Proton Exchange Membrane Fuel Cell Research Articles

Unlocking the potentials using humidifier-dehumidifier for proton exchange membrane fuel cell waste heat recovery

This study addresses the dual challenges of waste heat dissipation and operational longevity degradation in proton exchange membrane fuel cells (PEMFCs) by proposing a novel hybrid system that integrates PEMFC with a humidifier-dehumidifier (HDH) unit. The primary objective is to enhance the energy efficiency and sustainability of PEMFC systems by leveraging waste heat for freshwater generation. A comprehensive mathematical model based on thermodynamic and electrochemical principles is developed to capture the system’s inherent irreversible losses. The performance metrics, including power density, energy efficiency, and exergy efficiency, are evaluated through systematic computational simulations. The proposed PEMFC/HDH hybrid system achieves a maximum power density of 4.8292 W cm-2 and an energy efficiency of 17.30%, showing a 1.48% improvement over standalone PEMFC systems. Parametric studies reveal that optimizing operating pressure significantly enhances system performance, while factors such as increased PEM thickness and thermodynamic loss composite parameter hinder it. Local sensitivity analysis identifies PEM thickness as the most critical parameter affecting performance. This research pioneers the integration of PEMFC and HDH for cogeneration, offering a viable path for performance enhancement and efficient waste heat utilization. The findings contribute to the strategic design and operational regulation of PEMFC-based hybrid systems for sustainable energy solutions.

A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction

The performance of proton exchange membrane fuel cells (PEMFCs) will gradually deteriorate during long-term operation. Accurate performance degradation prediction is crucial for extending the lifespan and improve the durability of fuel cells. This paper proposes a deep learning method (CNN-BiGRU-AM) that incorporates convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) and attention mechanism (AM) for fuel cell degradation prediction. In the proposed method, CNN extracts complex features from the input data through convolutional operations, BiGRU models temporal information by considering both forward and reverse directions of the input sequence, and attention mechanism highlights key information in the input data through weight allocation. The proposed method is validated using long-term experimental data from fuel cells under steady-state and quasi-dynamic conditions. The results indicate that the absolute error of the proposed method is less than 1.2mV for 97.94% of the data samples under steady-state conditions and less than 1.2mV for 94.82% of the data samples under quasi-dynamic conditions. The prediction accuracy and stability of the proposed method are significantly improved compared to other deep learning prediction methods.

Parameter Estimation of Proton Exchange Membrane Fuel Cells Using Chaotic Newton-Raphson-Based Optimizer

This paper presents a novel improved metaheuristic algorithm, Chaotic Newton-Raphson-Based Optimizer (CNRBO), for the Proton Exchange Membrane Fuel Cells (PEMFCs) parameter extraction problem. In this study, ten distinct chaotic maps are integrated within the Newton-Raphson-based optimizer (NRBO) to improve its performance. The chaotic maps are employed to define the probability of the trap avoidance operator (TAO). The proposed CNRBO algorithm is validated using standard benchmark optimization problems. Results proved its advantages over the standard NRBO algorithm in terms of accuracy, robustness, and convergence speed. For PEMFC parameters extraction using the proposed CNRBO algorithm, the objective function to be minimized is the sum of squared errors (SSE) between estimated and measured stack voltages across various data points. To validate the effectiveness and reliability of the proposed CNRBO, its performance is compared with recent algorithms on eight different PEMFC stack models including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. Statistical measures are employed to assess the superiority and robustness of the proposed CNRBO. Statistical analyses demonstrate that the proposed CNRBO algorithm outperforms existing algorithms in terms of accuracy, search capability, and convergence speed, solidifying its position as a powerful tool for PEMFC parameter estimation.

Green hydrogen, power, and heat generation by polymer electrolyte membrane electrolyzer and fuel cell powered by a hydrokinetic turbine in low-velocity water canals, a 4E assessment

Thousands of kilometers of man-made low-velocity water transfer canals around the world can be used as a source of renewable energy for electricity and green hydrogen production. These canals have not been well investigated as an energy source, according to the literature. In the present paper, three technologies of Hydrokinetic Turbine (HKT), Polymer Electrolyte Membrane Fuel Cell, and Electrolyzer (PEM-FC/EL) are utilized to produce electricity, green hydrogen, and heat using these canals. Thermodynamic, economic, and environmental models of the cycle are presented, coded in the Engineering Equation Solver software, and finally validated with published research and manufacturers’ data. Two scenarios were examined, first HKT, PEMEL, and PEMFC were used for electricity generation (power-to-hydrogen-to-power, P2X2P) and second only HKT and PEMEL were used for green hydrogen production (power-to-hydrogen, P2X). While both scenarios are economical, the P2X scenario has a smaller payback period (less than 2 years) and a higher net present value. Practical correlations are derived to determine the rate of hydrogen production, power generation, and emission reduction as a function of water velocity. The round-trip energy and exergy efficiency of the system is 46.17% and 20.78% and it reduces carbon dioxide by 0.874 tons/year when water velocity is 1.5 m/s.

