Marc stettler modelling of instantaneous vehicle emissions - dmug17

CI9-T-16 Transport and Environment

Modelling instantaneous vehicle emissionsMarc Stettler, Rosalind ODriscoll, Helen ApSimon, Simon Hu, Jiahui Yang, Yiheng Guo, Justin Bishop, Adam Boies, Nick Molden

[email protected] | www.imperial.ac.uk/people/m.stettlerCentre for Transport Studies | Department of Civil and Environmental Engineering Imperial College London@TransEnvLab_IC

6th April 2017Institute of Air Quality Management DMUG 2017

OutlineMotivation

Overview of road transport emissions models

Introduction to PEMS data

New emissions models Emissions maps from PEMSNeural networks

Preliminary application

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MotivationUrban air pollution challenges8% of Europeans1 exposed to harmful levels of NO2Major contribution from transportUK cities required to bring air quality into compliance with regulations

Shortcomings of standard emissions models Uncertainty propagates to forecasts of urban air qualityLimited 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 Oct3

esa.int

1EEA, Air Quality in Europe 2014 Report, EEA Report No 5/2014European Environment Agency, Copenhagen, Denmark (2014)

18 exceedances of 200gm3 NO23

Air quality (NO2) in LondonEURO 3EURO 4EURO 5EURO 64NO2 concentrations have not improved even though vehicle emissions standards have become stricter

Road transport emissions modelsAverage speed COPERT

Vehicle specific powerEPA Motor Vehicle Emissions Simulator (MOVES)International Vehicle Emissions (IVE) modelAlso used for microscopic prediction

Drive cycle (traffic) parametersVERTSIT+ (TNO)EnViVer plug-in for PTV VissimHBEFA

Engine/emissions mapsPHEM (TU Graz)AIRE plug-in for S-Paramics

Physics-basedComprehensive Model Emissions Model (CMEM)CMEM plug-in for Paramics

Function of vehicle speed and acceleration - f(v,a)Luc Int Panis et al. (2006)

Black-boxNeural networks

5Street-level and upMicroscopic (instantaneous)

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Engine load6

FliftFpropulsionFweightFrollFdragFnormalFpropulsion = Fdrag + Froll + Fgrade + ma

FgradeThe 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 sin

Average speed emissions: COPERT7Emissions are predicted for different vehicle speed (v) using different functions based on fits to experimental drive cycle emissions data:

http://emisia.com/products/coperthttp://www.eea.europa.eu/publications/emep-eea-guidebook-2016 Street-level and up

COPERT uncertainties: emissions variability

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

Vehicle specific power: MOVES (EPA)Power = Force velocityVehicle specific power (VSP) is the engine power divided by the vehicle mass9

https://www.epa.gov/moves Street-level and up

Statistical regression: VERSIT+ (TNO)10

http://www.sciencedirect.com/science/article/pii/S1361920907000521 Street-level and up

Engine maps: PHEM (TU Graz)

11http://www.ivt.tugraz.at/index.php?option=com_content&view=article&id=69&Itemid=301&lang=en Microscopic

Physics-based: CMEM (UC Riverside)

12http://www.cert.ucr.edu/cmem/docs/CMEM_User_Guide_v3.01d.pdf Microscopic

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 vehicle13Int Panis et al. (2006) Modelling Instantaneous Traffic Emission and the Influence of Traffic Speed Limits. Science of The Total Environment 371(1): 27085 http://www.sciencedirect.com/science/article/pii/S004896970600636X

Microscopic

Neural networksBlack-box approach (model structure is learned)Non-linear NOx formation processesEngine emissions and effectiveness of a series of emissions control devices is highly complexExhaust gas re-circulationDiesel oxidation catalystDiesel particulate filterNOx control (selective catalytic reduction or lean NOx trap)Ammonia slip catalyst14Inputs:SpeedAccelerationVSPRPMExhaust temperatureOutput:NOx (g/s)Fuel consumption

Microscopic

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 limitSignificant variability between manufacturer/modelDiscrepancy between laboratory and track/road testingNOx emissions sensitive toAccelerationAmbient temperatureEmissions control technology state

Alternative vehicles/powertrainsHybrid electric vehiclesRange extended electric vehiclesConnected and autonomous vehicles

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Opportunity to use PEMS dataOn-road emissions for >1000 vehicles, 2-3 new vehicles added each week

Use real-world emissions data to develop instantaneous models:Extract emissions maps from PEMS real-world emissions data (1 Hz) (i.e. similar to PHEM)Use a neural network technique and evaluate different inputs and data processingDemonstrate use of instantaneous emissions models to evaluate the emissions benefits of connected and autonomous vehicles

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PEMS dataDataset 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 unitNOx measured by non-dispersive UV17

ODriscoll et al. 2016. Atmospheric Environment 145: 8191.http://www.sciencedirect.com/science/article/pii/S135223101630721X

