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A two-layer predictive control for hybrid electric vehicles energy management

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  • معلومة اضافية
    • Contributors:
      Technocentre Renault Guyancourt; RENAULT; Laboratoire des signaux et systèmes (L2S); Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS); Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME); Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA); The International Federation of Automatic Control
    • بيانات النشر:
      HAL CCSD
    • الموضوع:
      2017
    • Collection:
      Université d'Orléans: HAL
    • الموضوع:
    • نبذة مختصرة :
      International audience ; In this paper, a two-layer predictive energy management strategy for hybrid electric vehicles without an external recharge is introduced. The low-level layer exploits telemetry data over a short-term horizon in a model predictive control structure that provides the engine torque, but also the stop-start decision. The upper layer uses a tuning mechanism with a longer horizon to calculate the MPC weighting factor that ensures a balance between the fuel and battery consumption. An analysis of this upper-level tuning prediction horizon dependence on the drive cycle characteristics is performed. The robustness with respect to state-of-charge and engine torque estimation is also proven by a sensitivity analysis.
    • Relation:
      hal-01566029; https://univ-orleans.hal.science/hal-01566029; https://univ-orleans.hal.science/hal-01566029/document; https://univ-orleans.hal.science/hal-01566029/file/IFACWC2017_Stroe_el_al_MPC_HEV.pdf
    • الدخول الالكتروني :
      https://univ-orleans.hal.science/hal-01566029
      https://univ-orleans.hal.science/hal-01566029/document
      https://univ-orleans.hal.science/hal-01566029/file/IFACWC2017_Stroe_el_al_MPC_HEV.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.78E97B36