Energy-Efficient Climate Control in Electric Vehicles Through Innovative Sensor Technology and Novel Methods for Thermal Comfort Evaluation

  • Henning Metzmacher
  • Daniel Wölki
  • Carolin Schmidt
  • Jérôme Frisch
  • Christoph van Treeck
Conference paper

Abstract

The increasing emission of greenhouse gases caused by a growing global rate of motorization contributes substantially to global warming and climate change. Germany aims to cut CO2 emission by 80% by the end of 2050 (BMWi 2012). In order to reach this goal, the transportation sector has to make a significant contribution. The required energy for engines in electric vehicles can be harvested from regenerative energy sources, therefore offering an opportunity for the reduction of greenhouse gas emissions. This work introduces a system for intelligent thermal management and energy-efficient climate control in electric vehicles adopting a sensor-based evaluation of individual thermal comfort of each passenger. By deriving individual measures for each person, the overall vehicle air conditioning system operates at much lower energy levels, which results in a drastic reduction of energy consumption and hence an increase in the driving range of the vehicle.

In order to evaluate the individual thermal comfort of each passenger, novel and innovative sensor technology is used. The paper presents a method for fusing temperature and humidity sensor information as well as different types of optical and thermal infrared sensors, proposing a structured approach to merge and evaluate the acquired data. The system itself consists of four consecutive sub-processes. Initially, camera-based sensors recognize the gestures and infrared signature of each passenger. Seat mounted heat and moisture sensors detect zonal microclimates at the interface between the seat surface and a person, thus completing the overall picture. In a subsequent step, this information is merged and pre-processed using a central software abstraction layer. The processed information is passed to high-level mathematical models in order to generate an accurate evaluation of the overall and local thermal condition of each passenger including the thermal physiology. Finally, individual control variables for local climate control are computed and sent to the vehicle’s air condition system. Furthermore, each passenger has the opportunity to give feedback on its individual thermal comfort level, which is subsequently used to individualize the prediction model for each passenger.

Keywords

Human thermal comfort Sensor fusion Face detection Pose detection Thermography Intelligent climate control Model predictive control 

References

  1. ANSI/ASHRAE. Standard 55-20013, Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, GA (2013)Google Scholar
  2. Blochwitz, T., et al.: Functional mockup interface 2.0: the standard for tool independent exchange of simulation models. In: Proceedings of the 9th International MODELICA Conference, pp. 173–184, 3–5 September 2012Google Scholar
  3. BMWi. Germany’s new energy policy. Federal Ministry of Economics and Technology (BMWi), Berlin (2012)Google Scholar
  4. Fanger, P.: Thermal Comfort. Danish Technical Press, Copenhagen (1970)Google Scholar
  5. Fiala, D., Lomas, J., Stohrer, M.: A computer model of human thermoregulation for a wide range of environmental conditions: the passive system. J. Appl. Physiol. 87, 1957–1972 (1999)Google Scholar
  6. Fiala, D., Lomas, K., Stohrer, M.: Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. Int. J. Biometeorol. 45, 143–159 (2001)CrossRefGoogle Scholar
  7. Nilsson, H.O.: Comfort climate evaluation with thermal manikin methods and computer simulation models. Indoor Air 13(1), 28–37 (2003)CrossRefGoogle Scholar
  8. Rangel, J., Soldan, S., Kroll, A.: 3D thermal imaging: fusion of thermography and depth cameras. In: International Conference on Quantitative InfraRed Thermography (2014)Google Scholar
  9. Schmidt, C.: Zusammenhang zwischen lokalem und globalem Behaglichkeitsempfinden: Untersuchung des Kombinationseffektes von Sitzheizung und Strahlungswärmeübertragung zur energieeffizienten Fahrzeugklimatisierung. FAT-Schriftenreihe 272 (2015)Google Scholar
  10. Schmidt, C.: Entwicklung eines Modellansatzes zur Bewertung der thermischen Behaglichkeit unter inhomogenen Klimabedingungen (2016)Google Scholar
  11. Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56, 116–124 (2013)CrossRefGoogle Scholar
  12. Smolyanskiy, N., Huitema, C., Liang, L., Anderson, S.E.: Real-time 3D face tracking based on active appearance model constrained by depth data. Image Vis. Comput. 32, 860–869 (2014)CrossRefGoogle Scholar
  13. Tanabe, S., et al.: Evaluation of thermal comfort using combined multi-node thermoregulation (65MN) and radiation models and computation fluid dynamics (CFD). Energy Build. 34, 637–646 (2002)CrossRefGoogle Scholar
  14. Wölki, D., Schmidt, C., van Treeck, C.: Neu-Kalibrierung eines Modells des menschlichen Thermoregulationssystems zur Untersuchung des Einflusses der Physiologie auf das thermische Komfortempfinden. Gebäudetechnik, Innenraumklima: GI 134(4), 238–251 (2013)Google Scholar
  15. Zhang, H.: Human thermal sensation and comfort in transient and non-uniform thermal environments. Center for the Built Environment (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Henning Metzmacher
    • 1
  • Daniel Wölki
    • 1
  • Carolin Schmidt
    • 1
  • Jérôme Frisch
    • 1
  • Christoph van Treeck
    • 1
  1. 1.Institute of Energy Efficiency and Sustainable Building (E3D)RWTH Aachen UniversityAachenGermany

Personalised recommendations