Degradation Prediction of PEM Fuel Cell Stack Based on Multiphysical Aging Model With Particle Filter Approach
Degradation Prediction of PEM Fuel Cell Stack Based on Multiphysical Aging Model With Particle Filter Approach
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DOI：
10.1109/tia.2017.2680406
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发表时间：
2017-07-01
影响因子：
4.4
通讯作者：
Miraoui, Abdellatif
中科院分区：
文献类型：
--
作者：
Zhou, Daming;Wu, Yiming;Miraoui, Abdellatif
基于粒子滤波多物理老化模型和外推方法，提出了一种新的质子交换膜燃料电池(PEMFC)性能退化预测模型。提出的多物理老化模型考虑了燃料电池内部的主要物理老化现象，包括燃料电池欧姆损耗、反应活性损耗和反应物传质损耗。此外，为了得到变负荷条件下电化学活化损耗值的精确值，提出了一种求解隐式Butler-Volmer方程的二分法求解器。所提出的老化模型首先通过拟合PEMFC寿命开始时的极化曲线来初始化。在预测过程中，老化数据集被分为两个部分，学习阶段和预测阶段。在学习阶段，使用PF框架来研究退化特征和更新老化参数。然后选择合适的拟合曲线函数来满足训练好的老化参数的退化趋势，并在预测阶段进一步外推老化参数的未来值。通过使用这些外推的老化参数，可以从所提出的老化模型获得预测结果。用不同的老化测试曲线进行了三次实验验证。结果表明，该预测方法具有较好的鲁棒性和优越性。
In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler-Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method.