaluate the
emissions benefits of connected and autonomous vehicles
16

17. PEMS data
• Dataset includes Euro 5 and 6 petrol
and diesel vehicles
• Mixed motorway/non-motorway
route
• ~80 km distance
• ~9100 s duration
• Sensors Inc Semtech-DS PEMS
with GPS unit
• NOx measured by non-dispersive UV
17
O’Driscoll et al. 2016. Atmospheric Environment 145: 81–91.
http://www.sciencedirect.com/science/article/pii/S135223101630721X

18. Vehicle speed and NOx emissions profile
18
O’Driscoll et al. 2016. Atmospheric Environment 145: 81–91.
http://www.sciencedirect.com/science/article/pii/S135223101630721X

19. Real-world emissions compliance
• Data below is for 39 diesel Euro 6 vehicles
• Not-to-exceed (NTE) limits for Euro 6c (RDE)
• ×2.1 (0.168 g/km) from Sep 2017
• ×1.5 (0.12 g/km) from 2020
• A few vehicles with different emissions control devices already comply
• LNT (L), SCR (S)
19
O’Driscoll et al. 2016. Atmospheric Environment 145: 81–91.
http://www.sciencedirect.com/science/article/pii/S135223101630721X
www.equaindex.com

20. Comparison to COPERT
• COPERT underestimates compared to the average over all vehicles
measured in real-world over the entire cycle
• Measured NOx and NO2 are 1.6 and 2.5 times higher than COPERT
20
O’Driscoll et al. 2016. Atmospheric Environment 145: 81–91.
http://www.sciencedirect.com/science/article/pii/S135223101630721X

21. Approach: Bottom-up emissions modelling
Emissions map models relies on extracting lookup tables of emissions (i.e.
emissions as a function of engine speed and torque):
1. Use PEMS measurements and on-board diagnostics (OBD) data (e.g.
engine speed) to extract effective gear ratios in order to calculate
engine torque.
2. Extract the emissions measurements for a given engine speed and
torque range.
3. Simulate a given drive cycle (vehicle speed versus time)
Bishop, et al. 2016. Applied Energy 183: 202–17.
http://www.sciencedirect.com/science/article/pii/S0306261916312843
21

22. 1. Extracting gear ratios (e.g. for truck)
22
𝜏 N ∙ m =
𝐹engine 𝑟tyre
𝜙gear 𝜙finalBishop, et al. 2016. Applied Energy 183: 202–17.
http://www.sciencedirect.com/science/article/pii/S0306261916312843

23. 2. Extracting emissions maps
23
Bishop, et al. 2016. Applied Energy 183: 202–17.
http://www.sciencedirect.com/science/article/pii/S0306261916312843

24. 2. Extracting emissions maps (passenger cars)
log(NOx) in
g/s
log(NOx) in
g/s
log(NOx) in
g/s
log(NOx) in
g/s
24
Bishop et al., (2017). Int. J. Transp. Dev. Integr., Vol. 1, No. 2 (2017)

25. 3. Transient emissions prediction (1Hz)
25
Bishop et al., (2017). Int. J. Transp. Dev. Integr., Vol. 1, No. 2 (2017)

26. Neural network model
Evaluate:
1. Different ways to define the training data set
2. Different input data
• Speed
• Acceleration
• VSP
• RPM
• Exhaust temperature
• Number of hidden layers and data averaging not discussed
26
Output:
• NOx (g/s)
• Fuel consumption
Preliminary – please do not cite or quote
Inputs from
PEMS data

27. Training data set sampling
27
Sequential
(default)
Random selection
of data blocks
(5 seconds)
Training
(65%)
Validation
(15%)
Testing
(20%)
Preliminary – please do not cite or quote

28. Accuracy depends on input data to NN
Scenario Vehicle
speed
VSP Acceleration Engine
speed
Exhaust
temperature
R2
1  0.19-0.68
2  0.43-0.65
3   0.47-0.72
4    0.48-0.73
5    0.66-0.85
6     0.65-0.84
28
Preliminary – please do not cite or quote

29. Comparison of NN and f(v,a)
• ANN: neural network calibrated to each vehicle
• Self-calibrated: f(v,a) calibrated to individual vehicle
• Calibrated for all: f(v,a) calibrated to combination of five vehicles
• Panis 2006: f(v,a) same as from Int Panis et al. (2006)
29
Preliminary – please do not cite or quote
ER 𝑛 𝑡 = max[𝐸0, 𝑓1 + 𝑓2 𝑣 𝑛 𝑡 + 𝑓3 𝑣 𝑛 𝑡 2 + 𝑓4 𝑎 𝑛 𝑡 + 𝑓5 𝑎 𝑛 𝑡 2 + 𝑓6 𝑣 𝑛 𝑡 𝑎 𝑛 𝑡 ]
What if we only have vehicle speed and acceleration data?

30. Comparison of NN and f(v,a)
30
Preliminary – please do not cite or quote

31. Accuracy is currently inconsistent
31
Preliminary – please do not cite or quote

32. Summary and future work
• Instantaneous emissions modelling for NOx
• Better able to capture high emissions events but missing the peaks
• Needs further validation and improvement compared to real-world PEMS
data
• Challenges:
• Do we need emissions data for each vehicle on the road, and is this
feasible?
• Can we obtain accurate instantaneous vehicle trajectories (and other data)
for each vehicle?
• How to account for ambient temperature effects and cold start
• Primary NO2, PM, NH3, N2O
• In order to estimate air quality at the city scale, a hybrid approach using
data from GPS, ANPR, traffic cameras and remote sensing is likely to
be required
32

33. What is a CAV?
Connected Autonomous
• Vehicle can communicate
with other vehicles (V2V)
• Vehicle can communicate
with road infrastructure
(V2I)
• Vehicles have different longitudinal
behaviour
• Vehicles have different lateral
behaviour
• Vehicles have better throttle control
(Preliminary) Emissions benefits of CAVs
CAV ≠ zero emissions
33
Optimised Vehicle Autonomy for Ride
and Emissions (OVARE)

34. CAV modelling
V2V – vehicle to vehicle
V2I – vehicle to infrastructure
34

35. Traffic simulation details
• VISSIM South Kensington traffic model
• Modelled period: 7:30 to 9:30 AM peak model
• Total number of links: 333
• Number of junctions: 20
• Each simulation run: 20 minutes (@ CPU:3.1 GHz, RAM: 8 GB)
• Calibration of the model
• Traffic flow is calibrated against survey data collected at key locations on
the network
• Routing choice is calibrated based on O-D survey
• Signal timing is used for existing fixed timing
• Instantaneous emissions model
• Use f(v,a) calibrated to one vehicle (for preliminary analysis)
35
Preliminary – please do not cite or quote

36. CAV simulation around South Kensington
36
Preliminary – please do not cite or quote

37. 0% CAV 100% CAV
37
(Preliminary) emissions benefits of CAVs
Preliminary – please do not cite or quote

38. Thank you!
Marc Stettler, Rosalind O’Driscoll, Helen ApSimon, Simon Hu, Jiahui Yang, Yiheng
Guo, Justin Bishop, Adam Boies, Nick Molden
m.stettler@imperial.ac.uk | www.imperial.ac.uk/people/m.stettler
@TransEnvLab_IC
Centre for Transport Studies | Department of Civil and Environmental Engineering
Imperial College London
6th April 2017
Institute of Air Quality Management – DMUG 2017