The detachment and calculation of functionalities from a vehicle into a cloud creates new chances. By linking different data sources with the in-vehicle data in the cloud, an optimization of these functionalities in terms of en-ergy efficiency can be applied. For example, the Heating, Ventilation and Air Conditioning (HVAC) consumes up to 30% of total energy in a vehicle. Electric vehicles in particular lead to these high values because they are not able to re-cover the waste heat from combustion engines for interior heating. Therefore, the optimization of energy efficient strategies with respect to the vehicle energy management system becomes more relevant. Forecasts of the interior vehicle temperature are directly related to the HVAC energy consumption. This work focuses on the implementation and accuracy evaluation of Recurrent Neural Networks (RNN) for interior vehicle temperature forecasting.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Using Machine Learning to Optimize Energy Consumption of HVAC Systems in Vehicles


    Beteiligte:

    Erscheinungsdatum :

    2019



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Balancing of HVAC System Energy Consumption in Electric Vehicles

    Poal, Vinay / Panchare, Datta / Mehta, Bhavik | British Library Conference Proceedings | 2021


    Balancing of HVAC System Energy Consumption in Electric Vehicles

    Mehta, Bhavik / Panchare, Datta / Poal, Vinay | SAE Technical Papers | 2021


    Estimating the HVAC energy consumption of plug-in electric vehicles

    Kambly, Kiran R. / Bradley, Thomas H. | Tema Archiv | 2014



    HVAC HVAC module for vehicles

    JEONG JAE HO / KIM JIN HWA / PARK SANG MUN | Europäisches Patentamt | 2020

    Freier Zugriff