Proton Exchange Membrane Fuel Cell System Research Articles

Fault diagnosis method of PEMFC system based on ensemble learning

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified.

Optimization on the peroxide ratio control strategy of PEMFC system based on immune algorithm

The air supply system is an essential component of a proton exchange membrane fuel cell (PEMFC), and proper control of the oxygen stoichiometry ratio(λo2) is crucial to prevent oxygen starvation in the PEMFC while maintaining efficient and stable performance output. In response to the issues of poor dynamic performance and slow response in traditional λo2 control strategies, this study was conducted to establish a dynamic oxygen flow model of the PEMFC and proposed a λo2 control strategy based on an immune algorithm-fuzzy PID (IAFPID). The performance of the IAFPID control strategy in λo2 control was analyzed under different load requirements, simulated vehicle dynamic loads, and temperature disturbances. The results demonstrate that the IAFPID control strategy has advantages such as fast convergence speed, strong robustness, and good dynamic performance. The efficiency of achieving the optimal stoichiometry ratio for high power output in PEMFC under disturbances was nearly 50% higher with IAFPID control compared to traditional PID, Fuzzy-PID, Genetic Algorithm PID, and Immune Algorithm PID strategies. Furthermore, this method ensures a lower average error in maintaining stable λo2 under dynamic disturbances compared to the IAPID and PID control strategies, with reductions of 12.93% and 37.75%, respectively.

A novel adaptive fast sliding mode control method based on fuzzy algorithm for the air management system of fuel cell stack

Proton exchange membrane fuel cells (PEMFCs) offer advantages, such as, cleanliness, high efficiency, and environmental friendliness. To achieve optimal control of the PEMFC stack's air supply and management system under complex operating conditions, a novel non-singular terminal sliding mode controller based on fuzzy algorithm is proposed. Compared with the conventional sliding mode control, the terminal sliding mode control exhibits further performance improvements. However, it still has limitations. The proposed innovative sliding mode structure would ensure finite-time convergence and avoid the issue of singularity associated with terminal sliding mode control. Moreover, fuzzy logic makes sliding mode gain adapt to the system and further enhances the performance and stability, which is benefited from a stronger sliding surface. Results indicate that, compared with the other controllers, the designed controller could reduce delay further and provide faster response to sudden changes. The overshoot of sliding surface has decreased by 70% compared to its previous level. This novel control approach significantly enhances the efficiency and reliability of the PEMFC system.

Dynamic model and robust control for the PEM fuel cell systems

In response to the escalating challenges posed by climate change, the global energy sector has witnessed a paradigm shift towards sustainable alternatives. The promising fuel cell technology known as the proton exchange membrane fuel cell (PEMFC) has found widespread use in a variety of mobile and stationary applications. This paper presents a super-twisting sliding mode (STSM) control for maximum power point tracking (MPPT) on the proton exchange membrane fuel cell (PEMFC) system incorporated floating interleaved boost converter (FIBC). This work aims to extract the maximum power from the PEMFC by means of robust control in conjunction with FIBC to improve current ripple. The proposed controller is designed for the PEMFC system by combining the STSM and MPPT methods. The designed MPPT control tracks down the maximum power point of the system, and the corresponding current values act as the reference values for the STSM controller. The results show that incorporating the PEMFC with the FIBC can result in current ripple reduction when compared with a traditional interleaved boost converter (IBC). The obtained results demonstrate the capability of the PEMFC closed-loop control system to maintain the system’s robustness under fuel cell parameter variations.

