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 es-tablished to systematically measure and analyze the dynamic response characteristics of reformer gas composition,high-temper-ature 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 ap-proach coupling the machine learning voltage prediction model and the energy conservation equations of sub-system was con-structed 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 sys-tems.
关键词
甲醇蒸气重整(MSR)/质子交换膜燃料电池/性能预测/预测模型
Key words
methanol steam reforming (MSR)/proton exchange membrane fuel cell/performance prediction/prediction model
维普
万方数据