Degradation prediction of PEM fuel cell stack based on multi-physical aging model with particle filter approach
Degradation prediction of PEM fuel cell stack based on multi-physical aging model with particle filter approach
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DOI：
10.1109/ias.2016.7731863
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Daming Zhou;Yiming Wu;Fei Gao;E. Breaz;A. Ravey;A. Miraoui
中科院分区：
文献类型：
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作者：
Daming Zhou;Yiming Wu;Fei Gao;E. Breaz;A. Ravey;A. Miraoui
提出了一种基于粒子滤波的多物理老化模型的质子交换膜燃料电池（PEMFC）性能退化预测方法。提出的多物理老化模型使用老化系数来描述燃料电池随时间的不同物理老化现象，包括膜电导率损失、反应物传质减少和反应活性损失。为了准确地模拟激活损失，采用了隐式的Butler-Volmer方程。通过拟合燃料电池寿命初期的极化曲线，对老化参数的初始值进行了调整。在初始化老化模型的基础上，预测方法的第一步是在老化试验学习阶段使用基于贝叶斯蒙特卡罗的粒子滤波（PF）估计所有老化参数。然后选择合适的拟合曲线函数来满足每个训练好的老化参数的退化行为，并进一步在验证阶段提供老化参数的外推值。将这些外推的老化参数应用到老化模型中，得到验证阶段燃料电池输出电压的预测结果。结果表明，该方法对燃料电池的降解具有良好的预测效果。此外，获得的每一个老化参数都可以深入了解燃料电池运行过程中不同程度的物理老化过程，这对于理解降解机制非常重要。
In this paper, a novel prediction approach for proton exchange membrane fuel cell (PEMFC) performance degradation is proposed based on a multi-physical aging model with particle filter approach. The proposed multi-physical aging model uses aging coefficients to describe fuel cell different physical aging phenomena over time, including membrane conductivity losses, reduction of reactants mass transfer and reaction activity losses. In order to accurately model the activation loss, the implicit Butler-Volmer equation is used. The initial values of the aging parameters are tuned by fitting the fuel cell polarization curve at the beginning of life. Based on the initialized aging model, the first step of prediction approach is to estimate all the aging parameters using Bayesian Monte Carlo-based Particle Filter (PF) during the learning phase of experimental aging test. The suitable fitting curve function is then selected to satisfy the degradation behavior of each trained aging parameter, and further provide the extrapolated values of aging parameters in the validation phase. By applying these extrapolated aging parameters into aging model, the prediction result of fuel cell output voltage in the validation phase can be obtained. The results demonstrate that the proposed approach have good prediction performance for fuel cell degradation. In addition, each obtained aging parameters provides an insight into the different degree of physical aging process over time during the fuel cell operating, which is important to understand degradation mechanisms.