Vehicle speed and NOx emissions profile18


Real-world emissions complianceData below is for 39 diesel Euro 6 vehiclesNot-to-exceed (NTE) limits for Euro 6c (RDE)2.1 (0.168 g/km) from Sep 20171.5 (0.12 g/km) from 2020A few vehicles with different emissions control devices already complyLNT (L), SCR (S)

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ODriscoll et al. 2016. Atmospheric Environment 145: 8191.http://www.sciencedirect.com/science/article/pii/S135223101630721X www.equaindex.com

Comparison to COPERTCOPERT underestimates compared to the average over all vehicles measured in real-world over the entire cycleMeasured NOx and NO2 are 1.6 and 2.5 times higher than COPERT20


Approach: Bottom-up emissions modellingEmissions map models relies on extracting lookup tables of emissions (i.e. emissions as a function of engine speed and torque):

Use PEMS measurements and on-board diagnostics (OBD) data (e.g. engine speed) to extract effective gear ratios in order to calculate engine torque.

Extract the emissions measurements for a given engine speed and torque range.

Simulate a given drive cycle (vehicle speed versus time)Bishop, et al. 2016. Applied Energy 183: 20217.http://www.sciencedirect.com/science/article/pii/S0306261916312843

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JB: Add flow schematic from last year and citation, send last years presentation21

1. Extracting gear ratios (e.g. for truck)22

Bishop, et al. 2016. Applied Energy 183: 20217.http://www.sciencedirect.com/science/article/pii/S0306261916312843

2. Extracting emissions maps23

Bishop, et al. 2016. Applied Energy 183: 20217.http://www.sciencedirect.com/science/article/pii/S0306261916312843

2. Extracting emissions maps (passenger cars)

log(NOx) in g/slog(NOx) in g/slog(NOx) in g/slog(NOx) in g/s24Bishop et al., (2017). Int. J. Transp. Dev. Integr., Vol. 1, No. 2 (2017)

JB: Units on scale and have logarithmic if possibleLog scale added, values are negative because they are less than 124

3. Transient emissions prediction (1Hz)

25Bishop et al., (2017). Int. J. Transp. Dev. Integr., Vol. 1, No. 2 (2017)

Remove fuel25

Neural network modelEvaluate:Different ways to define the training data setDifferent input dataSpeedAccelerationVSPRPMExhaust temperature

Number of hidden layers and data averaging not discussed26Output:NOx (g/s)Fuel consumption

Preliminary please do not cite or quote

Inputs from PEMS data

Training data set sampling27

Sequential (default)

Random selection of data blocks (5 seconds)

Training(65%)Validation (15%)Testing (20%)Preliminary please do not cite or quote

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Accuracy depends on input data to NNScenarioVehicle speedVSPAccelerationEngine speedExhaust temperatureR210.19-0.6820.43-0.6530.47-0.7240.48-0.7350.66-0.8560.65-0.84

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Comparison of NN and f(v,a)ANN: neural network calibrated to each vehicleSelf-calibrated: f(v,a) calibrated to individual vehicleCalibrated for all: f(v,a) calibrated to combination of five vehiclesPanis 2006: f(v,a) same as from Int Panis et al. (2006)29


What if we only have vehicle speed and acceleration data?

Comparison of NN and f(v,a)

30Preliminary please do not cite or quote

Accuracy is currently inconsistent

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Summary and future workInstantaneous emissions modelling for NOx Better able to capture high emissions events but missing the peaksNeeds 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 startPrimary 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 required32

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

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Optimised Vehicle Autonomy for Ride and Emissions (OVARE)

CAV modelling V2V vehicle to vehicleV2I vehicle to infrastructure

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Traffic simulation detailsVISSIM South Kensington traffic modelModelled period: 7:30 to 9:30 AM peak modelTotal number of links: 333Number of junctions: 20Each simulation run: 20 minutes (@ CPU:3.1 GHz, RAM: 8 GB)

Calibration of the modelTraffic flow is calibrated against survey data collected at key locations on the networkRouting choice is calibrated based on O-D surveySignal timing is used for existing fixed timing

Instantaneous emissions modelUse f(v,a) calibrated to one vehicle (for preliminary analysis)

35Preliminary please do not cite or quote

CAV simulation around South Kensington36


0% CAV100% CAV37(Preliminary) emissions benefits of CAVsPreliminary please do not cite or quote

Thank you!Marc Stettler, Rosalind ODriscoll, Helen ApSimon, Simon Hu, Jiahui Yang, Yiheng Guo, Justin Bishop, Adam Boies, Nick Molden

[email protected] | www.imperial.ac.uk/people/m.stettler@TransEnvLab_ICCentre for Transport Studies | Department of Civil and Environmental Engineering Imperial College London

6th April 2017Institute of Air Quality Management DMUG 2017