The seasonal-trend disentangle based prognostic framework for PEM fuel cells

Prognostics and Health Management (PHM) offer a potential solution to improve the durability of Proton Exchange Membrane Fuel Cells (PEMFCs), where degradation prognostic is the key component of the PHM system. However, limited by the dynamic operating environment, accurate and efficient prognostic algorithms still required for real applications. In this paper, the Seasonal-Trend Disentangle (STD) based prognostic framework is proposed to enhance the Remaining Useful Life (RUL) prediction of PEMFC system. Specifically, a lightweight parameter estimation neural network is introduced to estimate the aging parameters of the FC degradation model directly from PEMFC measurements. Besides, the integral of the FC degradation model at the specified current density range serves as the comprehensive Health Index (HI), which could combine different aging parameters and mitigate the effect of load dynamics. The STD-based prognostic model effectively captures the time-invariant features in the HI sequence from trend and seasonal phases, improving Remaining Useful Life (RUL) prediction performance. The proposed prognostic model is compared with RNN-family, ESN, and Dilated CNN methods using three distinct durability datasets from FCs operating under static, quasi-dynamic, and cyclically varying dynamic current loads. Experimental results demonstrate that the proposed algorithm outperforms the reference methods in terms of RMSE and MAPE metrics in the aging prognostic, and the proposed method could obtain satisfactory RUL prediction results 100–150 h earlier than the reference methods.

Temperature Control of Fuel Cell Based on PEI-DDPG

Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep reinforcement learning for stack temperature control. Given the PEMFC system’s characteristics, such as nonlinearity, uncertainty, and environmental conditions, we propose a novel deep reinforcement learning algorithm—the deep deterministic policy gradient with priority experience playback and importance sampling method (PEI-DDPG). Algorithm design incorporates technologies such as priority experience playback, importance sampling, and optimized sample data storage structure, enhancing the controller’s performance. Simulation results demonstrate the proposed algorithm’s superior effectiveness in temperature control for PEMFC, leveraging the PEI-DDPG algorithm’s high adaptability and robustness. The proposed algorithm’s effectiveness is additionally validated on the RT-LAB experimental platform. The proposed PEI-DDPG algorithm reduces the average adjustment time by 8.3%, 17.13%, and 24.56% and overshoots by 2.12 times, 4.16 times, and 4.32 times compared to the TD3, GA-PID, and PID algorithms, respectively.

Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

The proton exchange membrane fuel cell (PEMFC) stands at the forefront of advancing energy sustainability. Effective monitoring, control, diagnosis, and prognosis are crucial for optimizing the PEMFC system’s sustainability, necessitating a dynamic model that can capture the transient response of the PEMFC. This paper uses a dynamic fractional-order model to describe the behaviors of a stationary micro combined heat and power (mCHP) PEMFC stack. Based on the fractional-order equivalent circuit model, the applied model accurately represents the electrochemical impedance spectroscopy (EIS) and the dynamic voltage response under transient conditions. The applied model is validated through experiments on an mCHP PEMFC stack under various fault conditions. The EIS data is analyzed under different current densities and various fault conditions, including the stoichiometry of the anode and cathode, the stack temperature, and the relative humidity. The dynamic voltage response of the applied model shows good correspondence with experimental results in both phase and amplitude, thereby affirming the method’s precision and solidifying its role as a reliable tool for enhancing the sustainability and operational efficiency of PEMFC systems.

Effect of air depletion in the preferential oxidation reactor and fuel depletion in a proton exchange membrane fuel cell system fueled by natural gas

This study estimates the steady-state performance of a proton exchange membrane fuel cell (PEMFC) under conditions of air depletion in the preferential oxidation reactor (PrOx) and fuel depletion at the anode, which has not been previously studied. Considering the detailed chemical reactions of the reforming process, the composition of the reformed gas at the anode inlet is determined using the temperature and pressure data measured at various state points in the PEMFC system. To calculate the voltage loss at the anode due to the air depletion in PrOx or reformate gas depletion before the anode, the electrochemical reaction of the reformed gas in the anode catalyst layer is studied. The calculations show that when the fuel entering the anode or air entering the PrOx decreases, the amount of CO at the anode increases depending on the reduction, resulting in activation loss at the anode, and thereby leading to power loss. This analysis of the power loss in a PEMFC due to a lack of air or fuel can serve as an auxiliary diagnostic tool to identify faulty parts in the Balance of Plant (BOP).

