The efficiency of energy management strategies for hybrid electric vehicles depends significantly on the accuracy of the prediction of the load power which has to be provided by the hybrid drive. For vehicles in fixed-route service, measurements gained during vehicle operation can be used for the design of a predictor for the online calculation of the expected speed profile which is directly related to the load power profile. The paper describes, how the relevant information contained in measurements of the vehicle speed and position is processed and compressed in order to obtain a prediction algorithm that can cope with the limited memory space and computing power of a vehicle controller. The characteristics of the resulting algorithm are illustrated with real-life data. The outline of the paper is as follows: In Section 2, the preprocessing of the measurements is described, which includes first the description of the vehicle route by using GPS measured trajectories of the vehicle (position over time) and second the merging of position and speed measurements in order to increase the accuracy of the noisy sensor data. In Section 3, the compression of the measurements into a smaller data set, which describes only the information relevant to the prediction of the speed profile, is presented. Then, the developed algorithm for the calculation of the prediction Ny(./s) is described. In Section 4, that algorithm is applied to real-life data and the Summary gives an outlook on the future work concerning the improvement of the prediction algorithm.


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    Titel :

    Prediction of power demand for hybrid vehicles operating in fixed-route service


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2008


    Format / Umfang :

    6 Seiten, 9 Bilder, 4 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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