Proton Exchange Membrane Fuel Cell System 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.

Development of optimal energy management strategy for proton exchange membrane fuel cell-battery hybrid system for drone propulsion

Drones powered by fuel cells have been developed to ensure long flight distances. Due to the low dynamic responsiveness of proton exchange membrane fuel cell (PEMFC) systems, they must be integrated with batteries to fully meet the power demand profiles of drones. In this study, a dynamic model of a PEMFC-battery hybrid system for drone propulsion is developed in MATLAB-Simulink® to establish the optimal energy management strategy (EMS) for ensuring efficient and safe operation. The proposed PEMFC system comprises two modules of an open-cathode PEMFC stack, which are connected in parallel. To estimate the dynamic behavior and performance of PEMFC, one-dimensional dynamic model of PEMFC considering two-phase transport through the gas diffusion layer is developed and simulated. A zero-dimensional dynamic model of the battery was also developed based on an equivalent circuit model. The resulting PEMFC-battery hybrid system model was simulated to determine the power required for the propulsion of the drone according to a flight scenario comprising five phases: takeoff hover, climb, cruise, descent, and landing hover. Three different EMSs are developed and simulated to define the optimal one for the PEMFC-battery hybrid system by comparing the total hydrogen consumption and the final SOC of the battery As a result, the EMS that equally splits the required power between two PEMFCs exhibits the lowest hydrogen consumption during the flight simulation. This study helps provide the optimal system design and control scheme of drones powered by fuel cells.

Dynamic characteristics of the hydrogen injector-ejector unit in a PEM fuel cell system

The ejector, as a fluid-dynamics controlled passive component, is a promising hydrogen recirculation device in proton exchange membrane (PEM) fuel cell systems. Nevertheless, its performance is significantly affected by dynamic operating conditions and the behavior of the injector. This study investigates the dynamic performance of an ejector integrated with a hydrogen injector in a 100 kW PEM fuel cell system. A dynamic computational fluid dynamics (CFD) model of the integrated injector-ejector unit is developed using a dynamic mesh method and validated through theoretical analysis and experimental data. A thorough analysis of the global performance and local fluid flow characteristics of the injector-ejector unit is conducted. The results show that the hydrogen injector, with a throat diameter of 2.00 mm, exhibits an adjustable linear flow range from 0 to 1.62 g/s under an inlet pressure of 2.0 MPa. Additionally, the primary mass flow rate can be linearly regulated by adjusting the valve gap, while the secondary mass flow rate exhibits asynchronous behavior with the primary flow. The velocity and temperature distributions within the injector-ejector unit undergo significant changes under dynamic conditions. These findings contribute to optimizing the design and control strategies of hydrogen supply components in PEM fuel cell systems.

Multi-objective energy management strategy for a dual-stack fuel cell heavy-duty truck via deep reinforcement learning

The Proton Exchange Membrane Fuel Cell (PEMFC) system, as an efficient, zero-pollution emission, and high-efficiency energy conversion device, is particularly suitable for heavy-duty transportation. Considering the limited power of the current PEMFC system, this study focuses on a hybrid fuel cell heavy-duty truck with dual stacks. Considering the optimal operating range of the PEMFC system and the health status of the lithium-ion battery pack, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm is established. This strategy optimizes the hydrogen consumption cost, degradation costs of lithium-ion battery and fuel cells. Additionally, a dynamic programming (DP) and an equivalent consumption minimization strategy (ECMS) based EMSs are established. The power allocation and overall transportation costs under the CSC driving cycle are compared and verified for the three EMSs. The results show that the DDPG algorithm provides a more optimal power allocation for the dual-stack fuel cells, improving the overall system efficiency, enhancing the lifecycle of heavy-duty trucks, and significantly reducing overall travel costs.

