This study presents a novel framework by which the energy consumption of an electric vehicle (EV) or the zero‐emissions range of a plug‐in hybrid electric vehicle (PHEV) may be predicted over a route. The proposed energy prediction framework employs a neural network and may be used either ‘off‐line’ for better estimating the real‐world range of the vehicle or ‘on‐line’ integrated within the vehicle's energy management control system. The authors propose that this approach provides a more robust representation of the energy consumption of the target EVs compared to standard legislative test procedures. This is particularly pertinent for vehicle fleet operators that may use EVs within a specific environment, such as inner‐city public transport or the use of urban delivery vehicles. Experimental results highlight variations in EV range in the order of 50% when different levels of traffic congestion and road type are included in the analysis. The ability to estimate the energy requirements of the vehicle over a given route is also a pre‐requisite for using an efficient charge blended control strategy within a PHEV. Experimental results show an accuracy within 20–30% when comparing predicted and measured energy consumptions for over 800 different real‐world EV journeys.


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    Titel :

    Method for estimating the energy consumption of electric vehicles and plug‐in hybrid electric vehicles under real‐world driving conditions


    Beteiligte:
    Shankar, Ravi (Autor:in) / Marco, James (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2013-03-01


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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