为研究车用质子交换膜燃料电池的预测和健康管理问题，提出了一种以相对功率损耗率为健康指标、灰狼优化（grey wolf optimizer, GWO）算法与径向基（radial basis function, RBF）神经网络相结合的方法（GWO-RBF），对车用质子交换膜燃料电池的剩余使用寿命进行预测。首先，通过对初始时刻燃料电池极化曲线的分析，构建以相对功率损耗率为健康指标的计算方法，并采用灰色关联度分析方法验证其可行性。然后，应用GWO算法优化的RBF神经网络预测车用质子交换膜燃料电池的剩余使用寿命。最后，采用两组数据集对提出的方法进行了验证分析。结果表明：与其他方法相比，提出的基于GWO-RBF方法的平均绝对百分比误差、均方根误差最小，决定系数最大，相对误差小于1%。可见本文方法能够以较少的数据集、较高的精度预测车用质子交换膜燃料电池的剩余使用寿命。

In order to study the prediction and health management of PEMFCs(proton exchange membrane fuel cells) for vehicles, a method combining GWO(grey wolf optimizer) and RBF(radial basis function) neural network with relative power loss rate as a health indicator was proposed to predict the remaining useful life of vehicular PEMFCs. Firstly, by analyzing the polarization curve of the fuel cell at the initial moment, a calculation method based on the relative power loss rate as a health indicator was constructed, and its feasibility was verified using the grey correlation analysis method. Then, the RBF neural network optimized by GWO algorithm was applied to predict the remaining useful life of vehicular PEMFCs. Finally, the proposed method was validated using two datasets. The results show that compared with other methods, the GWO-RBF method proposed in this paper has the smallest average absolute percentage error and root mean square error, the largest coefficient of determination, and a relative error of less than 1%. It is concluded that the proposed method can be used to predict the remaining useful life of vehicular PEMFCs with fewer datasets and better accuracy.