PEMFC剩余使用寿命直接预测的混合方法

A Hybrid Method for Direct Prediction of PEMFC Remaining Useful Life

针对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)的剩余使用寿命预测问题,该文基于考虑双层电容效应的PEMFC动态半经验模型,提出一种通过直接预测方式结合数据驱动和模型驱动的混合预测方法。对于数据驱动,使用深度卷积网络提取多维老化数据的特征,传递到长短期记忆网络预测老化电压。对于模型驱动,将电压预测值作为自适应扩展卡尔曼滤波框架的观测值。分别基于静态和动态2种工况下的老化数据,利用混合预测方法分别进行短期和长期预测。短期预测结果表明,在动态工况下动态半经验模型能更有效地拟合老化电压数据。长期预测结果表明,基于动态半经验模型的预测误差更小,且混合方法预测的PEMFC剩余使用寿命更接近真实值。

For the remaining lifetime prediction problem of proton exchange membrane fuel cell(PEMFC),this paper proposes a hybrid prediction method combining data-driven and model-driven by direct prediction approach based on a dynamic semi-empirical model of PEMFC considering the double-layer capacitance effect.For the data-driven approach,features of multi-dimensional aging data are extracted using a deep convolutional network and passed to a long and short-term memory network for aging voltage prediction.For model-driven,the voltage predictions are used as observations in an adaptive extended Kalman filtering framework.Short-term and long-term predictions are performed using hybrid prediction methods based on aging data under two operating conditions,static and dynamic,respectively.The short-term prediction results show that the dynamic semi-empirical model can fit the aging voltage data more effectively under dynamic conditions.The long-term prediction results show that the prediction error based on the dynamic semi-empirical model is smaller,and the remaining useful life of PEMFC predicted by the hybrid method is closer to the real value.