Ressource en auto-formation : Synthesis and validation of representative real world driving cycles for Plug-In HEVs

Driving cycles play an important role in analysis and design of hybrid propulsion systems. Their role is magnified in case of Plug-In Hybrids, since driving patterns have a strong impact on specific energy consumption, ability of the vehicle to provide pure electric operation, range of charge deplet...
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Présentation de: Synthesis and validation of representative real world driving cycles for Plug-In HEVs

Informations pratiques sur cette ressource

Langue du document : Anglais
Type : cours / présentation
Niveau : enseignement supérieur, master
Durée d'exécution : 16 minutes 5 secondes
Contenu : vidéo
Public(s) cible(s) : apprenant, enseignant
Document : Document HTML
Droits d'auteur : pas libre de droits, gratuit
Mentions légales : http://lille1tv.univ-lille1.fr/page.aspx?id=mentions

Description de la ressource en auto-formation

Résumé

Driving cycles play an important role in analysis and design of hybrid propulsion systems. Their role is magnified in case of Plug-In Hybrids, since driving patterns have a strong impact on specific energy consumption, ability of the vehicle to provide pure electric operation, range of charge depleting operation, and finally assessments of the state-of-charge (SOC) at the beginning of charging. Certification schedules are commonly used, but in case of PHEV standard cycles are repeated many times to study the charge depleting operation. While this is useful for comparisons with results in literature, it is not representative of real-world driving. Therefore, characterizing the naturalistic driving patterns and generating representative real-world schedules is essential for PHEV design and control work, studies of technology adoption by real consumers, and assessments of the impact on the grid. This work presents analysis of naturalistic driving data collected in a Midwest region of US, followed by a procedure for synthesizing real-world driving cycles based on that data. The characteristic of the real-world cycles are captured with transition probability matrices (TPMs), and a regression analysis is applied to select significant explanatory variables for determining the most representative synthetic cycles. Validation indicates the ability to generate representative real-world driving cycles for any arbitrary driving distance by combining a stochastic process and statistical methodology.

"Domaine(s)" et indice(s) Dewey

  • Transports terrestres. Transports routiers (388)
  • Environnement (363.7)
  • Ressources naturelles et énergie (333.7)

Domaine(s)

Informations techniques sur cette ressource en auto-formation

    Intervenants, édition et diffusion

    Intervenants

    Expert(s) du sujet : Zoran Filipi
    Créateur(s) de la métadonnée : Virginie Lacaze
    Validateur(s) de la métadonnée : Virginie Lacaze

    Édition

    • Lille1.tv - Université Lille 1

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    • Identifiant de la fiche
      oai:uved:UnivLille1-Driving-Cycles
    • Identifiant OAI-PMH
      oai:uved:UnivLille1-Driving-Cycles
    • Version
      V1
    • Statut de la fiche
      final
    • Schéma de la métadonnée
    • Entrepôt d'origine
      UVED
    • Publication
      03-09-2010

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