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization–Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is <3.5 %. Second, RBF neural network is introduced to identify the stack temperature and provide the accurate ∂y(k)∂u(k) for BP-PID, which solves the problem of using sign function sgn(∂y(k)∂u(k)) to approximate the ∂y(k)∂u(k) in BP-PID. Regarding the temperature control of air-cooled PEMFC, several controllers are compared, including PID, Fuzzy-PID, BP-PID and RBF-BP-PID. The proposed RBF-BP-PID achieves the best control effect, which reduces the integrated time and absolute error (ITAE) by 3.4 % and 15.8 % based on BP-PID in the startup and steady phases, respectively. Since the ∂y(k)∂u(k) provided by RBF changes softly and continuously during the control process, the parameters self-tuning ability of RBF-BP-PID is better than BP-PID. Third, to improve the control effect of RBF-BP-PID further, TLBO-DE is adopted to optimize the parameters of RBF neural network and BP neural network.

Dynamic performance analysis of proton exchange membrane fuel cell in marine applications

To investigate the dynamic characteristics of proton exchange membrane fuel cells (PEMFCs) on maritime vessels, a lumped-parameter model for the PEMFC systems was developed based on a hybrid vessel propulsion systems model. Simulations and analyses of the dynamic characteristics of PEMFCs were conducted under typical sailing conditions. The results reveal a noticeable hysteresis in the operating temperature of PEMFC stacks during vessel voyages, with a delay of about 25 s, leading to significant overvoltage in activation, ohmic, and concentration differences. Significant variations in vessel loads can cause large fluctuations in the component gas pressures in the cathode and anode flow paths within 16.6–19.8 kPa and 78.57–93.06 kPa, respectively. A comparison of different humidification levels for cathode and anode gases demonstrates that, at the same moment, as the humidification of cathode and anode gases increases from 20 % to 100 %, the water content in the proton membrane increases from 2.29 to 13.47, the ohmic impedance decreases from 1.35 mΩ/cm2 to 0.174 mΩ/cm2, and the overshoot of the fuel cell voltage decreases. The output delay decreases from 25 s to 8 s, enhancing overall fuel cell performance. These findings are significant for optimizing the design, performance, and real-time control of marine PEMFC systems.

Design and development of different adaptive MPPT controllers for renewable energy systems: a comprehensive analysis

As of now, all over the world is focusing on the Electric Vehicle (EV) technology because its features are low environmental pollution, less maitainence cost required, high robustness, and good dynamic response. Also, the EVs work continuously until the input fuel is provided to the fuel stack. Here, a Proton Exchange Membrane Fuel Cell (PEMFC) is used as an input source to the electric vehicle system because of its merits fast startup, and quick response. However, the PEMFC gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. The main aim of this work is to study different Maximum Power Point Tracking Techniques (MPPT) for the DC-DC converter-fed PEMFC system. The studied MPPT controllers are Adjusted Step Value of Perturb & Observe (ASV with P&O), Adaptive Step Size with Incremental Conductance (ASS with IC), Radial Basis Functional Network (RBFN), Incremental Step-Fuzzy Logic Controller (IS with FLC), Continuous Step Variation based Particle Swarm Optimization (CSV with PSO), and Adaptive Step Value-Cuckoo Search Algorithm (ASV with CSA). The selected MPPT controllers’ comprehensive study has been in terms of maximum power extraction, tracking speed of the MPP, settling time of the fuel stack output voltage, oscillations across the MPP, and design complexity. From the comprehensive performance results of the hybrid MPPT controllers, the ASV with CSA technique gives superior performance when equated to the other MPPT controllers.

