Improving the climate system of cars is important since it is the largest auxiliary load in a standard vehicle with an increase of fuel consumption by up to 20%. In Electric Vehicles (EV) the range of the car is more limited than in a fossil fueled car; furthermore there is a limited waste heat available from the EV, approximately 2-3kW at 40oC for heating and defogging in winter. The goals of this report have been part of an existing European project (ICE) where the climate system of an electric minibus is being investigated. The specific objectives of this project were to develop a radiation model and integrate it in the existing thermal model of the cabin, validating the new model with existing experimental data, including the thermal model in the overall model of the complete vehicle and to use the existing AC-model to optimize the control with the aim of decreasing the energy consumption maintaining thermal comfort inside the cabin. The radiation model uses total radiation on a horizontal surface in order to calculate the radiation hitting the different parts of the car body and windows, finally the total radiative power entering the minibus is calculated. After including these calculations into the thermal model it could be seen that the results from the model in terms of cabin temperatures fit the experimental values surprisingly well. The control of the AC-system was optimized for a hot and sunny summer day in Italy which resulted in the AC-system working very hard following that the best control strategy was to reduce only the speed of the compressor in order to save energy. Calculations show that in the Normal European Driving Cycle (NEDC) the potential energy savings of following this control strategy can result in an energy saving of the AC-system by up to 27% compared to an unregulated case, with a maintained thermal comfort resulting in 4,2% increase in autonomy.

Indiana University-Purdue University Indianapolis (IUPUI) / Heating, ventilation, and air conditioning (HVAC) is the technology responsible to maintain temperature levels and air quality in buildings to certain standards. In a commercial setting, HVAC systems accounted for more than 50% of the total energy cost of the building in 2013 [13]. New control methods are always being worked on to improve the effectiveness and efficiency of the system. These control systems include model predictive control (MPC), evolutionary algorithm (EA), evolutionary programming (EP), and proportional-integral-derivative (PID) controllers. Such control tools are used on new HVAC system to ensure the ultimate efficiency and ensure the comfort of occupants. However, there is a need for a system that can monitor the energy performance of the HVAC system and ensure that it is operating in its optimal operation and controlled as expected. In this thesis, an air handling unit (AHU) of an HVAC system was modeled to analyze its performance using real data collected from an operating AHU using a wireless monitoring system. The purpose was to monitor the AHU's performance, analyze its key parameters to identify flaws, and evaluate the energy waste. This system will provide the maintenance personnel to key information to them to act for increasing energy efficiency. The mechanical model was experimentally validated first. Them a baseline operating condition was established. Finally, the system under extreme weather conditions was evaluated. The AHU's subsystem performance, the energy consumption and the potential wastes were monitored and quantified. The developed system was able to constantly monitor the system and report to the maintenance personnel the information they need. I can be used to identify energy savings opportunities due to controls malfunction. Implementation of this system will provide the system's key performance indicators, offer feedback for adjustment of control strategies, and identify the potential savings. To further verify the capabilities of the model, a case study was performed on an air handling unit on campus for a three month monitoring period. According to the mechanical model, a total of 63,455 kWh can be potentially saved on the unit by adjusting controls. In addition the mechanical model was able to identify other energy savings opportunities due to set point changes that may result in a total of 77,141 kWh.