DEVELOPMENT OF A FAST RUNNING LEAN … research, advanced studies & materials ... development of a fast running lean noxtrap model for simulation of real customer drive cycles d.maroteaux,

RENAULTRESEARCH, ADVANCED STUDIES & MATERIALS ENGINEERING OCTOBER 25, 2010 RENAULT PROPERTY

DEVELOPMENT OF A FAST RUNNING LEAN NOx TRAP MODEL FOR

SIMULATION OF REAL CUSTOMER DRIVE CYCLESD.MAROTEAUX, P.DARCY - F.LORMIER - E.PAUTASSO - W.WANG, S.WAHIDUZZAMAN

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RENAULTRESEARCH, ADVANCED STUDIES & MATERIALS ENGINEERING OCTOBER 25, 2010

01 BAKGROUND

02 PROCEDURE

03 NEURAL NETWORK MODEL HYBRID NEURAL NETWORK & ARCHITECTURE

04 NEURAL NETWORK MODEL RESULTS

05 CONCLUSION

SUMMARY

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BACKGROUND

01

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Renault NOx Trap Technology

� First application : Renault Espace dCi 175 Euro 5

� Sold in Europe since mid 2009

� A technology that will be applied on Euro 6 high-end veh icles.

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Design of the NOx Trap system (1/2)

DPF

DPF

DPF

NOx Trap

NOx Trap

NOx Trap

1/ Architecture: NOx Trap position in the exhaust line

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2/ Catalyst characteristics:

� NOx Trap catalyst volume,

� NOx Trap coating, …

3/ Constraints :

� Emissions on NEDC / resp. FTP75 – US06 test cycles

� Compatibitily with real customer use (city, road, highway) and serviceability targets

Design of the NOx Trap system (2/2)

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Real Customer use

� Real customer use = various drive cycles

� City (including heavy traffic conditions & slow average speed)

� Road

� Highway

� Mixed

On distances up to several x 10.000 km

� A challenge for simulation experts :

� Even at a real-time speed simulation duration is prohibitive !

� Accuracy and numerical efficiency are often conflic ting requirements

=> A methodology is needed that is capable of simul ating at speeds exceeding 10 3 x Realtime sacrificing minimum accuracy.

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PROCEDURE

02

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Procedure (1/2)

=> Neural Network reduction of a detailed model, b ased on previous Gamma Technologies’ experience (with DOC and SCR – cf FISITA 2010 paper).

1/ Creation of the detailed model of a Lean NOx Trap .

2/ Model validation and calibration by LMM, based on Renault experimental data.

3/ Definition of operating ranges of the input para meters and creation of a design of experiments. Training of a set of static neural network to reproduce the kinetic behavior of the catalyst usin g the resulting data .

4/ Training of the NN models to predict the catalys t output conditions given the inlet conditions and the catalyst state (temper ature and NOx coverage)

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Procedure (2/2)

� Catalyst states could not be trained by the static Neural Network as they depend on past history. Dynamic Neural Network is r uled out as it cannot be easily made autonomous.

LNT Heat Flux

LNT Wall

Temperature

LNT

LNT NOx

CoverageNN Models

Physical Models

� State variables are deduced from two physical models that rely on outputs from the Neural Network models.

inletoutlet

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NEURAL NETWORK MODEL

HYBRID NEURAL NETWORK & ARCHITECTURE

03

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� The considered detailed model consists in a Lean NO x Trap catalyst operating under lean and rich conditions

Detailed Model Overview

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Detailed Model Overview

� The LNT detailed model behavior is characterized by the following 12 reactions :

