Introduction to Augmented Emission Maps - 19 May 2021 Norbert Ligterink, Jessica de Ruiter - Project ucare
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Introduction to Augmented Emission Maps
19 May 2021
Norbert Ligterink, Jessica de Ruiter
Agenda • Introduction to AEMs • Break • Insights and augmentations • Discussion on your applications of AEMs 19-05-2021 GA 815002 2
Workshop Goals • You can make your own emission estimates based on measurement data • You can use and compare data from different vehicles • You can investigate specific questions regarding the relationship between driving behaviour, vehicle use, and emissions • You know where to find the large amount of data already available • You can be part of, and contribute to, the uCARe community 19-05-2021 GA 815002 3
Augmented Emission Maps are a standard to publish
and share detailed emission measurement data
The vision of uCARe: sharing emission measurement data at a detailed level is important for scientific insight
and individualised advice for consumers
Measure AEMs Analyse
Predictions
Vehicle
Insights
Driver
behaviour
Advice
19-05-2021 GA 815002 4
AEMs are much more detailed than generalised
emission factors
Diesel Euro 6 emission factors AEMs
Per road type averaged over all use and Detail the relationship between vehicle,
vehicles driver behaviour and emissions
Urban Rural Motorway
0.428 0.344 0.410
g/km NOx g/km NOx g/km NOx
19-05-2021 GA 815002 5
More detailed emission data leads to more detailed insights Current motorway emission factors would Using detailed emission data (and vehicle assign the same emissions to this stretch behaviour) shows localised variations in of road emissions 19-05-2021 GA 815002 6
The vehicle taxonomy: a common language to characterise passenger cars A method of categorising vehicles, dependent on the main (technical) properties. Allows for cross-organisation and cross-project comparison and communication • CARES, MILE21, GVI Consists of • A vehicle code, and • An engine code Especially useful in cases where a base vehicle and engine are used by different manufacturers • E.g. Peugeot 107, Citroen C1, and Toyota Aygo 19-05-2021 GA 815002 7
The right AEM can be found using the vehicle
taxonomy
First three levels of Vehicle Code Engine Code
• Vehicle code also includes AWD capability and Fuel Euro standard Engine size Engine power Alliance code
battery capacity Manual
Niro
DCT
Kia
Manual Euro 5 898 cc 66 kW RNM
Sportage 898 cc 66 kW RNM3
Automatic
ICE Petrol HYUN
Leaf 1 speed 998 cc 74 kw
Nissan FORD
All vehicles Manual
Micra
Euro 6 45 kW RNM3
Automatic 999 cc
52 kW RNM3
Manual
Fortwo 92 kW VAG
DCT 1395 cc
Smart
110 kW VAG
Manual
Forfour
DCT Alliance codes distinguish collaborations or
DCT=dual clutch transmission engines with the same specs, but different
19-05-2021 GA 815002 manufacturers 8
AEMs can be vehicle-specific or on a more general
level
Vehicle specific codes Fallback codes (more generalised)
• P_5_898_66_RNM, • D_4_ALL_ALL_ALL
• D_6dT_1969_140_VOLV • D_6dT_ALL_ALL_ALL
• E--P_6b_1798_73_TOYO • P_6_ALL_ALL_ALL
• C--P_6_1395_81_VAG • P_6_898-999_ALL_RNM3
• 2 994 identified in uCARe data Allows for ranges as well as generalisations
• 27 210 identified in Dutch fleet Taxonomy code generator updated as new
vehicles come to market
19-05-2021 GA 815002 9
Using the different layers within an AEM, analysis can be tailored to a specific vehicle and behaviour. 19-05-2021 GA 815002 10
Base maps
Base maps document hot
tailpipe emissions based on
CO2 and either RPM or vehicle
speed
~power
+/-20kW
~speed or selected gear
19-05-2021 GA 815002 11Base maps
Base maps document hot
tailpipe emissions based on
CO2 and either RPM or vehicle
speed
CO2 is a proxy for power
• Accounts for variations in use ~power
e.g. head wind, going uphill +/-20kW
~speed or selected gear
19-05-2021 GA 815002 12CO2 is used as an input signal for AEMs
• CO2 input incorporates vehicle • CO2 signal availability
use aspects • 5 – 10 % accuracy is easy to obtain
• Rolling resistance • Different CO2 signals can be used
• Weight • On-board fuel meter
• Ambient temperature • Using power relation (velocity and
• Slope acceleration)
• Eco/sportive driving • Modelling from first principles
• Measurements
• Fuel flow or exhaust flow
19-05-2021 GA 815002 13Base maps Base maps document hot tailpipe emissions based on CO2 and either RPM or vehicle speed NOx (for example) can then be estimated throughout a trip 19-05-2021 GA 815002 14
Base maps can have different resolutions
depending on the amount of data available
P_6dT_998_74_FORD – 18.4 hours P_6b_999_70_VAG – 4.6 hours
• Flexible bin sizes dependent on data availability
• Pollutant map dependent on either RPM or vehicle speed, and CO2
• Python script available, to make your own maps from data
19-05-2021 GA 815002 15Comparison: the same engine pre/post diesel gate
MY 2018 MY 2015
19-05-2021 GA 815002 16Why do base maps have this form? • Dependent variables are typically well-known and can be measured • Emissions show a strong correlation with these dependent variables • Large variations in emissions remain unexplained • This reinforces the importance of collecting sufficient real-world data to cover a larger area of the AEM 19-05-2021 GA 815002 17
Base maps are representative of the data that is
used to make them
How accurate are base maps?
