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.

A Novel Development of Soft Computing based Hybrid Power Point Tracking Controllers for Hydrogen Vehicle Application with New Wide Source DC-DC Converter

From the present power generation scenario, most of the power production companies are focusing on renewable energy systems because their merits are easy to install, more reliable power sources, free from atmospheric pollution, and better adaptable to all weather conditions. In this article, a Polymer Electrolyte Membrane Fuel Cell (PEMFC) module is selected in the first objective to produce constant and continuous power to the hydrogen-based electrical vehicle systems. This fuel module provides fast power production and gives good energy density. However, the demerits of this module are more current production and low-level voltage generation. A new single-switch DC-DC circuit is introduced in the second objective of this work for optimizing the current levels of the fuel module thereby reducing the source power loss of the proposed system. Here, another problem of the fuel module is nonlinear power production which is linearized by proposing the Cuckoo Search Optimization (CSO)-based Adaptive Network-based Fuzzy Inference System (ANFIS) in the third objective to extract the maximum voltage from the fuel module. The merits of this introduced Maximum Power Point Tracking (MPPT) Controller are better tracking speed, low-level disturbances in the consumer load power, high robustness, more sustainability, and easy operation. The entire proposed system is investigated by selecting the MATLAB Simulink tool. The introduced converter circuit is analyzed by selecting the programmed DC supply.

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.

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.

One-step electrodeposition of reduced graphene oxide-amorphous carbon composite coatings for proton exchange membrane fuel cell bipolar plates

In this paper, a convenient electrodeposition method is proposed to directly fabricate a reduced graphene oxide-amorphous carbon composite coating (rGO-ACC) on a 316L stainless steel substrate. The rGO-ACC coating is achieved through a one-step reduction by utilizing a choline chloride-ethylene glycol deep eutectic solvent (DES) with the dispersion of graphene oxide. The analysis results from SEM, Raman and XPS reveal that the obtained rGO-ACC coating, with layered wrinkle morphology, uniformly covers and covalently bonds to 316L stainless steel substrate. Potentiodynamic and potentiostatic polarization tests showed that the corrosion current densities of rGO-ACC coated 316L stainless steel were of the order of 10-7 A cm−2 in simulated proton exchange membrane fuel cells (PEMFC) working environment, indicating a significant improvement of corrosion resistance of 316L and an excellent electrochemical stability. Meanwhile, compared with the naked 316L steel, the interfacial contact resistance (ICR) of the coated stainless steel is significantly reduced due to the outstanding electrical conductivity of the coated rGO. The results manifested that deposited rGO-ACC on steel surface may be a highly promising modification method for PEMFC metal bipolar plates.

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

Dynamic thermal and mass transport in PEM fuel cells at elevated temperatures and pressures: A 3D model study

Next-generation proton-exchange membrane fuel cells (PEMFCs) encounter significant challenges at elevated temperatures (OTs) and pressures (OPs) due to local thermal and mass fluctuations. In this study, a three-dimensional transient model was developed to analyze these dynamics by incorporating the effects of the microporous layer and variations in the coolant temperature along the channel. The results indicate that increasing the OT from 80 °C to 90 °C decreases the output voltage by 34–78 mV, increases voltage undershoot/overshoot by 0.2–16.6 mV, and raises the temperature difference between the cathode catalyst layer (cCL) and coolant from 1.2 to 2.1 °C, leading to more irregular temperature fluctuations in the cCL. These effects stem from the increased gas–liquid water outflow within the cCL that induces membrane dehydration, particularly in the electrolyte near the channel. Consequently, the lag in proton conduction relative to the oxygen reduction reaction (ORR), as quantified by the newly introduced Damköhler number, has emerged as a critical factor that influences both heat transfer and reaction kinetics. Conversely, increasing OPs from 130 kPa [anode]/120 kPa [cathode] to 400 kPa [anode]/390 kPa [cathode] improves output voltage by 50–150 mV and reduces proton conduction hysteresis by 12–54 % under dynamic loads. This improvement is linked to higher O2 concentration and membrane water facilitated by a 0.6–2 °C decrease in cCL temperature. However, the rise in OPs also results in an increased voltage undershoot/overshoot due to greater fluctuations in the membrane water content, ultimately leading to additional power loss. These findings are crucial for optimizing PEMFC performance under extreme conditions and advancing fuel cell technology.

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 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.

Numerical analysis of the influence of cooling design on temperature uniformity in the large proton exchange membrane fuel cell stack

The temperature distribution has a significant influence on the performance and lifespan of proton exchange membrane fuel cell (PEMFC) stacks. However, analyzing the temperature distribution of large PEMFC stacks is challenging and has been rarely conducted. In this study, a simplified model of a large PEMFC stack is developed using computational fluid dynamics (CFD) methods, which includes 300 cells and 60 cooling plates. The influence of cooling parameters, including configurations, dimension and flow rate, is discussed. The study indicates that U-type stack exhibits better temperature uniformity than Z-type stack. Increasing the coolant flow rate can effectively reduce the stack temperature. The study proposes a modified cooling design, which effectively reduces the heat accumulation at the end of the stack. Lastly, a comparison between traditional serpentine channels and porous foam metal flow fields reveals that the porous foam metal flow field exhibits superior thermal management characteristics. The results can offer insights for thermal management of large-scale PEMFC stacks.