1. NO + BaO + 1/2 O2 →→→→ BaONO2

2. NO2 + BaO →→→→ BaONO2

3. BaONO2 →→→→ NO2 +BaO

4. C10H22 + 15.5 BaONO2 →→→→ 15.5BaO + 7.75N2 + 11H2O + 10CO2

5. C3H6 + 4.5BaONO2 →→→→ 4.5BaO + 2.25N2 + 3H2O + 3CO2

6. C7H8 + 9BaONO2 →→→→ 9BaO + 4.5N2 + 4H2O + 7CO2

7. 10CO + 5BaONO2 →→→→ 5BaO + 2.5N2 + 10CO2

8. C10H22 + 15.5O2 →→→→ 10CO2 + 11H2O

9. C3H6 + 4.5O2 →→→→ 3CO2 + 3H2O

10.C7H8 + 9O2 →→→→ 7CO2 + 4H2O

11.CH4 + 2O2 →→→→ CO2 + 2H2O

12.CO + 0.5O2 →→→→ CO2

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Methodology - Neural Network Architecture

� The proposed neural network architecture is a three -layer feed-forward neural network with 2 hidden layers whose activatio n functions are tan-sigmoid and 1 output layer with linear activation

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LNT NOx Coverage

� The used approach consists in using a NN to predict derivative of coverage and then to integrate it along the simulation. Thus the coverage calculation during transient simulation is equal to:

� This procedure can be improved by using conservatio n principles.

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LNT wall temperature

� The LNT mass is treated as a single mass

� Model is considered to be adiabatic with respect to the heat loss to the environment

� Calculation based on the heat flux of energy enteri ng/leaving the catalyst wall predicted by a NN

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Methodology - DoE Setup

� Two DoE models were set up (each 5.000 experiments) : one for lean and one for rich conditions

� Results are used to train Neural Network: 8 input v ariables, 11 predicted quantities

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Neural Network model setup

� During the driving cycle transient simulation, the appropriate NN model is activated (switching from lean NN model to rich NN model and vice versa) based on the inlet condition.

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NEURAL NETWORK MODEL RESULTS

04

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Neural Network Model Results - Hydrocarbon (inst. and cumul.)

Inlet (measured)

Detailed model results

Neural Network results

CH

4M

ole

Fra

ctio

n

CH

4C

umul

ativ

e M

ole

Fra

ctio

n

C10

H22

Mol

e F

ract

ion

C10

H22

Cum

ulat

ive

Mol

e F

ract

ion

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Neural Network Model Results – NO2 and CON

O2

Mol

e F

ract

ion

NO

2C

umul

ativ

e M

ole

Fra

ctio

n

CO

Mol

e F

ract

ion

CO

Cum

ulat

ive

Mol

e F

ract

ion

Inlet (measured)



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Neural Network Model Results – Temperatures and NOx CoverageLN

T W

all T

empe

ratu

re

Gas

Tem

pera

ture

(LN

T

mid

dle

poin

t)

Gas

outle

ttem

pera

ture

NO

xC

over

age

Inlet temp. (measured)



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CONCLUSION

05

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Conclusion (1/2)

� Detailed After-treatment models can be reduced to v ery fast running Neural Network model with a more than acceptable lo ss of accuracy.

� Neural Network model was improved using a hybrid me thodology that improved the fidelity of the system model.

� For an overall duration of about 33h of real time. The computational time on a laptop with Intel 2.53 GHz CPU and 3 GB ram ( using a single core) were:

� Detailed model: 10’ 15”

� NN model: 01’ 25”

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Conclusion (2/2)

� It was noted that the Neural Network model runs 130 0 times faster than real time which is about 7 times faster than detail ed model.

� It should be pointed out that the performance of th e Neural Network model depends on the number of independent variable s (look ups) and the number of dynamic physical models. Thus, the nu merical efficiency of the NN model will be maintained even in cases wh ere the detailed model numerical efficiency degrades due to stiffer and/or more complex kinetics.

� Further improvements in computation speed can be expect ed, e.g. by using parallelized computing.

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DEVELOPMENT OF A FAST RUNNING LEAN … research, advanced studies & materials ... development of a fast running lean noxtrap model for simulation of real customer drive cycles d.maroteaux,