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Proton Exchange Membrane Fuel Cells (PEMFCs) are promising sources of clean and renewable energy, but their performance and efficiency depend on an accurate modeling and identification of their system parameters. However, existing methods for PEMFC modeling suffer from drawbacks, such as slow convergence, high computational cost, and low accuracy. To address these challenges, this research work proposes an enhanced approach that combines a modified version of the SqueezeNet model, a deep learning architecture that reduces the number of parameters and computations, and a new optimization algorithm called the Modified Transient Search Optimization (MTSO) Algorithm, which improves the exploration and exploitation abilities of the search process. The proposed approach is applied to model the output voltage of the PEMFC under different operating conditions, and the results are compared with empirical data and two other state-of-the-art methods: Gated Recurrent Unit and Improved Manta Ray Foraging Optimization (GRU/IMRFO) and Grey Neural Network Model integrated with Particle Swarm Optimization (GNNM/PSO). The comparison shows that the proposed approach achieves the lowest Sum of Squared Errors (SSE) and the highest accuracy, demonstrating its superiority and effectiveness in PEMFC modeling. The proposed approach can facilitate the optimal design, control, and monitoring of PEMFC systems in various applications.

Transformer Based Long-Term Prognostics for Dynamic Operating PEM Fuel Cells

Proton Exchange Membrane Fuel Cell (PEMFC) is considered as a clean alternative energy that has been widely used in many fields. However, the large-scale commercialization of PEMFC is still limited by its durability performance. Prognostics and Health Management (PHM) is considered as a helpful solution to improve the durability of PEMFC with degradation prediction and health-based control and maintenance methods. The long-term PEMFC prognostic task, as a primary component in PHM, has received extensive attention from both academia and industry. Recently, the combination of deep neural networks and PEMFC prognostics has expressed a broad research perspective. The deep-learning-based methods, such as Convolutional Neural Network (CNN), Echo State Network (ESN), and Recurrent Neural Network (RNN) family methods, have been well-studied by many researchers and have shown satisfactory performance in degradation prediction. However, the long-term prediction performance is still limited by the model structure and accumulative errors. This paper focuses on the degradation of the PEMFC and a Transformer-based PEMFC prognostic framework is proposed to predict the long-term degradation of the PEMFC system. The Transformer model is applied for the first time to predict the degradation of the PEMFC, and a series-attention mechanism is proposed to replace the self-attention mechanism in the Vanilla Transformer which could improve the health indicator (HI) prediction performance. Finally, the PEMFC dynamic durability test data is utilized to evaluate the performance of the proposed framework in both multi-step-ahead prediction and long-term prognostic conditions, and the experimental results illustrate the feasibility and effectiveness of the proposed method.

Dynamic accelerated stress test and coupled on-line analysis program to elucidate aging processes in proton exchange membrane fuel cells

The application of hydrogen proton exchange membrane fuel cells (PEMFC) in greenhouse gas emission free heavy-duty vehicles requires extremely durable PEMFC components with service lives in the range of 30,000 h. Hence suitable test and analysis methods are required that reflect realistic operation scenarios, but significantly accelerate aging. For this purpose, a dynamic accelerated stress test was developed, which is coupled with a comprehensive in-depth in-situ and ex-situ analysis program to determine the aging processes of a PEMFC membrane electrode assembly (MEA). The test comprehends dynamic cycling between low, moderate and high load, different temperature and humidity conditions as well as recovery sequences to distinguish between reversible and irreversible failure modes. All phases of the PEMFC system (i.e. solid, liquid and gaseous) are monitored on-line during aging by sophisticated electrochemical, mass spectrometric and ion chromatographic analytical methods. The structural and elemental composition of the MEA before and after the aging program (post-mortem) are investigated by X-ray fluorescence, scanning and transmission electron microscopy. This program was able to age a commercial PEMFC to end-of-life in 1000 h, while providing an accurate picture of the aging processes involved.

