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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- 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, %
- 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
References
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.
Hastie, T., Tibshirani, R. and Friedman, J. (2011). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer Verlag. New York.
Küçükay, F. (1990). Computer aided dimensioning of transmissions with representative load spectr. ATZAutomobiltechnische Zeitschrift 92, 6, 328–333.
Müller-Kose, J. P. (2002). Representative Load Spectra for Vehicle Gearboxes. Shaker Verlag. Aachen. Germany.
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.
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.
Siebertz, K., van Bebber, D. and Hochkirchen, T. (2010). Design of Experiments (DoE). Springer Verlag. Heidelberg. Germany.
Simpson, T., Lin, D. and Chen, W. (2011). Sampling strategies for computer experiments: Design and analysis. Int. J. Reliability and Applications, 209–240.
Wenzel, P. (2006). Modeling of soot and NOx emissions of a diesel engine. Otto-von-Guericke-Universität Magdeburg, 15–20.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Schudeleit, M., Küçükay, F. Emission-robust operation of diesel HEV considering transient emissions. Int.J Automot. Technol. 17, 523–533 (2016). https://doi.org/10.1007/s12239-016-0053-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-016-0053-6