甲醇重整高温质子交换膜燃料电池（MSRFC）是一种以甲醇为燃料的清洁高效能源转换装置。建立了目标功率为3.5 kW的MSRFC试验平台，系统测量和分析了高温燃料电池（HT-PEMFC）电堆功率阶跃期和稳定期甲醇燃料供给、重整气组分、电堆性能以及重整器、燃烧器和电堆温度等动态响应特征。基于试验数据研究了样本空间和不同机器学习方法对HT-PEMFC电堆性能预测的准确性和适用性，最终训练了高斯过程回归HT-PEMFC电堆电压预测模型。通过Simulink构建了耦合机器学习电压预测模型和MSRFC子系统能量守恒方程的系统仿真方法，可以准确预测MSRFC系统阶跃期和稳定期的功率、温度和响应时间，相对误差分别控制在1%和3%以内。试验结果和系统仿真模型可以为MSRFC系统优化和放大提供数据支撑。

Methanol steam reforming high temperature proton exchange membrane fuel cell (MSRFC) is a clean and efficient energy conversion device that utilizes methanol as fuel source. A MSRFC test platform with a target power of 3.5 kW was established to systematically measure and analyze the dynamic response characteristics of reformer gas composition, hightemperature fuel cell (HT-PEMFC) stack performance and temperatures of reformer, combustor and HT-PEMFC stack in the power step and stabilization periods caused by the variation of methanol fuel supply. The sample space and different machine learning methods were investigated for the accuracy and applicability of the HT-PEMFC stack based on the experimental data. The HT-PEMFC stack voltage prediction model was trained by means of Gaussian process regression. A MSRFC system simulation approach coupling the machine learning voltage prediction model and the energy conservation equations of subsystem was constructed in the frame of Simulink, which could accurately predict the power,temperature and response time of the HT-PEMFC stack during step and stabilization periods. The relative errors could be controlled within 1% and 3% respectively. The obtained experimental results and system simulation model could provide data support for optimization and scaling up of MSRFC systems.