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

  • Martin BöhmeEmail author
  • Andreas Lauber
  • Marco Stang
  • Luyi Pan
  • Eric Sax
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1018)


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 energy 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 recover 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.


Heating Ventilation and Air Conditioning (HVAC) Energy efficiency Internet of Things Machine learning 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Böhme
    • 1
    Email author
  • Andreas Lauber
    • 1
  • Marco Stang
    • 1
  • Luyi Pan
    • 1
  • Eric Sax
    • 1
  1. 1.Karlsruhe Institute of Technology, Institute for Information Processing Technologies (ITIV)KarlsruheGermany

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