Proton Exchange Membrane Fuel Cell Research Articles

AI-Assisted Flow Field Design for Proton Exchange Membrane Fuel Cells: Progress and Perspective

AI-Assisted Flow Field Design for Proton Exchange Membrane Fuel Cells: Progress and Perspective

Dual-Sliding-Surface Robust Control for the PEMFC Air-Feeding System Based on Terminal Sliding Mode Algorithm

The proton exchange membrane fuel cell (PEMFC) is the most widely used fuel cell, but it also has some limitations. One of the research pain points is controlling the oxygen content in PEMFCs. A moderate excess of oxygen boosts electrochemical reaction efficiency, while an appropriate oxygen content ensures system stability. In this paper, a fourth-order nonlinear mathematical model of a PEMFC stack air supply system is established to solve the problem of optimal oxygen excess ratio (OER) control under dynamic load conditions. Based on the model, a nonsingular terminal sliding mode controller (NTSMC) based on a sliding mode observer (SMO) is proposed. The NTSM exhibits superior robustness and performance compared to other sliding mode structures. Meanwhile, the SMO accurately predicts system states, facilitating precise control actions. Additionally, the dual sliding mode surfaces enhance system stability against parameter uncertainties and external disturbances. Our results demonstrate that the proposed controller outperforms traditional ones in terms of robustness and performance, which significantly enhances PEMFC system efficiency and stability.

Fe‐N‐C in Proton Exchange Membrane Fuel Cells: Impact of Ionomer Loading on Degradation and Stability

AbstractFe single atoms in N‐doped C (Fe‐N‐C) present the most promising replacement for carbon‐supported Pt‐based catalysts for the O2 reduction reaction at the cathode of proton exchange membrane fuel cells (PEMFCs). However, it remains unclear how the I/C ratio affects Fe‐N‐C degradation and the stability of single Fe atom active sites (FeNx). Here, an accelerated stress test (AST) protocol is combined with emerging electrochemical techniques for a porous Fe‐N‐C in PEMFC with a range of I/C ratios. The PEMFC current density degradation rates are found to be comparable; however, with increased I/C ratio the additional FeNx sites accessed are more stable, as shown by their higher active site stability number (electrons passed per FeNx lost) at the end of the AST protocol. Meanwhile, the initial rate of TOF decay is suppressed with increasing I/C. Electrochemical process changes are studied via distribution of relaxation times analysis. Minor changes in H+ and O2 transport resistance at low current density prove kinetic degradation dominants at high potentials. These findings demonstrate how electrochemical techniques can be combined with stability metrics to determine and deconvolute changes from the active site to device level electrochemical processes in PEMFCs.

PEMFC Gas-Feeding Control: Critical Insights and Review

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model

Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions.

Prescribed Performance Control for PEM Fuel Cell Air Supply System Based on Fully Actuated Approach With Fixed Regulation Time

ABSTRACTThe problem of air feed system control with guaranteed prescribed performance and fixed time control is investigated for proton exchange membrane fuel cell (PEMFC). First of all, the original model of PEMFC air feed system is converted to fully actuated system using higher order fully actuated system (HOFAS) approach. A fixed time extended state observer (ESO) is employed to approximate the cathode pressure. The parameter uncertainty is compensated by an adaptive radial basis function neural network (RBFNN). Then a prescribed performance sliding mode controller is designed with fixed convergence time. The proposed control law could guarantee the prescribed transient response for oxygen excess ratio (OER) error under stack current changing and bounded uncertainty. Lyapunov approach is utilized to proof fixed‐time stability of proposed controller. Simulation results of the study tests illustrate that the presented controller is efficient under constant OER tracking control, constant OER tracking control with parameter perturbation, and variable OER tracking control, respectively.

A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation

Accurate prognostication of degradation plays an essential role in effectively enhancing the operational lifespan of proton exchange membrane fuel cells (PEMFCs). This paper proposes a novel enhanced correctional grey Verhulst model (IHS-CRGVM-RE), designed to prognosticate the degradation process of PEMFCs using the voltage of PEMFCs stack as a health indicator. First, the inverse hyperbolic sine function transformation is employed to attain optimal smoothness in data treatment. Then, the background value within the grey Verhulst model framework is modified based on cellular automata with rectangle techniques. Finally, a residual correction mechanism is applied to delineate the influences of error outcomes concerning PEMFCs degradation. Rigorous validation is provided via a comprehensive analysis based on two distinct PEMFCs datasets. The results demonstrate that the proposed model outperforms other data-driven models in prognostication accuracy, highlighting its significant importance for prognosticating the lifespan of PEMFCs.