High-speed magnetically levitated centrifugal hydrogen recirculation pump in proton exchange membrane fuel cells

This paper presents a novel magnetically levitated centrifugal hydrogen recirculation pump (MLCHRP) designed for proton exchange membrane fuel cells (PEMFCs). Compared to traditional hydrogen recirculation pump, the MLCHRP offers significant advantages, including no lubrication, no mechanical friction, and active vibration suppression. However, the compact design of the pump imposes constraints on the load capacity of the active magnetic bearings (AMBs). As a result, the rotor is more susceptible to instability when subjected to unbalanced disturbance forces. This paper details the structure of the MLCHRP and provides an analysis of the basic model for unbalanced disturbance forces. A method for suppressing these unbalanced disturbances is proposed, utilizing a Linear Extended State Observer (LESO) combined with Parallel Peak Filters (PPF). The impact of this approach on system stability is thoroughly examined, and the effectiveness of the proposed method for unbalanced disturbance suppression is experimentally validated. The experimental results show that the peak-to-peak displacement of the rotor decreased by 80%. Additionally, the synchronous component in the rotor's displacement FFT was reduced by 6 dB (from -19.2 dB), and the second harmonic component decreased by 23.8 dB (from -23.7 dB). Finally, aerodynamic performance tests confirm that the MLCHRP meets the application requirements of actual PEMFCs system.

An innovative 3D wave channel design for boosting the dynamic response of proton exchange membrane fuel cell stack

The dynamic response of proton exchange membrane fuel cell (PEMFC) is a crucial metric for evaluating these systems in vehicular applications. The forced convection effect can significantly enhance the dynamic response capabilities of proton exchange membrane fuel cells. In this study, we propose the application of the forced convection effect within a parallel flow field. This approach retains the advantages of low pressure drop at the inlets and outlets of the parallel flow field and facilitates rapid distribution of reaction gases, while simultaneously enhancing the dynamic response of actual PEMFC stacks without compromising drainage efficiency. we propose an innovative 3D wave channel design for PEMFC. Specifically, we design and assemble a stack optimized for performance. The proposed design is tested against the traditional 2D straight channel stack, with the 3D wave channel stack demonstrating an increase in peak power density by 8.05% to 33.92% across a relative humidity range of 20% to 100%. This novel design outperforms the 2D straight channel design in terms of gas distribution uniformity, and enhancedmass transfer to the diffusion layer, as well as reduction of concentration polarization in the PEMFC. It also enhances self-humidification under low humidity conditions. Most importantly, the 3D wave channel stack exhibits a superior dynamic response, with voltage fluctuations after current loading being 74.36% to 91.15% lower than those of the 2D straight channel stack. Furthermore, the 3D wave channel stack maintains superior voltage stability under starvation conditions. The findings from this study contribute significantly to optimizing the dynamic response capability of PEMFC systems, highlighting the potential of the 3D wave channel design in enhancing PEMFC performance.

A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells

Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.

Robust observer-based-α-variable model-free supertwisting fractional order sliding mode control for nonlinear PEMFC system with uncertainties and disturbance

The service life and efficiency of proton exchange membrane fuel cells (PEMFCs) are significantly related to the control performance of the air supply system. Therefore, this research develops a novel robust observer-based- [Formula: see text]-variable model-free supertwisting fractional-order sliding mode control ([Formula: see text]-MF-STFOSMC) for complex nonlinear PEMFC air supply system with unmeasurable state variables, model uncertainties, and external disturbance. First, the fifth-order nonlinear PEMFC air supply system model is presented, and the unmeasured state variables are estimated using a nonlinear disturbance observer (NDOB1). Second, the ultra-local model (ULM) algorithm is employed to reconstruct the complex nonlinear PEMFC air supply system, avoiding the need for precise modeling and reducing the controller design difficulty. To estimate and compensate for the uncertain dynamics in the ULM algorithm, another NDOB2 is proposed to realize zero estimation error and finite-time observation. Besides, to achieve optimal control performance with less input chattering, finite-time convergence, and fast response speed, the [Formula: see text]-MF-STFOSMC is designed using super-twisting fractional-order sliding mode control (STFOSMC) algorithm. Furthermore, the stability of [Formula: see text]-MF-STFOSMC via a closed-loop system is verified using the Lyapunov theorem. Finally, the nonlinear PEMFC air supply system model with the proposed controller is realized in MATLAB/Simulink environment, and the simulation results are given to show the superiority and effectiveness of the proposed technique.

