gpf model-based optimization methodologies supporting rde

GPF model-based optimization methodologiessupporting RDE conformity

Michail Mitsouridis, Aristotle University ThessalonikiGrigoris Koltsakis, Aristotle University ThessalonikiZissis Samaras, Aristotle University ThessalonikiStefan-Herbert Veit, AVL List GmbHChristian Martin, AVL List GmbH

TAP conference 2019

• The results presented here were obtained within the project UPGRADE.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 724036.

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Acknowledgements

TAP conference 201917/5/2019

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UPGRADE project structure


4

Motivation


Ultra-clean gasoline engines via (c)GPF & Exhaust After Treatment (EAT) system optimization

In this respect:– 13 (c)GPFs of different cell- and wall micro-structure were tested

– Based on the test results, predictive deltaP and filtration models were developed

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ModelInputs / outputs


INPUTS

Mass flow rate

Temperature

Soot emissions

DESIGN & CONTROL PARAMETERS

Sizing

Catalyst technology

Substrate technology

Positioning

Engine fueling strategies

…

OUTPUTS

Filtration efficiency

deltaP

MODEL

Flow modeling

Filtration modeling

Kinetic modeling

Heat transfer modeling

deltaP model

• Soot-free wall permeability

• Soot loaded wall permeability

• Soot cake permeability

Filtration model

• Diffusion coefficient

• Interception coefficient

• Gradient coefficient

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ModelTunable parameters


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Predictive flow resistance toolas a function of wall micro-structure


1 8 3

6

B

A A

1

8 3

6 B

GPF A (UF: 100g/l) and B (CC: 50g/l)deltaP and filtration performance investigation

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deltaP & filtration efficiency prediction forcGPF A and cGPF B


• Experimental & simulation deltaP and filtration efficiency results.• SYSTEM B has better deltaP and worse filtration performance compared to SYSTEM A due to its higher

effective porosity.

UF: 100 g/l CC: 50 g/l

(100g/l w/c loading) (50g/l w/c loading)

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Model validationby driving cycle tests


The original exhaust system was removed and replaced with exhaust

systems of two different variants:

(100g/l w/c loading)

UF: 100 g/l

CC: 50 g/l

(50g/l w/c loading)

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deltaP model validationRDE aggressive test cycles


Soot lo

aded

initia

l sta

te

0.1 g/l 0.4 g/l

UF: 100 g/l CC: 50 g/l

Soot-

free

initia

l sta

te

The model is able to accurately predict deltaP for filters at both soot-free and soot loaded conditions

0 g/l 0 g/l

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Effect of soot loading on filtration performanceRDE moderate test cycle

• ~500°C appears to be the threshold above which soot oxidation takes place• The model is able to predict the significant effect of soot accumulation on a

GPF’s filtration performance

FE: 98% FE: 98%

FE: 86%FE: 87%

UF: 100 g/l CC: 50 g/l

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Effect of cGPF placement on filtration performanceRDE aggressive test cycle

CC 50 g/l GPF regenerates faster due to higher temperature➔ soot mass drops below 100% FE threshold level

UF: 100 g/l CC: 50 g/l

FE: 86%

0.16

FE<99.5%

FE<99.5%

0.02 g/l 0.16 g/l

FE: Filtration Efficiency

0.02


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Validation of model explanation with FCOff deactivationRDE aggressive test cycle

withfuel cut-off events

withoutfuel cut-off events

FE: 86%

FE: 98%

To validate that the CC filter deteriorated filtration performance is associated to its enhanced passive regeneration capability, the test was repeated de-activating fuel cut-off events to limit passive regeneration

By deactivation of fuel-cut off mode the GPF retains its high filtration efficiency

CC: 50 g/l

FCOff: Fuel Cut-Off

• Soot loading has an important beneficial effect to maintain RDE compliance w.r.t. filtration efficiency.

➔Careful management of the regeneration events desirable to avoid complete regenerations

• Multitude of exhaust line design parameters, such as:

– filter positioning (affecting temperature levels)

– washcoat amount (affecting filtration performance)

– engine fueling strategies

create a very demanding optimization problem.

➔ Employment of model-based methodologies can reduce development efforts and associated costs

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Conclusions


Thank you very much!

17/5/2019 15SIA Powertrain, Rouen 2014

TAP conference 2019

gpf model-based optimization methodologies supporting rde

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