Investigation of roll forming process and quality control factors for metal bipolar plates

Metal bipolar plates (BPPs) are crucial components of proton exchange membrane fuel cells (PEMFCs), with the quality of their formation affects the power generation efficiency and lifespan of the fuel cells. The predominant manufacturing method, stamping, often results in localized excessive stretching of metal BPPs, thereby limiting the depth of the flow channels. Roll forming, dominated by bending deformation, is a forming process that ensures uniform thinning and greater depth of BPPs. The multi-directional flow channel roll forming model for BPPs is constructed in this paper for the first time, the characteristics of the roll forming process is investigated. Effects of process parameters on the forming results are discussed. The investigation indicates that during the roll forming process, the channel ports and corners are critical areas where defects are most likely to occur. The average filling ratio of the longitudinal flow channels is 5.6% higher than that of the transverse flow channels. The plastic strain in the longitudinal flow channels is about 36% higher than in the transverse ones, which making the form is more difficult to form, but the thickness is more uniform. Increasing the diameter of the rollers can enhance the filling ratio and flatness, but it will exacerbate thinning at the corners of the flow channels. When the ratio of the flow channel width to the rib width is close to 1, the filling ratio of the BPP is higher, with less thinning, resulting in a better forming result. As the depth of the flow channel increases, resulting in a thinner BPP, when depth-to-width ratio exceeding 0.6 is more likely to lead to failure. Increasing the speed of roll forming can lead to a greater thinning of the BPP and may even cause it to crack. The thinner the sheet metal is, the higher the filling ratio of the BPP and the more uniform its thickness. Those results can provide technical references for the structural design and forming process optimization of BPPs.

Numerical Investigation of Single- and Two-Phase Flow Distribution in Large-Scale Proton Exchange Membrane Fuel Cells

Numerical Investigation of Single- and Two-Phase Flow Distribution in Large-Scale Proton Exchange Membrane Fuel Cells

RBFNN based fixed time sliding mode control for PEMFC air supply system with input delay

Ensuring rapid regulation of the oxygen excess ratio (OER) in proton exchange membrane fuel cells (PEMFC) during load changes is an important challenge. In this paper, fixed time sliding mode control of a PEMFC with input delay has been investigated. First, a simplified fourth-order nonlinear dynamical model with input disturbance and input delay is considered and a cascade structure is selected for the control design. A radial basis function neural network (RBFNN) is designed to estimate the input disturbance. To achieve precise estimation, a Cuckoo Search Algorithm is utilized to calculate the parameters of the RBFNN. Then, a new sliding mode controller is proposed for trajectory tracking within a fixed time. To ensure the effectiveness of the proposed controller, the fixed time convergence of both sliding and reaching phases is investigated and proven. Finally, a robust prediction based sliding mode control is designed for the PEMFC system that by incorporating the disturbance estimation, can eliminate the effect of input delay. The effectiveness and robustness of the proposed controller are validated via comparative simulations. It is noteworthy that this is the first study to propose predictor based control for input delay PEMFCs.

A Mathematically Optimized Design Solution for Structure of PEMFC Catalyst Layer Based on a Two-Phase Flow Model

Abstract Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising solution as the world is moving toward sustainable energy resources. However, in order to compete economically with existing technologies, further improvements in performance are necessary. Mathematical modeling and optimization are viable tools for designing better PEMFCs. This study aims to provide a framework for topological optimization of the electrode structure, with the ultimate goal of enhancing cell performance. To achieve this, a two-phase flow model of PEMFC is developed to characterize the cell performance. The model is then coupled with a topology optimization technique, which is the main focus of the present work, to seek an optimized constituent distribution in the catalyst layer. Results indicate that an electrode with a heterogeneous structure can enhance the overall cell performance by balancing various transport and rate processes. The optimized designs are investigated for various key factors, including effective diffusivity, effective conductivity, and liquid water management, to demonstrate how an optimized design can be advantageous.

