Dynamic compact thermal models for skin temperature prediction of portable electronic devices based on convolution and fitting methods

Abstract

In most portable electronic devices, besides the temperature of multiple heat sources, i.e. junction temperature, the temperature of the enclosure, i.e. skin temperature, should also be controlled to protect the user experience. Thus, generating the device-level compact thermal model for predicting the skin temperature will not only improve the efficiency of early-stage thermal design but also help to develop the model-based temperature control strategy. However, most of the existing modeling methods mainly focus on predicting the junction temperature. In this paper, the dynamic compact thermal models of two portable electronic devices, including a smartphone and laptop, are first generated based on the convolution method. Under the assumption of linear time-invariant (LTI) systems, the skin temperature of the two test devices could be fast calculated once the step response of each heat source is obtained. The results show that compared to the computational fluid dynamics (CFD) model generated in ANSYS Icepak, the transient deviation of the convolution-based model is within 5%. Then, a linear fitting model is proposed to real-time predict the skin temperature of laptops. Both the experimental and simulation results demonstrate that once the scale factors are trained, the linear fitting model could accurately real-time predict the skin temperature after 60s. The maximum transient deviation in the experiment and simulation are 4% and 7% respectively. The results indicate that the proposed modeling method has tremendous potential for both thermal design and control optimization.