Dynamic Modeling And Predictive Control Of Proton Exchange Membrane Fuel Cells

The shortage of traditional fossil energy sources has prompted countries around the world to increase research and utilization of renewable energy sources.Proton exchange membrane fuel cell(PEMFC)is one of the important energy sources of the future because of its excellent characteristics such as low emission,high energy efficiency and low operating temperature.Modeling and control studies of PEMFC systems are important to improve the cell life,system efficiency,and dynamic response capability.However,the PEMFC system is highly nonlinear and time-lagged,and the output is affected by both the current system input and the historical output,so it is difficult to obtain good results by traditional modeling and control methods.Analyze the PEMFC system structure and working principle,and build the system experiment platform.The internal structure of PEMFC and the characteristics of each component are introduced,the loss of single-cell output voltage caused by three polarization overpotentials is analyzed,and the influence of system parameters such as cathode inlet pressure and stack operating temperature on the output voltage of PEMFC system is analyzed.The experimental platform of the PEMFC system was built to conduct experimental data collection and provide data support for subsequent experimental analysis,model validation and control simulation.The LBF deep learning dynamic model is proposed to address the problem of nonlinearity and time lag in the PEMFC system and the complexity of modeling.the LBF model is a fusion of two networks,LSTM and BPNN,where the LSTM network is used to extract time lag information from the historical output voltage of the PEMFC system and the BPNN network is used to extract regular information from the current system input parameters,and then the outputs of both networks are fused to extract further information to predict the output voltage through a fully connected layer.The LBF model is compared with the BPNN,LSTM,and SVR models in ablation experiments.The LBF model provides good prediction performance with a minimum mean squared error(MSE)of 1.303,which is 84.32%lower than the optimal value of the other three models,providing a model basis for subsequent control studies.The model prediction control strategy of the PEMFC output voltage is designed for the problem of the PEMFC system input coupling and the existence of constraints,while the control is difficult.Based on the established LBF dynamic model,the rolling time-domain optimization problem with coupled input constraints is proposed,the quadratic optimization objective function about the output voltage is established,and the rolling optimization solution algorithm based on genetic algorithm is studied.Simulation analysis shows the effectiveness and feasibility of model predictive control for PEMFC output voltage control,and analyzes the influence of parameters on the control performance.A comparative study is conducted with the traditional PID control method,and the results show that the model predictive control strategy has better control performance with 25%and 71.57%reduction in dynamic regulation time and maximum deviation,respectively,compared with PID control.