Temperature management of liquid-cooled fuel cells based on active disturbance rejection control

In the proton exchange membrane fuel cell (PEMFC) systems, the operating temperature affects the gas transport, reaction rate and water balance inside the PEMFC stack, thus affecting the output performance and lifetime of the stack. Therefore, it is necessary to study the thermal management methods and control strategies to maintain the stack temperature at the expected value. A thermal management system model of the PEMFC based on the electrochemical reaction and thermodynamics is established. This study proposes a novel active disturbance rejection controller (ADRC) to solve the problem of excessive temperature fluctuation of liquid-cooled stack with the dynamic load current changes. The simulation data show that compared with the PID, Fuzzy-PID and PSO-PID controllers for the cooling fan, the overshoot amount of the coolant inlet temperature with the ADRC is significantly reduced from 1.02 %, 0.77 % and 0.26 % to 0.07 %, and the integral of time absolute error (ITAE) is decreased from 5.299 × 104, 5.206 × 104 and 1.255 × 104 to 3.930 × 103, respectively. The degree of temperature fluctuation is reduced and the parasitic power does not significantly increase. Therefore, the ADRC proposed in this study improves the temperature control effect of the PEMFC stack, which is conducive to enhancing the stack performance.

MPPT of PEM Fuel Cell Using DC-DC Boost Converter Based on SVM

MPPT of PEM Fuel Cell Using DC-DC Boost Converter Based on SVM

Current Advancements in the Behavior Analysis of EPDM Elastomers in Peripheral Applications of the Cathodic Side of PEMFC Systems

This review examines the latest developments in the study of how Ethylene Propylene Diene Monomer (EPDM) elastomers behave in peripheral applications of Proton Exchange Membrane Fuel Cells (PEMFCs), specifically on the cathodic side. The review highlights the crucial role of EPDM in maintaining the integrity and efficiency of PEMFCs in challenging conditions characterized by varying temperatures, humidity, and acidic environments. The study examines the impact of various additives and vulcanization procedures on EPDM's mechanical and chemical properties, demonstrating enhancements in tensile strength, thermal stability, and chemical resistance. The study also investigates the compounding methods and selection of fillers, such as silica and carbon black, to optimize the performance of EPDM. Additionally, the effects of prolonged operational circumstances on EPDM's mechanical integrity and aging resistance in PEMFCs are being examined. This research emphasizes EPDM's suitability for long-term use in fuel cell systems. This review aims to guide the design of more durable and efficient PEMFC systems by optimizing the use of EPDM elastomers.

Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network

In this paper, we propose a novel hybrid method for fault diagnosis of Proton Exchange Membrane Fuel Cells (PEMFCs) based on the combination of a physics-based model and a long short-term memory (LSTM) neural network. By incorporating the physics-based model in the fault diagnosis algorithm, we can access to several process variables not directly measured through sensors but related to the state of the PEMFC stack. The model estimates are subsequently combined with signals measured on the PEMFC stack and inputted to a LSTM neural network. The performance of the physics-guided LSTM is evaluated on an extensive dataset comprising a thousand hours of operation of a PEMFC stack under dynamic load profiles, proving the enhanced capability of the proposed fault diagnosis method in capturing complex fault patterns. Furthermore, the effectiveness of the proposed method in dealing with PEMFC aging is tested by using data from the initial phase of stack operation for algorithm training and reserving the data from the aged stack operation for the testing phase. The experimental results reveal that the proposed physics-guided LSTM method allows for a significant amelioration over purely data-driven LSTM.

Research Progress in Enhancing Proton Conductivity of Sulfonated Aromatic Polymers with ZIFs for Fuel Cell Applications

The escalating global temperatures and their adverse effects underscore the growing imperative for the widespread adoption of clean fuels, notably hydrogen. Proton Exchange Membrane Fuel Cells (PEMFC) emerge as a pivotal green energy technology, facilitating electricity and water generation. The optimization of PEMFC efficiency hinges on the judicious selection and fabrication of polymer membranes. Within innovative materials, Zeolitic Imidazolate Frameworks (ZIFs) represent a novel subclass within the expansive family of Metal-Organic Frameworks (MOFs). ZIFs exhibit promising potential in PEMFCs, owing to their distinctive properties such as a substantial contact surface, inherent porosity, and a sizable pore volume. This comprehensive review delves into composite membranes featuring ZIFs, shedding light on their chemical and thermal attributes. Additionally, the exploration extends to elucidating the diverse applications of ZIF compounds, accompanied by an in-depth discussion of selected chemical and thermal properties inherent to ZIF compounds. Incorporating ZIFs into various polymers yielded intriguing outcomes, demonstrating a notable enhancement in proton conductivity. The compilation of this review aims to provide researchers with foundational insights into the realm of ZIFs, serving as a valuable resource for future investigations and advancements in the field.