Comprehensive performance assessment and multi-objective optimization of high-power proton exchange membrane fuel cell system under variable load

Due to the advantages of zero emission, high efficiency and low noise, Proton Exchange Membrane Fuel Cell (PEMFC) has grown to be the spotlight in the field of new energy in recent years. In this work, a high-power PEMFC system model is designed, which consists of the fuel feed Subsystem, the air feed subsystem, the thermal management subsystem and the stack. Among them, the material diffusion in the catalyst layer of the stack is intensively studied and derived. For the comprehensive system performance assessment, the thermodynamic, economic and environmental evaluation models have been developed. The mapping relationships between the operating parameters, the structural parameters and the system comprehensive performance under variable load conditions are obtained. The results of the study prove that increasing the operating temperature and decreasing the membrane thickness under high load current conditions are conducive to increase the system net power output, but at the same time increase the levelized cost of energy and the carbon mass specific emission. In addition, a multi-objective optimization strategy based on the NSGA-Ⅱ is designed to obtain the optimal performance of the system under different operating conditions. The optimized net power, the levelized cost of energy and the carbon mass ratio emission of the system for the final optimal operating point are increased by 40.5 %, 10.3 % and 33.3 % respectively. The aforementioned evidence substantiates the effectiveness of the proposed multi-objective optimization strategy in enhancing the overall performance of the PEMFC system.

Remaining useful life prediction of vehicle-oriented PEMFC systems based on IGWO-BP neural network under real-world traffic conditions

Accurately predicting the useful life can serve as a pivotal reference for effectively extending the lifespan of proton exchange membrane fuel cells (PEMFCs). Herein, this paper proposes a novel method that combines an improved grey wolf optimizer (IGWO) algorithm and a backpropagation (BP) neural network to predict the remaining useful life (RUL) of vehicle-oriented PEMFC systems using relative power loss rate (RPLR) as a health indicator under real-world traffic conditions. First, The Pearson correlation coefficient is used to analyze the correlation of various monitoring parameters and to verify the effectiveness of RPLR as a dynamic health indicator. Then, the IGWO-BP neural network-based prediction method is described and used to predict the RUL of PEMFC systems. Finally, the accuracy and reliability of the proposed method are validated against two separate datasets of PEMFC city buses operating under different traffic conditions. Compared with other methods, the proposed method has a relative error of less than 5 % and predicts a shorter RUL than the actual RUL. These findings illustrate that the proposed method has a high prediction accuracy and offers an early warning function, which is beneficial for practical applications.

Auxiliary-ejector-based hydrogen recirculation system to broaden PEMFC operating range

This study presents an auxiliary-ejector-based hydrogen recirculation system to widen the operating range of the proton exchange membrane fuel cell system (PEMFCS). An auxiliary ejector (BE) design model is presented and then an experimentally verified two-dimensional numerical-simulation model is built to study the relationships between PEMFC output power, ejector geometrical parameters, and its performance (μ) based on sensitivity analysis. Finally, the operating characteristics of the BE are clarified. The results show that the μ is highly sensitive to position (Lb), followed by width (w) and angle (A) of auxiliary port. The BE can provide satisfactory μ above 0.8 under higher power output, and a μ higher than 0.6 for lower power output with the Lb, A, and w of 1.1, 30°, and 4 mm when PEMFCS works from 17.9 to 84 kW. This proposed system can extend the operation range of PEMFCS using a single ejector, which can contribute to fuel cell development.

Performance investigation on the bypass ejector for a proton exchange membrane fuel cell system

The poor entrainment performance of the conventional ejector is a significant problem that makes it hard to use in proton exchange membrane fuel cell (PEMFC) systems. This study aims to evaluate the entrainment performance of a bypass ejector for a 100 kW PEMFC system. The effects of three critical geometric parameters, namely the axial position, width, and angle of the bypass inlet, on the entrainment performance are thoroughly investigated. The results demonstrate that the bypass flow exhibits a significant performance improvement in the critical mode. In contrast, the performance improvement is negligible and even negative in the subcritical mode. After careful evaluation of the entrainment performance across various stack powers, the optimal axial position, width, and angle of the bypass inlet are found to be 1.1, 2 mm, and 10°, respectively. A comparative analysis between the bypass ejector and the conventional ejector underscores a significant advantage for the former, exhibiting a remarkable 22.1 % increase in the hydrogen entrainment ratio at the stack power of 101 kW. Nevertheless, the entrainment performance of the bypass ejector diminishes when operating at low stack powers below 24 kW.

