This work describes a method developed to simplify and reduce the cost of the standardized testing required to measure the energy consumption of electric motorcycles before they are sold on the Swiss market. The robust algorithms for estimating four characteristic parameters of electric two-wheelers are based on a linear dynamics model that is considered to be as simple as possible yet as complex as necessary to characterize the vehicles. The model equations are investigated analytically for their ability to find unique solutions, and it is shown that multiple solutions may exist. The algorithms are tested for their ability to handle various data frequencies, levels of noise, and initial guesses. Ultimately it is found that these methods effectively enable the use of simulation models trained on real-world driving to run mandated standard test cycles in place of expensive dynamometer testing to estimate on-road energy use. The results of this work have shown that: 1) Parameters for a simple grey-box model of an electric vehicle can be identified using a method which applies advanced algorithms to a large set of real-world data, 2) The results can be used to accurately predict electric vehicle energy use over standard cycles, 3) The numerical optimization algorithms are sensitive to initial conditions as predicted by the analytical solution, 4) The algorithm is sensitive to sampling frequency, however not to the degree which impedes its application for standard cycle analysis, 5) The algorithm is sensitive to noise and the cleaner the signals the more repeatable the result is. Real world energy use is inevitably higher than what standard cycles predict, which presents a rich area for further work based on the foundations presented in this paper.


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    Title :

    Electric vehicle parameter identification


    Contributors:

    Published in:

    Publication date :

    2012


    Size :

    10 Seiten, 13 Bilder, 3 Tabellen, 19 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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