Effects of using synthesized Driving Cycles on vehicle fuel consumption

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Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly, improvements on the DC synthesis method are proposed, to reach a more realistic slope profile and more accurate fuel consumption and CO2 emission estimates. The effects of using synthesized DCs on fuel consumption are investigated considering three different vehicle models: conventional ICE, and full hybrid and mild hybrid electric vehicles. Results show that short but representative synthetic DCs will results in more realistic fuel consumption estimates (e.g. in the 5%-10% range) and in much faster simulations. Using the results of this proposed method also eliminates the need to use very simplified DCs, as the New European Driving Cycle(NEDC), or long, measured DCs.

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Fuel consumption
Hybrid vehicles
Markov processes

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    @article{67b84e5538724aa2add4c3b32c8501a8,
    title = "Effects of using synthesized Driving Cycles on vehicle fuel consumption",
    abstract = "Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly, improvements on the DC synthesis method are proposed, to reach a more realistic slope profile and more accurate fuel consumption and CO2 emission estimates. The effects of using synthesized DCs on fuel consumption are investigated considering three different vehicle models: conventional ICE, and full hybrid and mild hybrid electric vehicles. Results show that short but representative synthetic DCs will results in more realistic fuel consumption estimates (e.g. in the 5{\%}-10{\%} range) and in much faster simulations. Using the results of this proposed method also eliminates the need to use very simplified DCs, as the New European Driving Cycle(NEDC), or long, measured DCs.",
    keywords = "Driving cycle, Efficiency, Markov Chain, Monte Carlo, Powertrain Design",
    author = "K. Hereijgers and E. Silvas and T. Hofman and M. Steinbuch",
    year = "2017",
    month = "7",
    day = "1",
    doi = "10.1016/j.ifacol.2017.08.1183",
    language = "English",
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    Effects of using synthesized Driving Cycles on vehicle fuel consumption. / Hereijgers, K.; Silvas, E.; Hofman, T.; Steinbuch, M.

    In: IFAC-PapersOnLine, Vol. 50, Nr. 1, 01.07.2017, blz. 7505-7510.

    Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelAcademicpeer review

    TY - JOUR

    T1 - Effects of using synthesized Driving Cycles on vehicle fuel consumption

    AU - Hereijgers,K.

    AU - Silvas,E.

    AU - Hofman,T.

    AU - Steinbuch,M.

    PY - 2017/7/1

    Y1 - 2017/7/1

    N2 - Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly, improvements on the DC synthesis method are proposed, to reach a more realistic slope profile and more accurate fuel consumption and CO2 emission estimates. The effects of using synthesized DCs on fuel consumption are investigated considering three different vehicle models: conventional ICE, and full hybrid and mild hybrid electric vehicles. Results show that short but representative synthetic DCs will results in more realistic fuel consumption estimates (e.g. in the 5%-10% range) and in much faster simulations. Using the results of this proposed method also eliminates the need to use very simplified DCs, as the New European Driving Cycle(NEDC), or long, measured DCs.

    AB - Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly, improvements on the DC synthesis method are proposed, to reach a more realistic slope profile and more accurate fuel consumption and CO2 emission estimates. The effects of using synthesized DCs on fuel consumption are investigated considering three different vehicle models: conventional ICE, and full hybrid and mild hybrid electric vehicles. Results show that short but representative synthetic DCs will results in more realistic fuel consumption estimates (e.g. in the 5%-10% range) and in much faster simulations. Using the results of this proposed method also eliminates the need to use very simplified DCs, as the New European Driving Cycle(NEDC), or long, measured DCs.

    KW - Driving cycle

    KW - Efficiency

    KW - Markov Chain

    KW - Monte Carlo

    KW - Powertrain Design

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