Model-based System Engineering for Powertrain Systems Optimization Sandra Hamze To cite this version: Sandra Hamze. Model-based System Engineering for Powertrain Systems Optimization. Automatic Control Engineering. Université Grenoble Alpes, 2019. English. NNT : 2019GREAT055. tel- 02524432 HAL Id: tel-02524432 https://tel.archives-ouvertes.fr/tel-02524432 Submitted on 30 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE ALPES Spécialité : Automatique-Productique Arrêté ministériel : 7 août 2006 Présentée par Sandra HAMZE Thèse dirigée par Emmanuel WITRANT et codirigée par Delphine BRESCH-PIETRI et Vincent TALON préparée au sein du laboratoire Grenoble Images Parole Signal Automatique (GIPSA-lab) dans l’école doctorale Electronique, Electrotechnique, Automatique, Traitement du Signal (EEATS) en collaboration avec Renault s.a.s Optimisation Multi-objectifs Inter-systèmes des Groupes Motopropulseurs Model-based System Engineering for Powertrain Systems Optimization 2 Thèse soutenue publiquement le 5 Novembre 2019, devant le jury composé de: Michel DAMBRINE UVHC-Ensiame, Examinateur, Président du jury Sorin OLARU CentraleSupélec, Rapporteur Yann CHAMAILLARD Université d’Orléans, Rapporteur Emmanuel WITRANT GIPSA-lab, Directeur de thèse Delphine BRESCH-PIETRI CAS-MINES ParisTech, Encadrante de thèse Vincent TALON Renault s.a.s, Encadrant de thèse UNIVERSITÉ DE GRENOBLE ALPES ÉCOLE DOCTORALE EEATS Electronique, Electrotechnique, Automatique, Traitement du Signal THÈSE pour obtenir le titre de docteur en Automatique de l’Université de Grenoble Alpes Mention : Automatique-Productique Présentée et soutenue par Sandra HAMZE Optimisation Multi-objectifs Inter-systèmes des Groupes Motopropulseurs Model-based System Engineering for Powertrain Systems Optimization Thèse dirigée par Emmanuel Witrant préparée au laboratoire Grenoble Images Parole Signal Automatique (GIPSA-lab) soutenue le 5 Novembre 2019 Jury : Président et examinateur : Michel DAMBRINE - UVHC-Ensiame Rapporteur : Sorin OLARU - CentraleSupélec Rapporteur : Yann CHAMAILLARD - Université d’Orléans Directeur : Emmanuel WITRANT - GIPSA-lab Encadrants : Delphine BRESCH-PIETRI - CAS-MINES ParisTech Vincent TALON - Renault s.a.s Acknowledgement This PhD thesis is the fruit of three years of hard work and dedication. It wouldn’t have been feasible without the support of many to whom I extend my sincere gratitude. First of all, I extend my utmost gratitude to my director Mr. Em- manuel WITRANT and my co-director Miss Delphine BRESCH-PIETRI from GIPSA-lab for their worthful efforts in orienting the work done throughout the PhD, in providing insight and expertise that greatly as- sisted the workflow, and in proofreading this manuscipt. On the other hand, I am immensely grateful to Mr. Vincent TALON from Renault s.a.s for granting me the opportunity of doing this PhD, and for taking my hand throughout the industrial part of the realized work. It is their knowledge and experience that helped put my work on the right track. Besides, I take this opportunity to thank Mr. Sorin OLARU, Mr. Yann CHAMAILLARD, and Mr. Michel DAMBRINE for their participation in the jury, and for their efforts in carefully examining this manuscript and reporting the necessary amendments. I would also like to send my sincere thanks to my colleagues in Renault with whom I shared a friendly atmosphere and an excellent working environment during the first year of my PhD and to my friends in GIPSA- lab who provided an enjoyable multicultural ambience which I enjoyed during the second and third years of the PhD. Last but not least, I share the credit of my work with my parents, my sister Carol, and my fiancé Hadi. Their encouragement and morale helped me overcome the work stress and pushed me forward all the way through these three years of hard work. It is only taking me a moment to thank you all, but your thoughtfulness I will remember a long time! i Contents List of Figures vii List of Tables xi Nomenclature xiii 1 Contexte et Objectif de la Thèse1 1.1 Ingénierie des systèmes à base de modèles..........1 1.2 Le groupe motopropulseur....................2 1.3 Ingénierie des systèmes à base de modèles pour l’optimisation des systèmes du groupe motopropulseur...3 1.3.1 Conception d’un système de commande basé sur un modèle...........................3 1.3.2 Optimisation des systèmes du groupe motopropulseur5 1.4 Objectif de la thèse........................6 1.5 Structure de la thèse.......................7 2 Context and Objective of the Thesis 11 2.1 Model-based systems engineering............... 