Optimizing Fe-N-C Electrocatalysts for PEMFCs: Influence of Constituents and Pyrolysis on Properties and Performance

Proton exchange membrane fuel cells (PEMFCs) are promising alternative technologies with applications in stationary power systems, vehicles, and portable electronics due to their low temperature operation, fast start-up, and environmental advantages. However, the high cost of platinum-based catalysts, in particular for the oxygen reduction reaction (ORR) of the cathode side, prevents their widespread incorporation. Fe-N-C electrocatalysts have emerged as viable alternatives to platinum. In this study, different precursor components were investigated for the way that they affect the pyrolysis process, which is crucial for tailoring the final catalyst properties. In particular, carbon allotropes such as carbon Vulcan, Ketjenblack, and carbon nanotubes were selected for their unique structures and properties. In addition, various sources of iron (FeCl2, FeCl3, and K[Fe(SCN)4]) were evaluated. The influence of the pyrolysis atmosphere on the resulting Fe-N-C catalyst structures was also assessed. Through an integrated structure and surface chemistry analyses, as well as electrochemical tests with rotating disk electrode experiments in acidic media, the ORR performance and stability of these catalysts were defined. By examining the relationships between carbon sources and iron precursors, this research provides valuable information for the optimization of Fe-N-C catalysts in fuel cell applications.

Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas (PtG), Power-to-Liquid (PtL), and Power-to-Heat (PtH) systems. For example, deep reinforcement learning has improved real-time decision-making in PtG systems, reducing operational costs and improving grid stability. Additionally, predictive diagnostics powered by ML have increased system reliability by identifying early failures in critical components such as proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges such as data quality, real-time processing, and scalability remain, presenting future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors, such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

A feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach for PEMFC air supply system

With the application of proton exchange membrane fuel cell (PEMFC) in the field of transportation and energy, the requirements for air flow and pressure tracking performance, efficiency and stability of PEMFC air supply system become higher and higher. To improve PEMFC cathode air flow and pressure control performance under load variation and avoid surge of air compressor, a novel feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach is proposed. Firstly, optimal references of oxygen excess ratio (OER) and cathode air pressure under different load are calculated based on net output power of PEMFC system analysis. Then, an extended state observer is designed to estimate the cathode air pressure and flow in real time, a feedback linearization sliding mode based decoupling controller is designed to enhance OER and air pressure tracking control performance and robustness. Considering on the operating trajectory and surge line of air compressor, a fuzzy logic based anti-surge compensator is designed to prevent air compressor from surge by compensating both air flow and pressure simultaneously. The proposed control approach is implemented in PEMFC air supply control experiments and the results demonstrates its effectiveness.

A novel leaf bionic flow field with structured multi-level channel based on Murray's law in proton exchange membrane fuel cell for enhanced mass transfer

The design of flow field has a significant impact on the performance of proton exchange membrane fuel cells (PEMFCs). In this study, a novel leaf bionic flow field is designed and optimized based on Murray's law. The power consumption ratio is first used in the bionic PEMFC. Additionally, an evaluation criterion, the mass transfer efficiency evaluation criterion (MTEEC), is proposed to characterize mass transfer efficiency, and field synergy theory is used to analyze performance differences in mass transfer among various flow fields. The results demonstrate that adding multi-level channel and obstacles in the flow field significantly enhances cell output performance and reduces voltage losses in mass transfer regions. Applying Murray's law to distribute multi-level channel improves the uniformity of oxygen concentration distribution in the flow field and alleviates under-rib water accumulation. Compared to the secondary stream flow field (SSFF), the structure mesh of leaf bionic flow field (SMLBFF) demonstrates a nearly 19% increase in current density output at 0.45 V. SMLBFF exhibits an 81.51% increase in convective mass transfer rate compared to SSFF at 0.4 V. Moreover, the MTEEC of SMLBFF shows improvements of 179.68% at 0.5 A/cm2 and 135.43% at 1.0 A/cm2, compared to SSFF.