Multi-criteria assessment of an auxiliary energy system for desalination plant based on PEMFC-ORC combined heat and power

A novel auxiliary energy system for desalination plant based on proton exchange membrane fuel cell (PEMFC)-organic Rankine cycle (ORC) combined heat and power is proposed in this paper. A thermodynamic model of the system is established, and a dynamic net present value (NPV) calculation method under the context of time-of-day tariff is designed. The system output power, economic and environmental evaluation indicators under steady-state and dynamic operation are optimized using NSGA-III and TOPSIS. The parameters sensitivity analysis shows increasing the ORC system saturation pressure and the auxiliary equipment efficiency is beneficial to increase output power and reduce indirect CO2 mass specific emission (MSE). However, excessive pressure and efficiency will lead to a significant decrease in NPV. Under steady-state optimization, the electrical power of the system is increased by 20.1 % compared to the conventional PEMFC system. And when the proposed system operates dynamically, the single-day power generation is 1620 kWh, which is 9.31 % higher than the PEMFC system. The single-day heated seawater production is 110.1 tons and the MSE is 0.105 kg/kWh. The NPV decreases significantly, but the system can pay for itself over its lifetime. Therefore, the proposed system has thermodynamic and economic performance advantages over the conventional PEMFC system.

Sustainability evaluation of a PEMFC system for coalbed methane recovery based on life cycle assessment and emergy analysis

To promote the sustainable development of clean energy, a novel integrated system coupled coalbed methane recovery and proton exchange membrane fuel cell (PEMFC) is developed. Life cycle assessment (LCA) and emergy analysis model are established to analyze sustainability of the proposed system. The parametric analysis is performed to assess the effects of the reforming temperature, steam carbon ratio and current density on the emergy performance. The LCA results indicate that the construction phase has the most significant impact on system sustainability, accounting for 51.65%. Reducing the use of stainless steel and copper in the construction phase and improving the recycling process in the recovery phase can help improve the sustainability performance. The emergy analysis results show that the total emergy consumption of the system is 8.53E+16, the use phase accounted for 55.8%, the construction phase accounted for 43.8%, and the recovery phase only accounted for 0.4%. Parametric analysis shows that the emergy savings ratio increases by 7.11% as the reforming temperature is 800 °C. The emergy sustainability index ranges from 3.04 to 4.45 with great sustainability performance, as the increase of steam carbon ratio. The system has low environmental load and high sustainability when the current density is close to 1. This research will help improve the sustainability evaluation system of the energy system and promote the sustainable development of clean energy.

An intelligent control method for PEMFC air supply subsystem to optimize dynamic response performance

The excellent dynamic response performance of the proton exchange membrane fuel cell (PEMFC) system provides an important guarantee for quickly and accurately following the power demand of fuel cell vehicles. Insufficient air supply during load changes is the main reason for poor dynamic response. However, due to the strong coupling characteristics of flow and pressure, the existing technology has shortcomings such as poor anti-interference ability, slow decision-making speed and unstable control performance, which limit the optimization effect. To solve the above problem, this paper proposes a control method for air supply subsystem to optimize PEMFC dynamic response performance based on deep reinforcement learning. First, a model of the air supply subsystem is established, and its accuracy is verified through PEMFC system experimental bench. Second, the relationship law between the control parameters of air supply subsystem and the dynamic response performance of PEMFC is revealed. Finally, an air supply subsystem control architecture based on Soft Actor-Critic (SAC) is proposed. When the load power is stepped up from 58.5 kW to 101.6 kW, the time for the voltage to reach the steady state under intelligent control is reduced from 1.2 s to 0.7 s, and the electrical efficiency is increased from 72.6 % to 79.1 % compared with traditional decoupling control. The dynamic response performance is improved by 41.6 % and the efficiency is improved by 6.5 %.