Towards maximum efficiency of an open-cathode PEM fuel cell system: A comparative experimental demonstration

Despite its high energy density and zero-carbon emissions, the path to large commercialization for Proton Exchange Membrane Fuel Cell (PEMFC) systems faces challenges related to cost reduction, efficiency enhancement, and durability improvement. One key strategy to address these hurdles is the development of reliable control algorithms that ensure operation at the Maximum Efficiency Point (MEP) of PEMFC systems. This optimization is essential for achieving high efficiency, a crucial factor for PEMFC technology commercial viability. This work explores the experimental design, implementation, and evaluation of Maximum Efficiency Point Tracking (MEPT) control algorithms for a PEMFC system. The investigated methods span across five distinct categories: conventional, data-driven, metaheuristics, online model identification, and filter-based approaches. To assess these methods, a real-time small-scale PEMFC system test bench was built. The setup consists of 200 W open-cathode PEMFC, industrial step-up DC–DC converter, programmable load and a cost-effective measurement and monitoring unit. Merits and demerits of each method are then discussed based on different performance metrics such as tracking speed, hydrogen consumption and power stress. Comparative analysis of the results reveals that, while all the algorithms achieved a maximum electrical efficiency of about 48%, data-driven methods exhibited a superior trade-off between performance metrics. This ultimately leads to improved PEMFC durability.

Comprehensive performance analysis and optimization for PEMFC-heat pump combined system considering dual heat source

Traditional proton exchange membrane fuel cell (PEMFC) systems face the challenges of strong thermal–electrical coupling and insufficient heating capacity. This study proposes a PEMFC–heat pump combined system considering dual heat source, which effectively utilizes fuel cell waste heat and environmental heat to achieve significant improvements in thermal–electrical decoupling and heating capacity. Energy, economic, and environmental analysis models were established, and the effects of key parameters on the combined system were systematically analyzed, followed by multi-objective optimization. Results show that the PEMFC current density has the largest effect on the heating capacity, while the heat flow ratio exerts the strongest influence on economic performance. The minimum pollutant emissions are achieved when wind power is adopted for hydrogen production. Under an ambient temperature of 248.15 K, the combined system increases the heating capacity by 12.8 % and reduces the levelized cost of energy and greenhouse gas emissions by 7.85 % and 15.9 %, respectively. The optimal solution presents a strong nonlinear relationship with the PEMFC current density and condenser undercooling, the heat flow ratio is close to the upper limit (0.99), and the operating temperature is close to the lower limit (333.4 K). These findings provide innovative thermoelectric decoupling and efficient energy supply solutions for PEMFC systems and have important implications for accelerating the low-carbon transition of regional energy systems.

Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer

Proton exchange membrane fuel cell (PEMFC) is regarded as the most promising clean energy to address the fossil energy crisis and environmental pollution. However, it is susceptible to frequently variable load and the impurities of hydrogen, which can directly cause the degradation of performance over time during operations. Degradation prediction has received much attention in recent years, as it can improve the durability and reliability of the PEMFC system. This paper proposes an effective multi-step-ahead prediction for PEMFC degradation under various operational conditions by using variational mode decomposition (VMD), a double recurrent fuzzy neural network (DRFNN), and a light spectrum optimizer (LSO). The integrated method enables precise prediction of degradation trends of PEMFC using historical testing data, which brings together their advantages. To better learn degradation trends, VMD is applied to decompose the input voltage signal into a series of sub-signals with a simpler structure. Then, DRFNN with a feedback loop is developed to train each sub-signal model, which can learn and memorize past information. To further enhance the prediction precision of degradation model, LSO is adopted to automatically update the network’s weights. Finally, the prediction performance of the proposed method is experimentally verified under different load conditions. Compared with other degradation methods, the test results reveal that the proposed method can achieve significant improvements in terms of multi-step-ahead prediction accuracy and robustness.

