Proton exchange membrane fuel cell aging performance prediction based on conditional neural networks
In the context of actively pursuing the"dual carbon"goals,the advancement of hydrogen energy has experienced unprecedented opportunities.As a vital component of the green transportation revolution,fuel cell vehicles play a key role in carbon reduction and achieving carbon neutrality.Such vehicles have also become a focus of research on new energy vehicles.Improving the intelligence of fuel cell vehicles and continuously optimizing their performance through machine learning algorithms have become important ways to enhance the efficiency of such vehicles.Proton exchange membrane fuel cells,one of the core technologies of fuel cells,continue to encounter significant challenges in commercialization and market adoption due to durability issues.Accurately predicting the aging performance of fuel cells is highly challenging due to their nonlinearity and dynamic characteristics,coupled with their ever-changing operating conditions.This paper proposes a novel fuel cell performance prediction model based on conditional convolutional neural networks.The proposed model combines linear trends and nonlinear dynamic feature predictions to iteratively forecast the aging performance of fuel cells using a recursive method.The experimental results confirmed the high accuracy of this model in long-term performance prediction,instilling confidence in its practical implications for enhancing the reliability and efficiency of fuel cell systems.
fuel cellmachine learningneural networktime series forecasting
万方数据
维普
