Towards Predictive Lifetime-Oriented Temperature Control of Power Electronics in E-vehicles via Reinforcement Learning

As the electric vehicle (EV) industry rapidly grows, the reliability of EVs is an ongoing challenge to the automotive industry. Among them, due to the introduction of electric motors, the aging and degradation of power electronics in EVs have a direct influence on the overall system safety and may lead to total failure. Therefore, extending the lifetime of power electronics has been a focus in the last decades, where reducing the temperature swings is the key to achieving the goal. However, temperature optimization usually requires future information on power loads, which is not available in classic approaches. Therefore, in this paper, we propose a baseline framework for lifetime-oriented temperature control with reinforcement learning (RL). Focusing on long-term prediction, the framework integrates various physical modelings of EV modules (sensors, actuators, vehicle dynamics and temperature management) and utilizes real-time route information to train an agent for driving behavior prediction. Based on further interaction with the EV model, future temperature development can be estimated in advance, thus enabling better swing optimization. Experiments demonstrate the effectiveness of our approach and the lifetime of power electronics can be extended by up to 63% on a representative test route. Compared to the classic approach, our predictive temperature control shows impressive energy efficiency by achieving up to 2.8x power loss reduction with better lifetime optimization.