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Emission-robust operation of diesel HEV considering transient emissions

  • M. Schudeleit
  • F. Küçükay
Article

Abstract

In this paper, we consider a method to create an engine emission simulation model for cycle and customer driving of a vehicle. The emission model results from an empiric approach, also taking into account the effects of engine dynamics on emissions. We analysed transient engine emissions in driving cycles and during representative customer driving profiles and created emission meta models. The analysis showed a significantly higher correlation in emissions when simulating realistic customer driving profiles using the created verified meta models (< 1 % model error) compared to static approaches, which are commonly used for vehicle simulation. Therefore, a transient modelling approach is conducted, which shows a great increase in accuracy in customer driving operation.

Key words

Engine modelling Diesel engine Exhaust emissions Transient emission modelling Neural network Customer driving Vehicle simulation Energy management Hybrid electric vehicle Particulate matter Nitrogen oxide NOx 

Nomenclature

a

acceleration, m/s2

c

coefficient

D

data set

DCT

double clutch transmission

EM

electric motor

Fa

aerodynamic drag, Nm

Fg

gravitational forces, Nm

Fi

inertial forces, Nm

Fr

rolling frictional force, Nm

FTP75

Federal Test Procedure 75

Grad

gradient

HEV

hybrid electric vehicle

i

ratio

ICE

internal combustion engine

KFCV

k-fold cross validation

LPS

load point shift

MAE

maximum adverse excursion

mr

rotational mass, m

mv

vehicle mass, m

n

speed, rad/s

NEDC

New European Driving Cycle

NN

neural network

OSP

orientation speed profile

P

power, W

PT1

first order systems

Q0

battery initial capacity, Ah

r

radius, m

Ri

internal resistance, Ω

RMSE

root mean square error

SOC

state of charge, %

T

torque, Nm

T1

time constant 1, 1/s

v

vehicle speed, m/s

Vsoc

ideal open-circuit voltage source, V

WLTP

Worldwide harmonized Light vehicles Test Procedures

α

slope angle, o

η

efficiency, %

Subscripts

a

axle

b

braking

c

cardan shaft

d

differential

dyn

dynamic

em

electric motor

f

front

g

gearbox

p

proportion

r

rear

T

torque

w

wheel

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References

  1. Asprion, J., Chinellato, O. and Guzzella, L. (2013). A fast and accurate physics-based model for the NOx emissions of diesel engines. Applied Energy, 103, 221–233.CrossRefGoogle Scholar
  2. Hastie, T., Tibshirani, R. and Friedman, J. (2011). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer Verlag. New York.MATHGoogle Scholar
  3. Küçükay, F. (1990). Computer aided dimensioning of transmissions with representative load spectr. ATZAutomobiltechnische Zeitschrift 92, 6, 328–333.Google Scholar
  4. Müller-Kose, J. P. (2002). Representative Load Spectra for Vehicle Gearboxes. Shaker Verlag. Aachen. Germany.Google Scholar
  5. Nuesch, T., Wang, M., Isenegger, P., Onder, C., Steiner, R., Macri-Lassus, P. and Guzzella, L. (2014). Optimal energy management for a diesel hybrid electric vehicle considering transient PM and quasi-static NOx emissions. Control Engineering Practice, 29, 266–276.CrossRefGoogle Scholar
  6. Schüler, M., Hafner, M. and Isermann, R. (2000). Use of fast neural networks for model-based optimization of combustion engines. MTZ-Motortechnische Zeitschrift, 61, 704–711.CrossRefGoogle Scholar
  7. Siebertz, K., van Bebber, D. and Hochkirchen, T. (2010). Design of Experiments (DoE). Springer Verlag. Heidelberg. Germany.Google Scholar
  8. Simpson, T., Lin, D. and Chen, W. (2011). Sampling strategies for computer experiments: Design and analysis. Int. J. Reliability and Applications, 209–240.Google Scholar
  9. Wenzel, P. (2006). Modeling of soot and NOx emissions of a diesel engine. Otto-von-Guericke-Universität Magdeburg, 15–20.Google Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Automotive EngineeringBraunschweig University of TechnologyBraunschweigGermany

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