Highlights ► The accuracy of the Markov method in drive cycle analysis depend on the number of repetitions. ► The best driving cycle is 500s, using 135 velocity modes. ► General corpus behaviour requires less than 1000 Markov repetitions and accurate corpus behaviour up to 1,000,000.

    Abstract This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a driving cycle without deconstructing the raw velocity–time sequence. The accuracy of the driving cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, driving cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180km from a trial of electric scooters. The accuracies of the candidate driving cycles depended most strongly on the number of Markov repetitions. The best driving cycle used 135 velocity modes, was 500s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best driving cycle reproduced the corpus behaviour better when road grade was included.


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    Titel :

    A robust, data-driven methodology for real-world driving cycle development


    Beteiligte:


    Erscheinungsdatum :

    2012-01-01


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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