Automated Model Generation for Hybrid Vehicles Optimization and Control

  • Verdonck N
  • Chasse A
  • Pognant-Gros P
  • et al.
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Abstract

Création automatique de modèles de composants pour l'optimisation et le contrôle de véhicules hybrides-L'optimisation de l'utilisation des groupes moto-propulseurs (GMP) modernes nécessite de modéliser le système de manière quasi-statique avec une logique inverse ("Backward Qua-sistatic Model''-BQM), en particulier dans le cas des GMP hybrides. Cependant, les modèles utilisés pour la simulation réaliste de ces GMP sont souvent dynamiques à logique directe ("Forward Dynamic Model''-FDM). Cet article présente une méthodologie pour obtenir les BQM des composants de GMP actuels directement issus de la limite quasi-statique des FDM correspondants de manière analytique. Grâce à l'aspect paramétrique de cette procédure, il n'est pas nécessaire de relancer une campagne de simulations après chaque changement du système modélisé : il suffit de modifier les paramètres correspondants dans le BQM. Cette approche est illustrée par trois cas d'étude (moteur turbo, moteur électrique et batterie), et l'effet d'un changement de paramètre sur le contrôle de supervision d'un véhicule hybride est étudié en simulation hors-ligne, en co-simulation et sur un banc d'essai HiL adapté aux architectures hybrides (HyHiL). Abstract-Automated Model Generation for Hybrid Vehicles Optimization and Control-Systematic optimization of modern powertrains, and hybrids in particular, requires the representation of the system by means of Backward Quasistatic Models (BQM). In contrast, the models used in realistic powertrain simulators are often of the Forward Dynamic Model (FDM) type. The paper presents a methodology to derive BQM's of modern powertrain components, as parametric, steady-state limits of their FDM counterparts. The parametric nature of this procedure implies that changing the system modeled does not imply relaunching a simulation campaign, but only adjusting the corresponding parameters in the BQM. The approach is illustrated with examples concerning turbocharged engines, electric motors, and electrochemical batteries, and the influence of a change in parameters on the supervisory control of an hybrid vehicle is then studied offline, in co-simulation and on an HiL test bench adapted to hybrid vehicles (HyHiL). A {in;exm;exh} External surface: intake manifold, exhaust manifold, exhaust pipe C {e;tc;m} Torque: engine, turbocharger, motor C q,{tv;he,eq;wg;exh,eq} Discharge coefficient: throttle, exchanger (equivalent), waste gate, exhaust pipe (eq.) c p,{a;exh} Constant pressure specific heat: air, exhaust C {Ni;MH} Concentration: nickel, metal hydride D Mass flow rate through the engine D {in j;exh;t;t,corr;wg} Mass flow rate: injector, exhaust, turbine, turbine (corrected), waste gate h {exm;exh} Conductive heat exchange coefficient: exhaust manifold, exhaust pipe H {in;exm;exh} Convective heat transfer coefficient: intake manifold, exhaust manifold, exhaust pipe I {d;dt;q;qt} Current: direct, direct (transferred), quadrature, quadrature (transferred) I {m;m,max;b} Current: motor, motor (maximum), battery k {Ni;MH} Electrode parameter: nickel, metal hydride L s Stator inductance m air Inducted air mass N {e;tc;tc,corr} Rotational speed: engine, turbocharger, turbocharger (corrected) n cell No. of battery cells p Number of pole pairs p {in;c;e} Pressure: intake manifold, compressor exit, exchanger exit p {exm;t;0} Pressure: exhaust manifold, turbine exit, ambient P {m;m,max;b} Electric power: motor, motor (maximum), battery Q { f ;exm} Fuel lower heating value, heat flow at the engine exhaust R

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CITATION STYLE

APA

Verdonck, N., Chasse, A., Pognant-Gros, P., & Sciarretta, A. (2010). Automated Model Generation for Hybrid Vehicles Optimization and Control. Oil & Gas Science and Technology – Revue de l’Institut Français Du Pétrole, 65(1), 115–132. https://doi.org/10.2516/ogst/2009064

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