In vehicular fuel cell, the change of operating parameters (pressure, temperature, humidity) may lead to health problem, which is a key parameter for fuel cell system shutdown. In this study, the health state of the proton exchange membrane fuel cell is recognized by considering several typical operating parameters. The cell voltage consistency (spatial fluctuation degree) is used to characterize the health state of fuel cell. Specifically, the health state of the minimum cell voltage is also considered. The process of health states labeling is achieved with the non-parametric statistics and unsupervised learning methods by calculating the threshold values for health evaluation indexes. Moreover, a variety of feature selection methods are applied to select the features which have relatively significant on health of fuel cell for improving the efficiency of health recognition. In addition, the random forest algorithm is used to identify the health state of based on the results of feature selection. The main results show that the relatively optimal features are temperature, current, cathode stoichiometry and pressure, respectively. Furthermore, the accuracy rate of random forest algorithm achieves to 95.04%. The effectiveness of the proposed methods is validated under operation condition of low current density and various temperatures by the results of dynamic loading experiments. The presented method of health recognition can be used to health management of fuel cell vehicle.

在车载燃料电池中，运行参数（压力、温度、湿度）的变化可能导致健康问题，这是燃料电池系统停机的关键参数。在这项研究中，通过考虑几个典型的操作参数来识别质子交换膜燃料电池的健康状态。电池电压一致性（空间波动程度）用于表征燃料电池的健康状态。具体而言，还考虑了最小单体电压的健康状态。通过计算健康评价指标的阈值，采用非参数统计和无监督学习方法实现健康状态标注过程。而且，应用多种特征选择方法，选取对燃料电池健康有较重要意义的特征，以提高健康识别的效率。此外，随机森林算法用于根据特征选择的结果识别健康状态。主要结果表明，相对最优的特征分别是温度、电流、阴极化学计量和压力。此外，随机森林算法的准确率达到了 95.04%。动态加载实验结果验证了所提方法在低电流密度和各种温度工​​作条件下的有效性。提出的健康识别方法可用于燃料电池汽车的健康管理。随机森林算法用于根据特征选择的结果识别健康状态。主要结果表明，相对最优的特征分别是温度、电流、阴极化学计量和压力。此外，随机森林算法的准确率达到了 95.04%。动态加载实验结果验证了所提方法在低电流密度和各种温度工​​作条件下的有效性。提出的健康识别方法可用于燃料电池汽车的健康管理。随机森林算法用于根据特征选择的结果识别健康状态。主要结果表明，相对最优的特征分别是温度、电流、阴极化学计量和压力。此外，随机森林算法的准确率达到了 95.04%。动态加载实验结果验证了所提方法在低电流密度和各种温度工​​作条件下的有效性。提出的健康识别方法可用于燃料电池汽车的健康管理。随机森林算法的准确率达到95.04%。动态加载实验结果验证了所提方法在低电流密度和各种温度工​​作条件下的有效性。提出的健康识别方法可用于燃料电池汽车的健康管理。随机森林算法的准确率达到95.04%。动态加载实验结果验证了所提方法在低电流密度和各种温度工​​作条件下的有效性。提出的健康识别方法可用于燃料电池汽车的健康管理。