Development of a CAE Method for Predicting Solar Loading Impact for Electrical System Performance in an Automotive Cabin

A number of market factors such as customer demand for improved connectivity and infotainment systems, automated driver assist systems and electrification of powertrain have driven an increase in the number of electrical systems within the cabin of automotive vehicles. These systems have limited operating temperature windows, therefore markets with high ambient temperatures and solar loading represent a significant challenge due to high cabin temperatures. Traditionally climatic facilities have been used replicate the conditions seen in these markets in order to understand the performance of the electrical systems. However such facilities have a number of limitations such as fixed solar arrays, secondary radiation from the walls and substantial operating costs limiting testing to envelope tests. Therefore the requirement for CAE based approach to more accurately represent the conditions seen in the real world is clear. To this end this work presents a CAE method for predicting component and ambient temperatures within the cabin. To improve the understanding of the effect environmental factors have on cabin temperatures and to correlate the computational results an experimental methodology has been developed to collect in-field data. The key features of the test procedure include comprehensive instrumentation of vehicle cabin to measure ambient and surface temperatures, characterization of the ambient conditions local to the vehicle including; direct, diffuse and global solar irradiance, temperature, wind speed and direction. The results generated by the computational model have been also been correlated against those collected from the climatic wind tunnel to ensure robust behavior. A comparison of the temperature distributions for the real world and wind tunnel datasets is presented to determine the validity of wind tunnel testing.