Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles. Receding time window is used to update the latest driving patterns in real time. The recognition performance is investigated with naturalistic driving cycles measured in Midwestern US. Velocity-acceleration probability distributions are analyzed to assess the proposed recognition algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-World Driving Pattern Recognition for Adaptive HEV Supervisory Control: Based on Representative Driving Cycles in Midwestern US


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Lee, Tae-Kyung (Autor:in) / Filipi, Zoran (Autor:in)

    Kongress:

    SAE 2012 World Congress & Exhibition ; 2012



    Erscheinungsdatum :

    16.04.2012




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Real-World Driving Pattern Recognition for Adaptive HEV Supervisory Control: Based on Representative Driving Cycles in Midwestern US

    Lee, T.-K. / Filipi, Z. / Society of Automotive Engineers | British Library Conference Proceedings | 2012


    Unsupervised pattern discovery in automotive time series : pattern-based construction of representative driving cycles

    Noering, Fabian Kai Dietrich / Technische Universität Braunschweig / Springer Fachmedien Wiesbaden | TIBKAT | 2022


    Pattern-based Representative Cycles

    Noering, Fabian Kai Dietrich | Springer Verlag | 2022


    Stochastic Synthesis of Representative and Multidimensional Driving Cycles

    Esser, Arved / Zeller, Martin / Rinderknecht, Stephan et al. | SAE Technical Papers | 2018


    Stochastic Synthesis of Representative and Multidimensional Driving Cycles

    Esser, Arved / Zeller, Martin / Foulard, Stéphane et al. | British Library Conference Proceedings | 2018