Temperature Prediction in Car Lamps Using Sun Load Data. A simulation model development study for the rear lamps of the Volvo EX90

Abstract

Heat-sensitive materials and components, such as batteries, sensors, cameras, and polymeric materials, are a significant part of most cars on the market. To avoid damage to these parts, it is important to know what temperatures they will be subjected to. Sun Load Data (SLD) uses loads generated by the sun, solar radiation, to predict temperatures in different areas of the car.

This thesis aims to conduct an SLD study using a detailed rear lamp model of the Volvo EX90, which also includes the different material properties for each component, such as emissivity, transmittance, reflectivity and absorptivity. The material properties of the different components that make up the lamp will be measured and used as part of the input parameters. Different types of convection (using the lump capacitance model 0−D or 3−D) are simulated and compared, taking into account the level of accuracy and runtime. Different days are also selected to be used as the input weather file, making an attempt at extending the correlation to days when the ambient or part temperature is the highest. The effect of the external wind as well as the LED light source on the temperature readings are also taken into account.

The developed virtual model can be used in the early development phase for requirement setting, material choice, and the geometric design of the lamp. It can be used as an early checkpoint to decide whether the aforementioned parameters are ideal. This will therefore eliminate the situation where the vehicle is already in the physical testing phase, and the lamp experiences performance or aesthetic failure. Areas with potential for improvement are discussed together with their probable reasons, along with actions to mitigate them.