提出了一种用于最大额定功率为110 kW的质子交换膜燃料电池（proton exchange membrane fuel cell, PEMFC）剩余使用寿命的麻雀搜索算法优化门控循环单元的方法，进行了超过600 h的动态循环耐久试验，以模拟不同路况下车载PEMFC的工作情况。为准确预测大功率PEMFC的剩余使用寿命，需考虑其在不同工作状态下输出电压，将输出电压根据不同功率点进行分类预测。将采样数组经过滤波处理，减少峰值，平滑降噪，然后基于数据驱动的方法以各工作状态下电压数据以及不同的训练集划分作为输入，并预测结果通过选取的评价指标与不同的常见时序回归算法证实此模型的准确性。以数据的60%作为训练集为例，麻雀搜索优化门控循环单元（sparrow search algorithm-gate recurrent unit, SSA-GRU）的预测结果对比时间卷积网络（temporal convolutional network, TCN）其平均绝对百分比误差（mean absolute percentage error, MAPE）在30、50、70、90、110 kW分别降低了0.110 5%、0.525 7%、0.308 4%、0.402 1%和0.831 9%。在规定的寿命截止时间点下，使用寿命预测误差最小仅为0.733%，且不同工作状态下的预测误差都优于其他预测算法。

A sparrow search algorithm was proposes for optimizing gated recurrent units for the remaining useful life of PEMFC (proton exchange membrane fuel cell) with a maximum rated power of 110 kW. More than 600 hours of dynamic cycle endurance tests were conducted to simulate the operation of on-board PEMFC under different road conditions. To accurately predict the remaining useful life of high-power PEMFC, it is necessary to consider its output voltage under different operating states, and classify and predict the output voltage according to different power points. Firstly, the sampling array was filtered to reduce peak values and smooth noise reduction. Then, based on data-driven methods, voltage data at different operating states and different training set partitions were used as inputs. The accuracy of this model was confirmed by the selected evaluation indicators and different common time series regression algorithms. The experiment took 60% of the data as the training set as an example, the prediction results of SSA-GRU compared to TCN showed a decrease of 0.110 5%, 0.525 7%, 0.308 4%, 0.402 1%, and 0.831 9% in MAPE at 30, 50, 70, 90, and 110 kW, respectively. At the specified deadline for useful life, the minimum prediction error for useful life is only 0.733%, and the prediction error under different working conditions is superior to other prediction algorithms.