Study of cell voltage uniformity of proton exchange membrane fuel cell stack with an optimized artificial neural network model

Su, Yanghuai; Yin, Cong; Hua, Shiyang; Wang, Renkang; Tang, Hao

doi:10.1016/j.ijhydene.2022.06.240

Cited by 18 publications

(4 citation statements)

References 47 publications

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“…A dead zone unit G1 is introduced in Figure 5 to generate this interval. The input-output relationship of G1 is shown in Equation (3). The water content frequently fluctuates in FC systems.…”

Section: Design Of the Water Content Regulation Approachesmentioning

confidence: 99%

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A Closed-Loop Water Management Methodology for PEM Fuel Cell System Based on Impedance Information Feedback

Liao

et al. 2022

Energies

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Water management is an important issue for proton exchange membrane fuel cells (PEMFC). The research mainly focuses on the diagnosis and treatment of faults. However, faults harm PEMFC and cause its durability decay, whatever duration they last. This study designs a closed-loop water management system to control the water content in a reasonable range which can not only avoid the faults of hydration and flooding but also improve the performance and durability of PEMFC. The proposed system introduces the measurement methodology based on the phase of single-frequency impedance, which corresponds numerically well with the water content. Moreover, two preferred operating conditions, cathode air stoichiometry and stack temperature, are adopted to regulate the water content with a trade-off between the time cost and power loss. The open-loop characteristics of water content on the temperature and air stoichiometry are studied to design the corresponding control strategy. Findings suggest that air stoichiometry is suitable for large regulation requirements of water content, while the temperature is suitable to meet small demands. Finally, the proposed closed-loop water management system is validated by experiments in variable-load and constant-load with disturbance situations. The results indicate that the proposed system effectively controls the water content within a 3% deviation from the desired value.

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Section: Design Of the Water Content Regulation Approachesmentioning

confidence: 99%

“…Proton exchange membrane fuel cells (PEMFC) have received much attention in the fuel cell (FC) field because of their high-power density. Performance and durability are the key design criteria for PEMFC [1][2][3]. In the automotive application, where PEMFC is widely adopted, the performance and durability of PEMFC must be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A Closed-Loop Water Management Methodology for PEM Fuel Cell System Based on Impedance Information Feedback

Liao

et al. 2022

Energies

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Yin

Chen

2023

Sustainability

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The hydrogen fuel cell is a quite promising green device, which could be applied in extensive fields. However, as a complex nonlinear system involving a number of subsystems, the fuel cell system requires multiple variables to be effectively controlled. Oxygen excess ratio (OER) is the key indicator to be controlled to avoid oxygen starvation, which may result in severe performance degradation and life shortage of the fuel cell stack. In this paper, a nonlinear air supply system model integrated with the fuel cell stack voltage model is first built, based on physical laws and empirical data; then, conventional proportional-integral-derivative (PID) controls for the oxygen excess ratio are implemented. On this basis, fuzzy logic inference and neural network algorithm are integrated into the conventional PID controller to tune the gain coefficients, respectively. The simulation results verify that the fuzzy PID controller with seven subsets could clearly improve the dynamic responses of the fuel cells in both constant and variable OER controls, with small overshoots and the fastest settling times of less than 0.2 s.

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“…Most of the literatures focused on the average cell voltage, and did not consider the cell voltage's uniformity, which is critical to evaluating a fuel cell stack's performance, health, and reliability. In our previous work [41], an artificial neural network fuel cell model was used to predict each individual cell voltage of a 60 kW PEMFC stack. However, the training and prediction processes are time-consuming for the previous model, of which the computational efficiency should be greatly improved for potential applications in the real-time control and diagnosis of fuel cell engines.…”

Section: Introductionmentioning

confidence: 99%

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

et al. 2022

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The voltage consistency of hundreds of cells in a proton exchange membrane fuel cell stack significantly influences the stack’s performance and lifetime. Using the physics-based model to estimate the cell voltage consistency is highly challenging due to the massive calculation efforts and the complicated fuel cell designs. In this research, an artificial neural network (ANN) model is developed to efficiently predict the cell voltage distribution and the consistency of a commercial-size fuel cell stack. To balance the computation efficiency and accuracy, a dimension-reduced method is proposed with different output-grouping strategies to optimize the ANN structure based on the experiment test of a 100-cell stack. The model’s training time falls nonlinearly from 16 min to 6 s with the output neuron number decreasing from 100 to 5, while the model can still predict the cell voltage distribution trends. With the proposed model, the stack’s cell voltage distributions could be reproduced with significantly lowered computation time, which is beneficial to evaluate the fuel cell status and optimize the control strategies.

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Study of cell voltage uniformity of proton exchange membrane fuel cell stack with an optimized artificial neural network model

Cited by 18 publications

References 47 publications

A Closed-Loop Water Management Methodology for PEM Fuel Cell System Based on Impedance Information Feedback

A Closed-Loop Water Management Methodology for PEM Fuel Cell System Based on Impedance Information Feedback

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

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

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