• Strict separation of data and interpretation
• Metadata and indications of data spread are included to give an
indication of statistical significance
• Notes, number of vehicles, amount of data, average mileages
• Per bin/data point: count/observations, standard deviation, 25th and 75th
quantile
There are a broad range of uncertainties
• Emissions can be highly unpredictable
19-05-2021 GA 815002 18After the break • Insights from AEMs • Non-regulated pollutants • Augmentation layers • Available tools 19-05-2021 GA 815002 19
Insights from AEMs I: Low-CO2 driving is not
necessarily equivalent to low-NOx driving
Euro 5 diesel vehicles have
high NOx emissions.
CO2 eco-driving instruction:
2nd gear
high gear driving at low engine 3rd gear
speeds (~1500 RPM). 4th gear
5th gear
Low NOx instruction is to
retain a slightly lower gear, in
particular when accelerating.
Low CO2 Low NOx
19-05-2021 GA 815002 20Insights from AEMs II: Certain driving conditions
have much lower relative emissions
AEMs can be used to show
optimisations and weak spots in
the emission control strategy
For this Euro 6 diesel vehicle,
driving at speeds above 100 km/h
leads to higher NOx
Low NOx
19-05-2021 GA 815002 21Base maps are also available (where measured) for other and non-regulated pollutants. Carbon monoxide Ammonia 19-05-2021 GA 815002 22
Augmentation layers can be used to further specify
emissions
Currently two augmentations are
available
• Cold start
• Cold start extra emissions due to
driving with a cold engine
• Deterioration
• Scaling emissions due to the effect
of aging on a vehicle
19-05-2021 GA 815002 23Augmentation: Cold start
• Unlike the emission base map, cold start
Cold start extra emissions are time-dependent
• Augmentation in the form of a formula
• Time-dependent
• Formula requires a number of vehicle-
formula
dependent parameters
• Vehicle-dependent
• Fallback parameters have been generated and
and fallback are available in AEM form
parameters
• Can estimate the extra emissions for trip
sequences, or driver behaviour at the start
19-05-2021 GA 815002 24Insights from AEMs III: Re-ordering trips can lead to
lower emissions
• Cold start extra emissions can be estimated depending on trip
characteristics
• The order of the segments in a segmented trip can significantly
influence total cold start extra emissions
• Decrease of over 20% if the long segment is driven first
19-05-2021 GA 815002 25Augmentation: Deterioration
• Investigated formula vs. scaling factor
Deterioration approach
• CONOX/HBEFA deterioration factors used
• Dependent on
scaling factors • Deterioration factors used to scale entire
• CONOX/HBEFA base map
50 000 → 300 000
× 3.00
19-05-2021 GA 815002 26AEMs are shared via a text file format that is
human-readable
• File format allows for
concatenation of multiple
maps/augmentations
• A formal explanation of
the map.txt file is
documented in Backus-
Naur form
19-05-2021 GA 815002 27Three tools are available to assist in map selection
• Base maps available for a many common engines
• Many different engine types (taxonomy codes) in the European fleet; order
of magnitude: 22,000 (based on Dutch fleet)
• Three tools developed to help tool-builders:
• Fallback maps.
• One map based on joint data of all vehicles of a certain fuel/Euro standard.
• Selection tool.
• Sophisticated tool that analyses and characterises available maps, and proposes the best
suitable map for any given engine. Differences in emission behaviour related to engine size,
power, and make/alliance are accounted for.
• Combining tool.
• Combines base maps with matching taxonomy code from different sources (e.g. same engine
measured on dyno and with PEMS).
19-05-2021 GA 815002 28Discussion/questions • How can the additional detail in AEMs help you? • How/where will you implement AEMs? • What kind of other applications can you imagine for AEMs? • … 19-05-2021 GA 815002 29
Why use AEMs?
Fact-based • AEMs contain only measurement data-backed values
• Data is freely available via the OpenAire platform Zenodo to which new data can and will
Freely-available
continually be added as new measurements become available.
• AEMs can be vehicle-specific or generalised, depending on data availability and
Vehicle-specific or generalised
application
• Via AEMs and their surrounding framework, we facilitate easy data sharing of emission
Easy data sharing
measurements.
• The accuracy and applicability of AEMs is ensured via the significant amount of data that
Significant amount of data is used to generate each map, which is noted in kilometres and hours in the metadata of
each map.
• The ready availability of vehicle-specific and generalised AEMs, as well as the supporting
Readily available
tools, make this a solution that is ready for tool-builders and researchers to implement.
• Emission data can be shared in the form of maps dependent on any two variables, or in
Flexible the form of a function. This allows for easy integration of new pollutants and
relationships as they become available.
19-05-2021 GA 815002 30Useful links
• uCARe deliverables
• Taxonomy code generator v1.5
• D1.2 “Augmented emission maps are an essential new tool to share and
investigate detailed emission data”
• Zenodo uCARe community
• GitHub
• Intro to AEMs on YouTube
19-05-2021 GA 815002 31Consortium partners:
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