High-Power LED Photoelectrothermal Analysis Based on Backpropagation Artificial Neural Networks

As an electroluminescent device, the coupling relationship between light-emitting diode (LED) input currents, optical power, and LED junction temperature is a complicated multiphysics process. In this paper, a simplified LED photoelectron-thermal (PET) model by artificial neural network (ANN), which can translate multiphysics field issue into a single physics field problem, is mentioned to study the coupling relationship. In the first, an LED lumens, optical power, and electric power at different temperatures are monitored in a temperature controlling integrating sphere. Then, a backpropagation (BP) ANN is trained by these data to construct an LED Photo-Electron-Thermal (PET) relationship. In addition, LED luminaire thermal analyzing is performed using a finite-element method on the outputs of the BP ANN. Finally, the advantage of this method in terms of saving computing resources and computing time is analyzed by comparing the degrees of freedom in different models. The result shows that at least 6.7 times computing resource are saved by this method, which will reduce the LED thermal management system analyzing time greatly. Finally, the application and extension of this model are discussed.