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Towards a Complete Engine Calibration Methodology: Dynamic Design of Experiments (DDoE), Application to Catalyst Warm-Up Phase
Technical Paper
2021-24-0028
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In recent years, engine calibration became a very hard task because of the increasing complexity of systems and the severity of the depollution norms regarding Real Driving Emissions (RDE). In particular, optimal engine control during dynamic phases became crucial for reducing pollutant emissions. Beyond the classical engine calibration method based on steady state experiments, methods that integrate the dynamical response of the engine constitute therefore a promising approach.
This work proposes a global approach of engine dynamical model-based calibration (DMBC) and optimization based on a dynamic Design of Experiments (DDoE). After a general description of the architecture of the calibration process, the paper focuses on the methodology for the design of DDoE. The proposed DDoE is based on an extraction of highly dynamical legal norm cycles (from the analysis of existing RDE cycles), together with variations of calibration parameters which are intern variables of the control strategy of the engine control unit (ECU). Variations of calibration parameters allow variations of control parameters which are outputs of the control strategy and relevant regarding emissions. The criterion chosen for the definition of the DDoE is the minimization of the discrepancy of control parameters of the engine.
The catalyst warm-up phase has been chosen as an application case of the methodology developed in this paper. The main objective of this phase is to heat the catalyst as fast as possible so that it becomes efficient regarding reduction of pollutant emissions. The first results describe a part of the DDoE that will be next completed and performed in a test bench, in order to model engine emissions.
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Citation
HAMBAREK, D., PETIOT, J., Chesse, P., and WATEL, E., "Towards a Complete Engine Calibration Methodology: Dynamic Design of Experiments (DDoE), Application to Catalyst Warm-Up Phase," SAE Technical Paper 2021-24-0028, 2021, https://doi.org/10.4271/2021-24-0028.Data Sets - Support Documents
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References
- europa.eu 2016
- Cheimariotis , I. , Bordet , N. , Heitz , T. , Volut , M. et al. Ensuring Vehicle Full RDE Coverage Using an Advanced Cloud-Computing Simulation Solution Paris SIA Simulation numérique 2019
- Steinbrenner , U. Motronic Engine Management Stuttgart, West Germany Robert Bosch GmbH, Automotive Equipment Business Sector, Department for Technical Information 1994
- González , E.G. , Flórez , J.A. , and Arab , S. Development of the Management Strategies of the ECU for an Internal Combustion Engine: Computer Simulation Mechanical Systems and Signal Processing 22 6 2008 1356 1373
- Gokhale , S. and Kulkarni , S. Modeling and Optimization of Automotive Engine Calibration Process - A Review IPASJ International Journal of Electronics & Communication 3 1 2015
- Kruse , T. , Huber , T. , Kleinegraeber , H. , and Delflorio , N. Optimizing Gaseous and Particle Emissions of a GDI Engine by Coupling a Dynamic Data Based Engine Model with ECU Injection Berlin expert verlag GmbH 2019
- Castagné , M. , Bentolila , Y. , Chaudoye , F. , Hallé , A. , Nicolas , F. and Sinoquet , D. Comparison of Engine Calibration Methods Based on Design of Experiments (DoE) Oil & Gas Science and Technology - Rev IFP 63 4 563 582 2008
- Deflorian , M. , Klöpper , F. , and Rückert , J. Online Dynamic Black Box Modelling and Adaptive Experiment Design in Combustion Engine Calibration IFAC Symposium on Advances in Automotive Control Munich 2010
- Scheidel , S. , Gande , M. , Zerbini , G. , and Decker , M. A Versatile Approach for Transient Manoeuvre Optimization Using DoE Methods International Conference on Calibration Methods and Automotive Data Analytics Berlin 2019
- Ezzeddinne , M. , Castro , E. , and Lengellé , R. Dynamic Design of Experiments for Engine Pollutants Emissions Modeling and Optimization Rosemont SAE International 2008
