Research on PEMFC Internal Temperature Predictions and Thermal Management Strategy Based on a Kalman Algorithm

The study addresses proton exchange membrane fuel cell (PEMFC) internal temperature predictions and the designed thermal management strategy based on the predicted temperature. The effect of temperature difference on PEMFC operation performance is also explored to verify the feasibility of this thermal management strategy. First, a PEMFC thermal model is built using MATLAB/Simulink version 2019. The effectiveness of this model is confirmed by comparing with the calculated experimental bench thermal power under identical working conditions. An urban dynamometer driving schedule (UDDS) is simulated in a self-built experimental test. In the progress of simulating the UDDS in the experimental test bench, the PEMFC coolant outlet temperature is also monitored by the temperature sensor. Second, the PEMFC operation temperature is predicted with a Kalman filtering algorithm based upon the thermal model. The feasibility of the algorithm is evaluated by comparing the predicted and monitored temperatures. The result also shows that it is useful to predict the operating temperature in real time. Finally, two different experimental projects are implemented to further explore the effectiveness of the temperature prediction algorithm. The predicted temperature is designed as the core of the PEMFC thermal management strategy in one of the experiment projects. The effect of temperature differences is analyzed by monitoring single-cell voltage differences. The temperature difference can be controlled over a more reasonable range adopting this designed management strategy. A lower temperature difference benefits PEMFC operating performance.