Energy-Efficient Climate Control in Electric Vehicles Through Innovative Sensor Technology and Novel Methods for Thermal Comfort Evaluation
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.
KeywordsHuman thermal comfort Sensor fusion Face detection Pose detection Thermography Intelligent climate control Model predictive control
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