Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization-Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is <3.5 %. Second, RBF neural network is introduced to identify the stack temperature and provide the accurate partial derivative y(k)/partial derivative u(k) which solves the problem of using sign function sgn(partial derivative y(k)/ partial derivative u(k) ) to approximate the partial derivative y(k) / partial derivative u(k) temperature control of air-cooled PEMFC, several controllers are compared, including PID, Fuzzy-PID, BP-PID and RBF-BP-PID. The proposed RBF-BP-PID achieves the best control effect, which reduces the integrated time