This paper proposes to use Gaussian process regression to predict the consumption of a plug-in electric hybrid vehicle from low-quality data. We specify background knowledge regarding new operating points and information regarding the noise process. This makes it possible to adapt the original (naive’) model. Experiments realized using dynamic and energetic models simulated electrified vehicle show the interest of our approach in order to improve robustness against scarce and noisy data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle consumption estimation via calibrated Gaussian Process regression


    Beteiligte:
    Randon, Mathieu (Autor:in) / Quost, Benjamin (Autor:in) / Boudaoud, Nassim (Autor:in) / Wissel, Dirk von (Autor:in)


    Erscheinungsdatum :

    2022-06-05


    Format / Umfang :

    2203536 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiresolution partitioned Gaussian process regression for terrain estimation

    Zhang, Clark / Ono, Masahiro / Lanka, Ravi | IEEE | 2018



    VEHICLE TRAJECTORY PREDICTION WITH GAUSSIAN PROCESS REGRESSION IN CONNECTED VEHICLE ENVIRONMENT

    Goli, Sepideh Afkhami / Far, Behrouz H. / Fapojuwo, Abraham O. | British Library Conference Proceedings | 2018


    Aircraft centre-of-gravity estimation using Gaussian process regression models

    Yang, Xiaoke / Luo, Mingqiang / Zhang, Jing et al. | IEEE | 2016


    Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\star$

    Goli, Sepideh Afkhami / Far, Behrouz H. / Fapojuwo, Abraham O. | IEEE | 2018