Coordinated Control of the Fuel Cell Air Supply System Based on Fuzzy Neural Network Decoupling

Abstract

In order to achieve the goal of carbon neutralization, hydrogen plays an important role in the new global energy pattern, and its development has also promoted the research of hydrogen fuel cell vehicles. The air supply system is an important subsystem of hydrogen fuel cell engine. The increase of air supply can improve the output characteristics of a fuel cell, but excessive gas supply will destroy the pressure balance of the anode and cathode. In the actual operation of a proton-exchange membrane fuel cell, considering the load change, it is necessary not only to ensure the stability of reactor pressure but also to meet the rapid response of inlet pressure and flow in the process of change. Therefore, the coordinated control of the two is the key to improving fuel cell output performance. In this paper, the dynamic model of the intake system is built based on the mechanism and experimental data. On this basis, the double closed-loop proportion integration differentiation (PID) control and feedforward compensation decoupling PID control are carried out for the air supply system, respectively. Then, the fuzzy neural network decoupling control strategy is proposed to make up for the shortcomings that the double closed-loop PID cannot achieve decoupling and the feedforward compensation decoupling does not have adaptability. The results show that the fuzzy neural network control can realize the decoupling between air intake flow and pressure and ensure that the air intake flow and pressure have a good follow-up, and the system’s response speed is fast.

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

“…The coupling of OER and pressure in the ADS makes it difficult to achieve accurate, independent OER and pressure control, which can be effectively solved by using the decoupling strategies and realizing the high-precision independent control of OER and pressure [81,82]. In recent years, scholars have explored decoupling control strategies, including feedforward combined with internal model decoupling control strategy [83], dynamic fuzzy logic control (FLC) strategy [84], decoupled double closed-loop fuzzy PID control strategy based on feedforward compensation [85], double closed-loop PID control combined with fuzzy neural network decoupling control strategy [86], and a cascade decoupling control strategy based on sliding mode (SM) controller coupled with fuzzy extended state observer [87]. According to the study, adding fuzzy control strategies, feed-forward control strategies, and sliding mode control strategies to decoupled control systems can effectively reduce the complexity of control system design, enhance accuracy and robustness, and ensure that the OER and pressure stably track control independently when the load changes quickly.…”

Control of Oxygen Excess Ratio for a PEMFC Air Supply System by Intelligent PID Methods

“…Artificial neural networks are developing faster to form computational intelligence, integrated with fuzzy systems, genetic algorithms, and evolutionary mechanisms. In the control field, neural network has been widely utilized in modeling [30,31], prediction [32,33], controller design [34,35], and so forth. For fuel cell air supply control, Wang et al [34] presented an observer-based adaptive neural network control and used radial basis function neural networks in both observer and controller designs: the observer was used to estimate the variable according to the transformed canonical system, and the controller ensured all signals uniformly bounded while achieving prescribed transient and steady-state tracking performance.…”

“…For fuel cell air supply control, Wang et al [34] presented an observer-based adaptive neural network control and used radial basis function neural networks in both observer and controller designs: the observer was used to estimate the variable according to the transformed canonical system, and the controller ensured all signals uniformly bounded while achieving prescribed transient and steady-state tracking performance. Jia et al [35] uses a four-layer fuzzy neural network control strategy to compensate for the shortcomings of the two preceding controllers: the double closed-loop PID cannot achieve decoupling of intake air flow and pressure, and the feedforward compensation decoupling is not adaptive.…”

Quantum fuzzy neural network based on fuzzy number

“…The above-mentioned quantum neural networks [10][11][12] are the product of the fusion of quantum computing and artificial neural networks, aiming to improve the efficiency of neural networks in processing big data by introducing quantum computing into traditional neural networks. There are many quantum neural network models currently available, and they are still under continuous development and improvement.…”