A decoupling control of air supply for the PEM fuel cell with slide mode-active disturbance rejection controller

The air supply subsystem is crucial for optimizing proton exchange membrane fuel cells (PEMFCs). This study develops a transient model for the air subsystem and proposes a control strategy that decouples air pressure and flow using sliding mode-active disturbance rejection control (SM-ADRC). In this study, adjustments to the air compressor speed and the opening of the back-pressure valve are utilized to achieve this control strategy. The sliding mode-extended state observer (SM-ESO) is employed to estimate and compensate for uncertainties within the system, while the sliding mode-nonlinear state error feedback (SM-NLSEF) is used to simultaneously control air flow and pressure. The study evaluates the performance of the SM-ADRC controller across various scenarios and environmental pressures. Evaluations show that the SM-ADRC controller significantly outperforms traditional methods like Fuzzy-ADRC, ADRC, SMC, and PID, with overshoot reductions of 18.2%, 34.1%, 42.5%, and 63.7%, respectively, and adjustment time under 1 s. Additionally, SM-ADRC achieves a 4.1% and 1.8% improvement in efficiency compared to PID and ADRC.

H Induced Metal-Insulation Transition Boosts the Stability of High Temperature Polymer Electrolyte Membrane Fuel Cells.

High temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) have garnered significant attention due to their expanded range of hydrogen sources and simplified management systems. However, the frequent start-up and shut-down (SU/SD)caused fuel starvationoperation condition seriously deteriorates the performance and lifetime of the fuel cell.In this manuscript, VO2was incorporated in the anodeto restrain fuel starvationof the electrode. Under the operation condition, the insulative VO2would reversiblytransform into metallic HxVO2byintercalating hydrogen, and HxVO2can automatically release hydrogen,whichcouldact as the hydrogen bufferand suppress reverse-current degradation. After the hydrogen release, the generated insulating VO2would prevent theside reactions during fuel starvation.Comparedto the traditionalPt anode, the electrodewith VO2showed much higheroutput powerand greatly improved durability after fuel starvation. This work demonstrates the in-situreversible hydrogen storage/release-controlled metal-insulator phase transition strategy to enhance the durability of fuel cells.

H Induced Metal‐Insulation Transition Boosts the Stability of High Temperature Polymer Electrolyte Membrane Fuel Cells

High temperature polymer electrolyte membrane fuel cells (HT‐PEMFCs) have garnered significant attention due to their expanded range of hydrogen sources and simplified management systems. However, the frequent start‐up and shut‐down (SU/SD) caused fuel starvation operation condition seriously deteriorates the performance and lifetime of the fuel cell. In this manuscript, VO2 was incorporated in the anode to restrain fuel starvation of the electrode. Under the operation condition, the insulative VO2 would reversibly transform into metallic HxVO2 by intercalating hydrogen, and HxVO2 can automatically release hydrogen, which could act as the hydrogen buffer and suppress reverse‐current degradation. After the hydrogen release, the generated insulating VO2 would prevent the side reactions during fuel starvation. Compared to the traditional Pt anode, the electrode with VO2 showed much higher output power and greatly improved durability after fuel starvation. This work demonstrates the in‐situ reversible hydrogen storage/release‐controlled metal‐insulator phase transition strategy to enhance the durability of fuel cells.

Enhanced Low-Humidity Performance of Polymer Exchange Membrane Fuel Cells via Membrane Surface Engineering.

Polymer electrolyte membrane fuel cells (PEMFCs) play a pivotal role in meeting the energy needs of high-power applications such as construction and agricultural machinery and mobility. High-power operation often exacerbates problems associated with water management within the cell due to excessive water generation, affecting water distribution at the cathode and anode interfaces. Our research recognizes the importance of addressing challenges associated with the high-power operation of polymer electrolyte membrane fuel cells (PEMFCs) for use in high-power fuel cell applications. The introduction of surface-patterned membranes leads to overall performance enhancement and improved humidity stability, thereby mitigating critical issues related to high-power operation. The enhanced contact at the electrolyte/catalyst interface and the expanded three-phase interface contribute to better heat dissipation and water management, ultimately alleviating challenges associated with catalyst efficiency and water stability during high-power usage. Furthermore, the improved low-humidity performance and stability observed in our study leverage the excessive water generated during PEMFC low-humidity operation. This not only enhances overall performance but also presents an opportunity to improve efficiency by utilizing the excess water generated, potentially reducing the costs associated with humidification maintenance. Our findings highlight the potential of surface-patterned membranes to address both high-power and low-humidity challenges, offering a comprehensive solution for the optimal performance of PEMFCs in demanding applications such as construction and agricultural machinery, as well as in the field of mobility.