PEMFC AST Protocol for Air-Air Starts: Screening of Cathode Tolerances

The long-term durability of the cathode catalyst layer continues to be one of the main obstacles for the widespread commercialization of proton exchange membrane fuel cells (PEMFCs) for transportation applications. A major challenge is air/air-start events that occur after long shut-down periods where an H2/air gas front passes through the anode and causes polarization of the cathode electrode to ≈1.4-4.5 V. To screen catalyst materials for their air/air-start tolerance, several accelerated stress test (AST) protocols have been developed. The two most commonly used AST protocols have been recommended by the U.S. Department of Energy (DoE): (1) catalyst support protocol consisting of triangle sweep cycles: 500 mV/s between 1.0 V and 1.5 V (H2/N2, 80ׄ°C, 100% relative humidity (RH)) designed to trigger corrosion of the carbon support; (2) unmitigated start-up/shut-down durability protocol involving an H2/air front, whereby each cycle consists of a start-up event, intermittent fuel cell operation, an unmitigated shut-down event, and an idle period under relevant air/air-start conditions (35°C, 100% RH). While the second protocol is a closer representation of air/air-start events that occur in a PEMFC system, it requires specialized fuel cell testing equipment to allow for an H2/air front in the anode electrode. Conversely, the triangle sweep protocol can be used for catalyst benchmarking on any fuel cell test bench and even in model systems like rotating disc electrode (RDE), or gas diffusion electrode (GDE) setups.In this work, we compared H2/air front-based start-up/shut-down (SUSD) aging to the voltage-controlled carbon corrosion AST for four catalyst coated membranes (CCM). Using in-situ electrochemical and ex-situ characterization techniques (e.g., μXRD, SEM, and TEM), the degradation is compared with respect to the rate of loss in the electrochemically active surface area (ECSA). Particularly the correlation between voltage losses and ECSA, Pt particle size growth, catalyst layer thinning, and homogeneity of aging were investigated. Our results with the catalyst support AST confirms that this method exclusively triggers the carbon corrosion aging mechanism without a significant increase in the Pt particle size and is therefore a suitable screening protocol for support stability. However, the more application relevant SUSD protocol resulted in a combination of comparatively mild carbon corrosion and severe Pt dissolution/redeposition that caused significant Pt size growth and Pt band formation. Furthermore, SUSD resulted in inhomogeneous aging where the Pt size growth is more pronounced towards the anode inlet of the CCM. Due to the difference in the dominating aging mechanisms, the voltage loss response at nominally the same loss of ECSA varies greatly for the two protocols. Therefore, typical voltage-controlled AST (1-1.5V) is found to be unsuitable for predicting the air/air start tolerance of catalyst material. Instead, we propose to use an alternative voltage-controlled protocol that combines the two main degradation mechanisms of air/air starts: triangle sweep cycles at 500 mV/s between 0.7 V and 1.4 V (H2/N2, 45ׄ°C, 100% relative humidity).

Bayesian expectation maximization-maximization for robust estimation in proton exchange membrane fuel cells: A comparative study

