Highlights Empirically compare fuel consumption of diesel and hybrid bus based on a unique bus monitoring dataset. Develop microscopic (1hz) and mesoscopic (5–60 min) fuel consumption models for hybrid bus. The mean absolute percentage errors of the micro and mesoscopic models are ranging 1–3% and 5–9% respectively. Investigate driving speed, engine demand and grade on fuel consumption prediction performance.

    Abstract Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.


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    Titel :

    Hybrid electric buses fuel consumption prediction based on real-world driving data


    Beteiligte:
    Sun, Ruixiao (Autor:in) / Chen, Yuche (Autor:in) / Dubey, Abhishek (Autor:in) / Pugliese, Philip (Autor:in)


    Erscheinungsdatum :

    2020-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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