Study on Mitigating Membrane Degradation in Degraded Membrane Electrode Assembly of Polymer Electrolyte Membrane Fuel Cell through Temperature and Humidity Control

Study on Mitigating Membrane Degradation in Degraded Membrane Electrode Assembly of Polymer Electrolyte Membrane Fuel Cell through Temperature and Humidity Control

A Manganese-Doped Cerium-Based Metal–Organic Framework as a Radical Scavenger for Proton Exchange Membrane Fuel Cells with Superior Stability

A Manganese-Doped Cerium-Based Metal–Organic Framework as a Radical Scavenger for Proton Exchange Membrane Fuel Cells with Superior Stability

Selective Hydrogen Oxidation Catalyst for PEM Fuel Cells: Tungsten Cluster-Tuned Platinum Single Atoms.

The selective hydrogen oxidation reaction (HOR) electrocatalyst is crucial for enhancing the performance of proton-exchange membrane fuel cells (PEMFCs) against degradation caused by reverse currents. In this study, a catalyst comprising platinum single atoms (Pt1) finely tuned by tungsten nanoclusters (WNC) on an accordion-like nitrogen-doped carbon support (ANC) is presented. The tungsten nanoclusters, derived from phosphotungstic acid, are embedded within the carbon support, while the Pt single atoms are uniformly dispersed on its surface. Experimental results and theoretical calculations reveal that the WNC effectively reduce the hydrogen adsorption strength on Pt1 to an optimal level, thereby facilitating HOR catalysis. Notably, this adjustment also weakens oxygen adsorption, rendering Pt1 less effective for catalyzing the oxygen reduction reaction. Consequently, the catalyst Pt1/WNC-ANC exhibits high resistance to degradation from reverse currents when oxygen leaks into the anode. Meanwhile, it demonstrates an ultralow HOR overpotential with an outstanding mass activity, 13 times greater than commercial Pt/C. This work provides a highly active and selective low-Pt HOR catalyst, paving the way for the development of cost-effective, long-lasting, and robust PEMFCs.

Numerical Investigation of Effects of Obstacles in Flow Channels and Depth of Flow Channels for PEMFCs

The channel is a crucial component of the polymer electrolyte membrane fuel cell (PEMFC). Since the channel can change the reactant transfer capability, water removal capability, and distribution of the reactant, it affects the performance and durability of PEMFCs. This study investigated the effects of obstacles in the serpentine-type flow channel on the performance of PEMFCs by computational fluid dynamics (CFD). The height of the obstacles was varied to analyze the electrochemical performances of the fuel cells. In addition, the depth of the flow channel was varied to compare the performances of the PEMFCs. To better represent the real-world tendency, the agglomerate model and the Forchheimer inertial effect were used. The results showed that changes in the channel depth caused greater performance improvements compared to the installation of obstacles, due to the enhanced mass transfer and improved water removal. However, the results for the installation of obstacles showed the lower non-uniformity of the current density and a reduced pressure drop compared to the changes in the channel depth, offering advantages in terms of flooding, the fuel cell life, and the operating cost.

Strategies for the Design and Synthesis of Pt-Based Nanostructured Electrocatalysts in Proton Exchange Membrane Fuel Cells (PEMFCs)

Strategies for the Design and Synthesis of Pt-Based Nanostructured Electrocatalysts in Proton Exchange Membrane Fuel Cells (PEMFCs)

Structural Design and Optimization of Proton Exchange Membrane Fuel Cell Ejector

Proton exchange membrane fuel cells (PEMFCs) produce electrical energy using hydrogen as an energy source, characterized by enhanced energy conversion efficiency and diminished emissions, contributing to the sustainable development of energy. The hydrogen ejector is essential for improving the hydrogen utilization efficiency in PEMFCs. In this study, the theoretical design and simulation optimization of a hydrogen ejector used for a hydrogen fuel cell are performed in order to improve the efficiency of the hydrogen ejector. According to Sokolov’s design method, the dimensions of the ejector nozzle and mixing chamber were calculated. A three-dimensional fluid simulation model of the ejector was established, and the accuracy of the model was verified by the experimental results. The influences of the nozzle outlet distance, the mixing chamber diameter, the length–diameter ratio of the mixing chamber, and the nozzle curvature on the ejector ratio were studied under multiple working conditions, and the optimal structural size of the ejector was obtained to satisfy the working conditions. It was found that the maximum ejector ratio of 1.21 could be achieved at a nozzle exit distance of 9 mm, a mixing chamber diameter of 7 mm, a mixing chamber length–diameter ratio of 9, and a nozzle curvature of 0.02. This work can provide some insights into the relationship between the structural parameters and performance of hydrogen ejectors.

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.