11 2.2 The powertrain.......................... 12 2.3 Model-based systems engineering for powertrain systems optimization............................ 13 2.3.1 Model-based control system design........... 13 2.3.2 Powertrain systems optimization............ 14 2.4 Objective of the thesis...................... 15 iii iv Contents 2.5 Thesis structure.......................... 17 3 Black-box Gasoline Engine Identification using Subspace Methods 21 3.1 H5FT400 nonlinear model.................... 27 3.2 Linear state-space model inputs and outputs......... 29 3.3 Experiment design........................ 30 3.3.1 Disturbance signals.................... 30 3.3.2 Disturbance signals correlation study.......... 32 3.3.3 Disturbance signals spectral analysis.......... 33 3.3.4 Disturbed inputs and outputs.............. 34 3.4 State-space linear model identification............. 35 3.4.1 Subspace methods..................... 36 3.4.2 Identification algorithm.................. 37 3.4.3 Identification results................... 40 3.5 State-space linear model validation............... 54 3.5.1 Validation criteria..................... 54 3.5.2 Validation results..................... 58 3.6 Conclusion and future perspectives............... 65 4 Electric Throttle Linear Model Identification and Control 67 4.1 Modeling an electric throttle with an LPV Model....... 73 4.2 Identification of an electric throttle model and simulation results............................... 80 4.2.1 Nonlinear AMESim® model A and its nonlinearities.. 80 4.2.2 Linear-parameter-varying state-space model A.... 82 4.2.3 Identification of linear ARX model A........... 85 Contents v 4.3 Electric throttle test-bench and experimental results..... 90 4.3.1 Nonlinear AMESim® model B and its nonlinearities.. 91 4.3.2 Identification of linear ARX model B........... 101 4.4 Control of the throttle LPV model................ 104 4.4.1 Linear quadratic regulator with reference and distur- bance feedforwards.................... 105 4.4.2 Control of linear model A................. 106 4.4.3 Control of AMESim® model A.............. 108 4.5 Methodology of identification and control........... 108 4.6 Conclusion and future perspectives............... 110 5 Identification and Control of Transport Models in the En- gine Air-path 113 5.1 Identification of advective flows time-delay model in a heat exchanger............................. 125 5.1.1 PDE model and problem formulation.......... 125 5.1.2 From PDE to time-delay equations........... 127 5.1.3 Estimation of the parameters of time-delay equations. 129 5.1.4 Experimental setup.................... 131 5.1.5 Experimental results................... 132 5.2 Optimal control of mass-transport time-delay model in an EGR................................. 142 5.2.1 EGR linear state-space model.............. 142 5.2.2 Control problem formulation............... 144 5.2.3 Indirect optimal control methods............ 145 5.2.4 Augmented Lagrangian method............. 149 5.2.5 Indirect method: discretize-then-optimize approach. 154 vi Contents 5.2.6 Indirect method: optimize-then-discretize approach. 158 5.2.7 Simulation results..................... 164 5.3 Conclusion and future perspectives............... 168 Conclusions and Future Perspectives 171 Bibliography 188 List of Figures 1.1 Cycle de développement des systèmes de contrôle......4 2.1 Control system development lifecycle............. 14 3.1 H5Ft400 engine scheme..................... 24 3.2 Nonlinear model of the H5Ft400 engine built on AMESim® 27 3.3 ISO field covering the engine operating range........ 28 3.4 Disturbance signals added to PWMwg and PWMth ...... 33 3.5 Spectrum of the disturbance signals.............. 34 3.6 Inputs and outputs at operating point (2000 rpm,11:35 bar).. 35 3.7 Discrete black-box SS model................... 35 3.8 Identification results at operating point (4200 rpm,−1 bar).. 44 3.9 Identification results at operating point (4200 rpm,11:76 bar). 45 3.10Identification results at operating point (2000 rpm,14:87 bar). 46 3.11DC motor scheme......................... 49 3.12Rotational speed of the DC motor................ 51 3.13Eigen values of identified systems at 4200 rpm ........ 53 3.14DC gains of identified systems at 4200 rpm ........... 54 3.15Stability analysis of identified systems at 4200 rpm ...... 55 3.16Criteria used for validation of identification results..... 56 3.17Identification results at 4200 rpm subject