Technical and economic feasibility of applying fuel cells as the power source of unmanned aerial vehicles

This study aims to quantitatively compare the weight, cost, and flight endurance of small unmanned aerial vehicles (UAVs) powered by batteries and fuel cells. We compare the use of lithium-polymer (LiPo) batteries, proton exchange membrane fuel cells (PEMFCs), and direct methanol fuel cells (DMFCs) in powering two representative small UAVs: KHawk-Thermal (fixed-wing, 1.4 m wingspan) and KHawk-CarbonQuad (quadcopter, 0.7 m diagonal length). This study uses experimentally measured polarization curves of fuel cells and power consumption data of LiPo-powered UAVs during flight tests as input to obtain the minimum mass, power consumption, and life cycle costs (LCC) of these power systems. The analyses consider the voltage, efficiency, and power at each current density to find the optimal operating current density to obtain the minimum weight and cost of fuel cell systems to obtain the desired flight endurance. The LiPo battery is the most cost-effective option to power UAVs with an endurance of 0.7 hrs (42 min) or less. At medium flight endurance (e.g., 0.8 – 2.1 hrs), the PEMFC system has the lowest mass and LCC. While hydrogen is the most energy-dense fuel, hydrogen storage significantly increases the mass and cost of the system. For UAVs undergoing long endurance missions (>2.2 hrs for fixed-wing UAVs, > 4.3 hrs for quadcopter UAVs), a DMFC system has the lowest mass and power consumption due to the high energy density of methanol and its simple storage requirements. This study also analyzes the impact of key parameters, such as the specific energy and power of battery and fuel cells, the mass ratio of hydrogen storage, and the lift-to-drag ratio, on UAVs' mass and power consumption.

The Positive Effects of Carbon-Coating Layers on Protecting the Nanoparticle Towards Poisoning Ions or Gases

Recently, the research that explores the positive effects of carbon-coating nanoparticles to fight the severe oxidation/corrosion in Alkaline fuel cell (AFC), Proton-exchange membrane fuel cell (PEMFC) or water electrolyzer is well-recognized, since the carbon layers can extend the catalyst’s durability towards hydrogen oxidation/evolution reactions (HOR, HER) and oxygen evolution/reduction reactions (OER, ORR). Especially for the reactions in alkaline pH, with the graphite coating, the Ni nanoparticles in both HOR and HER can resist passivation and hydride poisoning. [1,2] Similarly, in the same alkaline conditions, the amorphous carbon coating slows down the degradation of Pd catalysts.[3] Moreover, the coating can block big ions such as halide, thus benefiting Pt group metals from resisting halide adsorption, that eventually prevents the dissolution of metal.[4,5]In this work, we examined the gas poisoning effects on coated catalysts. For example, we synthesized carbon-coated Pd-Ni nanoparticles, supported on Vulcan carbon and studied its electrochemical response in 0.1M KOH at room temperature and its tolerance towards CO gas. As a result, Pd-Ni coated had better resistance to passivation compared to the non-coated Pd-Ni catalyst. It also showed that the thickness of the carbon coating layers was sufficient to block the CO absorption in RDE condition initially (Figure 1, black solid line). Only after 150 cycles of accelerated stress test in Ar (AST-Ar, using potential stepping between 0.1 and 1.23 V vs. RHE with a pulse of 3 s at each potential in Ar-saturated electrolyte, 6s/cycles), the passivation that occurred at high potential could cause a small portion of Pd to be “exposed” and adsorb CO (Figure 1, red solid line). After another 150 cycles of AST-Ar (AST 300), more Pd was exposed to CO adsorption but not fully. This proves the carbon-coating layers can protect the nanoparticles from poison contaminant gas such as NOx, SOx, CO, or the COx-like species. Both AFC and PEMFC systems can benefit from this type of catalyst to operate extensively without losing catalytic activity to passivation or pollution issues.