Reinforcement Learning Algorithm for Junction Temperature Control of Automotive LED Chips

In the field of modern automotive lighting, LED headlights are increasingly occupying a core position due to their excellent brightness, flexible light color control, significant energy-saving and environmental benefits, long lifespan, compact design, lightweight, and anti glare characteristics. However, a key obstacle faced by LED headlights in practical deployment is the problem of thermal management: electrical energy is mainly converted into thermal energy, which not only weakens the luminous efficiency of LEDs, but also reduces their lifespan and affects the overall reliability of the system. To address this challenge, this paper creatively designs a junction temperature control algorithm for automotive LED chips that integrates reinforcement learning (RL) technology. This algorithm utilizes intelligent prediction mechanisms to dynamically adjust LED operating parameters, achieving precise management of LED chip junction temperature and significantly enhancing LED heat dissipation efficiency. Experimental data has shown that the application of this algorithm in automotive LED lighting systems effectively reduces the junction temperature of LED chips, prolongs the lifespan of LEDs, and improves the overall energy efficiency and reliability of the system while maintaining high brightness output.