Widely acknowledged by experts, the inconsistency between the cells of the proton exchange membrane fuel cell stack during operation is an important cause of the fuel cell life decay. Existing studies mainly focus on qualitative analysis of the effects of operating parameters on fuel cell stack consistency. However, there is currently almost no quantitative research on predicting the voltage consistency through operating parameters with machine learning methods. To solve this problem, a three-dimensional model of proton exchange membrane fuel cell stack with five single cells is established in this paper. The Computational Fluid Dynamic (CFD) method is used to provide the source data for prediction model. After predicting the voltage consistency with several machine learning methods and comparing the accuracy through simulation data, the integrated regression method based on Gradient Boosting Decision Tree (GBDT) gets the highest score (0.896) and is proposed for quickly predicting the consistency of cell voltage through operating parameters. After verifying the GBDT method with the experimental data from the fuel cell stack of SUNRISE POWER, in which the accuracy score is 0.910, the universality and accuracy of the method is confirmed. The influencing sensitivity of each operating parameter is evaluated and the current density has the greatest influence on the predicted value, which accounts for 0.40. The prediction of voltage consistency under different combination of operating parameters can guide the optimization of structural parameters in the process of the fuel cell design and operating parameters in the process of fuel cell control.

专家普遍认为，质子交换膜燃料电池堆在运行过程中电池之间的不一致是导致燃料电池寿命衰减的重要原因。现有的研究主要集中在运行参数对燃料电池堆一致性影响的定性分析。然而，目前几乎没有通过机器学习方法通​​过运行参数预测电压一致性的定量研究。为了解决这个问题，本文建立了具有五个单体电池的质子交换膜燃料电池堆的三维模型。计算流体动力学 (CFD) 方法用于为预测模型提供源数据。在通过几种机器学习方法预测电压一致性并通过仿真数据比较精度后，基于梯度提升决策树（GBDT）的综合回归方法获得最高分（0.896），被提出用于通过运行参数快速预测电池电压的一致性。以SUNRISE POWER燃料电池堆的实验数据对GBDT方法进行验证，准确率为0.910，验证了该方法的通用性和准确性。评价各运行参数的影响灵敏度，电流密度对预测值的影响最大，占0.40。不同运行参数组合下电压一致性的预测可以指导燃料电池设计过程中的结构参数和燃料电池控制过程中的运行参数的优化。