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    • Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning

    Adam Thor Thorgeirsson

    Band 117 von Karlsruher Schriftenreihe Fahrzeugsystemtechnik
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    In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.

    Umfang: XX, 155 S.

    Preis: 39.00 €

    Fächer:

    Maschinenbau und Verfahrenstechnik 

    Schlagwörter:

    Reichweite Federated Learning Probabilistic Predictions Driving Range Electric Vehicles Föderiertes Lernen Probabilistische Vorhersage Elektrofahrzeuge

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    Empfohlene Zitierweise
    Thorgeirsson, A. 2024. Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000171796
    Thorgeirsson, A.T., 2024. Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000171796
    Thorgeirsson, A T. Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning. KIT Scientific Publishing, 2024. DOI: https://doi.org/10.5445/KSP/1000171796
    Thorgeirsson, A. T. (2024). Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000171796
    Thorgeirsson, Adam Thor. 2024. Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000171796



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    Weitere Informationen

    Veröffentlicht am 3. September 2024

    Sprache

    Englisch

    Seitenanzahl:

    192

    ISBN
    Paperback 978-3-7315-1371-1

    DOI
    https://doi.org/10.5445/KSP/1000171796