Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

Abstract

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

“…43,44 In this algorithm, the best solution at each iteration is considered the output data, and the main goal is to decrease the error between this best solution and the other predicted solutions. The ANN structure consists of several artificial neurons, which are connected together to form the NN.…”

“…These input data can be produced from experiments or simulation results. 43,44 In this algorithm, the best solution at each iteration is considered the output data, and the main goal is to decrease the error between this best solution and the other predicted solutions. This target solution is updated with the course of iterations.…”

“…It is invented by Ali Sadollah and other researchers in 2018. 44 This current research study aims at (a) identifying the best values of the unknown PEMFC parameters, (b) developing an effective model of commercial PEMFCs based on NNA to simulate their real behaviors under various operating conditions, and (c) generating the various polarization characteristics for further investigations by other researchers. A comparison of its results with other optimization methods has proven its high efficacy and performance.…”

“…Recently, NNA has been successfully applied to tune a fractional order fuzzy PID control of an automotive PEMFC air feed system. 44 This current research study aims at (a) identifying the best values of the unknown PEMFC parameters, (b) developing an effective model of commercial PEMFCs based on NNA to simulate their real behaviors under various operating conditions, and (c) generating the various polarization characteristics for further investigations by other researchers.…”

Special Issue “Intelligent Control in Energy Systems”

“…In [1], AbouOmar, Zhang and Su presented a fractional order fuzzy PID controller for a Proton Exchange Membrane Fuel Cell (PEMFC) air feed system, in order to achieve maximum power point tracking for the PEMFC stack, used the Neural Network Algorithm (NNA), a new metaheuristic optimization algorithm inspired by the structure and operation of ANNs to optimize the controller. A detailed simulation on MATLAB/SIMULINK environment proved the efficiency of the proposed controller over other types of controllers.…”