EMROAD evaluation RDE-LDV meeting on the 1st and 2nd February 2017

Contribution from the Netherlands

Introduction • Independent software implementation compares perfect with JRC tool

• How EMROAD normalize the test data

• Small effect of the weighing factors for the windows

• Large effect and ambiguity in RDE: forward versus backward windows

• Effect of the window size variation

• EMROAD method and the large variation in the correction of the data for different vehicles and RDE trips

• NL proposal for fair weighing and averaging of all seconds in the urban trip

• NOx/CO2 method evaluation option for conventional vehicles

Validation of EMROAD by independent implementation based solely on Appendix 5

EMROAD Our Python implementation captures all the features of the JRC EMROAD package

3 decimals identical

EMROAD normalizes on the road emissions

• Normalization with respect to WLTP CO2 –emissions (blue).

• Variable lower tolerance: 25-30% (red). And fixed upper tolerance: 50% (green).

• 50 % of windows (black) required within lower tolerance.

• 15 % of windows required in each of three speed bins (grey: URBAN, RURAL, MOTORWAY).

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Windows are weighed 1 within lower tolerance (red), decreasingly from 1 to 0 within upper tolerance (green) and 0 outside upper tolerance.

Small effect of weighing

• For the RDE trip depicted on the previous sheet the right top picture shows the weighing factors. In blue the weighing factors for the windows that fall between the lower and upper tolerance and in red the windows that are within the primary tolerance.

• The picture below illustrates the effect of weighing (blue line) on the windows (red line) expressed in mg NOx per km.

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Window sizes varies with direction

• Window sizes are defined to be half of the CO2 emitted during half the WLTP cycle.

• Window sizes vary in total amounts of seconds included and become smaller as vehicle speed increases (depicted in the two graphs on the right).

• The number and distribution of window sizes changes with the direction of the MAW.

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forward

backward end with first

end with last

Differen

ce in n

um

be

r of w

ind

ow

s

Difference in results: forward vs backward • We find a large difference

between forward and backward MAW on the total trip [g/km] averages of a set of warm start EUR6 vehicles.

• Forward MAW can correct the data upwards more than 20% with respect to backward.

• It is advised that the legal text explicitly mentions whether the method starts windows from the first second forward or from the last second backward.

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Effect of averaging • Depicted here is the averaging

(orange line) of the measured emissions (black dots).

• The top graph shows the averaging for forward MAW starting from the first second.

• The bottom graph shows the averaging for backward MAW starting from the last second.

• Note the gap (forward end, backward start) that is larger for backward MAW.

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forward

backward

High emissions in more (i.e. longer) forward windows

High emissions in fewer (i.e. shorter) backward windows

“Counting windows” • To validate the EMROAD MAW

method we have developed a function “counts” that evaluates how often a second of a RDE trip is included in the averaging.

• From the ”trapezium”-like shape it is clear that the starting seconds are underweighed as well as the seconds towards the end of the trip.

• The stark difference in counts between seconds is inherent of the EMROAD MAW method and the underlying reason for the variation in corrections from trip to trip.

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forward

backward

Accelerations in more forward windows than backward windows

Ambiguous outcomes (TNO report 11227, Oct. 2016)

• Despite one (backward) implementation of the EMROAD method in the JRC package a large variation is observed in the correction of the data for different vehicles and RDE trips.

• Also the difference between EMROAD and CLEAR corrections is pronounced.

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Looping for cold start • The EMROAD MAW method can be

improved by “looping” the windows from the last seconds back to the first.

• The bottom figure shows the increased counts emanating from this looping and a more equal distribution of counts among seconds.

• With respect to the regular EMROAD method (top figure) this is an improvement

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regular

“looped”

Urban: 81% < 60 km/h Rural: 60 < 15% < 90 km/h Motorway: 4% > 90 km/h

more even weighing

Correction on the data with looping

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• Looping the windows from start to end lowers the correction on the data.

• It confirms that more equal “counts” over seconds is the method to limit corrections on the data.

• The lack of major improvement confirms that the variation of window sizes dominates for the total trip and causes unequal “counts”.

urban part

separate RDE TEST urban evaluation: looping option

take out the urban part

glue the end to the start of the urban test

running the MAW around over the whole circle

giving every second equal weight

Urban loop: option 2 • An important problem with the EMROAD

MAW is the downward correction of data in the urban part of a RDE trip.

• For this we developed an “urban loop” that loops the first third of the data as in the top illustration.

• The result of this looping on the distribution of counts (bottom blue graph) for every second is almost perfect.

• We verified that 99,95% of the data in the first third of the RDE trip has a speed below 60 km/h and thus well reflects the urban part of the averaging.

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NOx/CO2 method (hybrid evaluation method) • Variation of NOx emission with RDE trip is expected and covered by the

evaluation methods: • Driving behaviour, payload, road gradient, braking, gear choice

• Increase of NOx emissions proportional with increase in CO2 emissions is acceptable: a constant fraction NOx/CO2 , i.e., a “flat emission ratio map”

NOx [g/s] CO2 [g/s] NOx/CO2 [g/kg]

High fractions with low absolute emissions will disappear in hybrid (sum NOx)/(sum CO2) velocity [km/h]

acce

lera

tio

n [

m/s

2]

factor 100

factor 10

comparable

factor 100

contribution limited by v*apos(95%) bound

READING EMISSION RATIO MAPS

low concentrations of both NOx and CO2 (i.e., 0/0) may yield high numbers due to uncertainties. Irrelevant for the total emissions in the (sum NOx)/(sum CO2) per velocity bin.

moderate driving ~ 5 mg NOx per g CO2

some acceleration ~ 10 mg NOx per g CO2

Given this ratio map: RDE test with 120 g/km CO2 yields 600 – 1200 mg/km NOx

RDE test with 240 g/km CO2 yields 1200 – 2400 mg/km NOx

NOx/CO2 emission ratio maps of 15 Euro-6

NOx/CO2 varies typically a factor 10 or less

0/0 0/0 0/0 0/0 0/0

0/0

0/0 0/0 0/0 0/0 0/0

0/0 0/0 0/0 0/0

Conclusions

• Independent software implementation based on the appendix 5 alone yields the same results (backward) as the JRC EMROAD tool.

• EMROAD leads to large corrections mainly due to the windows size variation • backward/forward ambiguity in the legislation

• Looping windows will lead to more consistent corrections and better coverage of the urban part and cold start emissions

• NOx/CO2 method for hybrids can be used to evaluate conventional technology, compensates for variations in payload and driving.