- Taindjis , D. , Dober , G. , Baumann , W. , and Guerrassi , N. Engine Transient Calibration for Real Driving Conditions : A Holistic Statistical Approach International Conference and Exhibition SIA Powertrain 449 457 Rouen 2018
- Deflorian , M. and Zaglauer , S. Design of Experiments for Nonlinear Dynamic System Identification IFAC Proceedings of the 18th World Congress Milan 2011
- Burke , R. , Baumann , W. , Akehurst , S. and Brace , C. Dynamic Modelling of Diesel Engine Emissions Using Parametric Volterra Series Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering 228 164 179 Bath 2013
- Berger , B. , and Rauscher , F. Robust Gaussian Process Modelling For Engine Calibration 7th Vienna International Conference on Mathematical Modeling. Volume 45, Issue2 159 164 Vienne 2012
- Rasmussen , C. and Williams , C. Gaussian Process for Machine Learning Massachusetts the MIT Press 2006
- Schulz , E. , Speekenbrink , M. , and Krause , A. A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions Journal of Mathematical Psychology 85 2018 1 16
- Sjöberg , J. , Zhang , Q. , Ljung , L. , Benveniste , A. et al. Nonlinear Black-Box Modeling in System Identification : a Unified Overview Automatica 31 12 1995 1691 1724
- Hao , D. , Zhao , C. , Li , Y.H.G. , Zeng , W. et al. Dynamic Indicated Torque Estimation for Turbocharged Diesel Engines Based on Back Propagation Neural Network IFAC PapersOnLine 2018
- Lyu , S. , Liu , P. , Liu , L. , Ma , S. et al. An Improved Dynamic Process Neural Network Prediction Model Identification Method Microprocessors and Microsystems 2020
- Qiao , D. , Li , P. , Ma , G. , Qi , X. et al. Realtime Prediction of Dynamic Mooring Lines Responses with LSTM Neural Network Model Ocean Engineering 2021
- Rahmoune , M.B. , Hafaifaa , A. , Kouzoua , A. , Chenc , X. et al. Gas Turbine Monitoring Using Neural Network Dynamic Nonlinear Autoregressive with External Exogenous Input Modelling Mathematics and Computers in Simulation 2021
- Shin , S. , Lee , Y. , Kim , M. , Park , J. et al. Deep Neural Network Model with Bayesian Hyperparameter Optimization for Prediction of NOx at Transient Conditions in a Diesel Engine Engineering Applications of Artificial Intelligence 2020
- Turkson , R.F. , Yan , F. , Ali , M.K.A. , and Hu , J. Artificial Neural Network Applications in the Calibration of Spark-Ignition Engines: An Overview Engineering Science and Technology, an International Journal 2016
- Navid , A. , Khalilarya , S. , and Abbasi , M. Diesel Engine Optimization with Multi-Objective Performance Characteristics by Non-evolutionary Nelder-Mead Algorithm: Sobol Sequence and Latin Hypercube Sampling Methods Comparison in DoE Process Fuel 228 2018 349 367
- Klampfl , E. , Lee , J. , Dronzkowski , D. , and Theisen , K. Engine Calibration Process Optimization Science and Technology Publications 2012 335 341
- Luc Pronzato , W.M. Design of Computer Experiments: Space Filling and Beyond Statistics and Computing Springer Verlag (Germay). 22 3 2012 681 701
- Mua , W. and Xiong , S. A Class of Space-Filling Designs and Their Projection Properties Statistics and Probability Letters 2018
- Faure , H. Discrépances de suites associées à un système de numération en dimension s Acta Arithmetica 41 4 337 351 1982
- Niederreiter , H. Low-Discrepancy and Low-Dispersion Sequences Journal of Number Theory 30 1988 51 70
- Halton , J. On the Efficiency of Certain Quasi-Random Sequences of Points in Evaluating Multi-Dimensional Integrals Numerische Mathematik 2 1 1960 84 90
- Sobol , I. On the Distribution of Points in a Cube and the Approximate Evaluation of Integrals USSR Computational Mathematics and Mathematical Physics. 7 4 1967 86 112
- Niederreiter , H. Discrepancy and Convex Programming 1972
- Warnock , T.
- Abbasimehr , H. , and Paki , R. 2020
- Millo , F. , Arya , P. , and Mallamo , F. Optimization of Automotive Diesel Engine Calibration Using Genetic Algorithm Techniques Energy 2018
- Shirneshan , A. , Bagherzadeh , S.A. , Najafi , G. , Mamat , R. et al. Optimization and Investigation the Effects of Using Biodiesel-Ethanol Blends on the Performance and Emission Characteristics of a Diesel Engine by Genetic Algorithm Fuel 2020
- Wang , J. , Shen , L. , Bi , Y. , and Lei , J. Modeling and Optimization of a Light-Duty Diesel Engine at High Altitude with a Support Vector Machine and a Genetic Algorithm Fuel 2020