In-depth characteristic analysis and wide range optimal operation of fuel cell using multi-model predictive control

Humidity and voltage are the critical parameters that have a significant influence on the thermal stability and electrical efficiency of the proton exchange membrane fuel cells (PEMFCs). However, the inherent coupling between the two parameters makes the controller design challenging from a system's perspective. Furthermore, frequent load fluctuations during fuel cell operation give rise to considerable fluctuations in voltage and humidity. To mitigate this undesirable variation, the system coupling of voltage and humidity and nonlinearity resulting from varying load are analyzed in this paper, where the coupled dynamics of relative humidity and output voltage are manipulated by the humidifier electrical power and compressor voltage. Based on the analysis, a multi-model predictive control (MMPC) scheme is formulated for the coupled plant dynamics, where the prediction model utilized is developed based on the subspace identification method for local models integrated with fuzzy membership weightings. The MMPC formulation has taken advantage of considering the actuator saturation and the allowable output range as input and output constraints. Simulation results show that the proposed MMPC can improve the tracking performance with a flexible trade-off between the relative humidity and output voltage.