Predictive energy management strategy including traffic flow data for hybrid electric vehicles

K.R. Bouwman, T.H. Pham, S. Wilkins, T. Hofman

Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelAcademicpeer review

5 Citaties (Scopus)

Uittreksel

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

Vingerafdruk

Energy management
Hybrid vehicles
Telecommunication traffic
Fuel consumption
Fuel systems

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    Citeer dit

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    title = "Predictive energy management strategy including traffic flow data for hybrid electric vehicles",
    abstract = "Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[{\%}] compared to conventional strategies for various battery capacities.",
    keywords = "eHorizon, hybrid electric vehicles, predictive energy management, traffic flow data",
    author = "K.R. Bouwman and T.H. Pham and S. Wilkins and T. Hofman",
    year = "2017",
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    doi = "10.1016/j.ifacol.2017.08.1775",
    language = "English",
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    Predictive energy management strategy including traffic flow data for hybrid electric vehicles. / Bouwman, K.R.; Pham, T.H.; Wilkins, S.; Hofman, T.

    In: IFAC-PapersOnLine, Vol. 50, Nr. 1, 01.07.2017, blz. 10046-10051.

    Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelAcademicpeer review

    TY - JOUR

    T1 - Predictive energy management strategy including traffic flow data for hybrid electric vehicles

    AU - Bouwman,K.R.

    AU - Pham,T.H.

    AU - Wilkins,S.

    AU - Hofman,T.

    PY - 2017/7/1

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    N2 - Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

    AB - Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

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    KW - hybrid electric vehicles

    KW - predictive energy management

    KW - traffic flow data

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