In this paper, we present a novel approach based on Bayesian Expectation Maximization-Maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional and nonlinear optimization problems, BEMM offers a robust framework that combines the benefits of Bayesian inference with the flexibility of expectation maximization and maximization techniques. By iteratively updating parameter estimates based on observed data and maximizing the likelihood of the model, BEMM effectively navigates the solution space to converge on accurate estimates of the unspecified variables in Proton Exchange Membrane Fuel Cells (PEMFCs)models. Through extensive experimentation and comparison with other metaheuristic techniques, including Arithmetic Optimization Algorithm (AOA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), and Biogeography-Based Optimization (BBO), we demonstrate the superior performance of our BEMM approach. Our results show that BEMM outperforms these alternative methods in terms of precision, convergence speed, and stability across various scenarios involving different numbers of unspecified variables. The implications of our findings are significant for both researchers and practitioners in the field of PEMFC modeling and optimization. By providing a more efficient and reliable method for estimating model parameters, our approach can facilitate more accurate predictions of PEMFC performance, leading to better-informed decision-making in the design, operation, and optimization of PEMFC systems. Furthermore, the robustness and versatility of BEMM make it well-suited for a wide range of optimization problems beyond PEMFC modeling, highlighting its potential impact across various domains of engineering and scientific research. In the sensitivity analysis, as the population size increases from 10 to 40, there is a significant improvement in solution quality by approximately 100 %. However, beyond a population size of 40, the marginal gains diminish, with only marginal improvements of less than 1 % observed despite further increases in population size up to 200.

Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions.

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications.

Multi objective optimization using artificial neural network to maximize the power output of PEMFCs

ABSTRACT Designing a PEM fuel cell model is exceedingly challenging because of its multivariate in nature. Optimization is required to achieve highest operating condition. Neural Network Model is one of the possible methods to solve complex problems. The polarisation curve of a PEMFC (Proton Exchange Membrane Fuel Cell) is investigated in this paper in relation to the effects of seven parameters, including temperature, relative humidity in the cathode, relative humidity in the anode, anode stoichiometry, cathode stoichiometry, partial pressure of H2, and partial pressure of O2, using an ANN (artificial neural network) model. Where model geometric parameters i.e. Channel width, Channel depth, Channel length, Rib width, Cell width, GDL thickness, CL thickness, Membrane thickness of PEMFC was constant. Initially single Objective Function (Output Power) is predicted. The research presented here makes predictions about a PEMFC stack's electrical performance under multiple operating conditions. Mathematical model was further verified using laboratory data. Co-efficient of Determination (R2), Mean Square Error (MSE), and Mean Absolute Error (MAE) was determined using the fuel cell stack voltage model and stack power model. The model results show the possibility of using ANN in the implementation of such models to predict the PEMFC system's steady-state behaviour.

Energy, Exergetic, and Thermoeconomic Analyses of Hydrogen-Fueled 1-kW Proton-Exchange Membrane Fuel Cell.

Exergy analysis evaluates the efficiency of system components by quantifying the rate of entropy generation. In general, the exergy destruction rate or irreversibility rate was directly obtained through the exergy balance equation. However, this method cannot determine the origin of the component's entropy generation rate, which is a very important factor in system design and improvement. In this study, a thorough energy, exergy, and thermoeconomic analysis of a proton-exchange membrane fuel cell (PEMFC) was performed, providing the heat transfer rate, entropy generation rate, and cost loss rate of each component. The irreversibility rate of each component was obtained by the Gouy-Stodola theorem. Detailed and extensive exergy and thermoeconomic analyses of the PEMFC system determined that water cooling units experience the greatest heat transfer among the components in the studied PEMFC system, resulting in the greatest irreversibility and, thus, the greatest monetary flow loss.

Degradation study and diagnostic technology for Nafion membrane

Developing a fast diagnostic technology for membranes and accurately predicting the lifetime of membranes that can effectively reduce the manufacturing cost and overcome the technical barriers of proton exchange membrane fuel cell (PEMFC) lifetime. This paper focuses on developing an ex-situ accelerated chemical degradation method and investigating the impact of chemical degradation on membrane structure and fuel cell performance. The results show that voltage drop and hydrogen permeation current can detect the state online and predict the remaining lifetime of membrane electrode assembly (MEA). The aging rate is faster by three orders of magnitude than that of the published results by the established aging way. This study provides an additional decision for evaluating performance degradation and fuel cell system optimization, which could potentially lead to further improvements in the efficiency and durability of PEMFC systems.