Efficiency Of Proton Exchange Membrane Fuel Cell Research Articles

Multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state

This study proposes a multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state. By integrating rule-based control with multi-objective optimization methods, the strategy aims to improve system efficiency, extend the lifespan of proton exchange membrane fuel cells (PEMFC), and reduce operating costs. Based on a comprehensive model of PEMFC output characteristics and lifetime degradation, this study introduces an optimized point line (OPL) strategy. This strategy dynamically adjusts operating constraints according to the state of health (SOH) of the PEMFC, ensuring optimal vehicle performance throughout its lifecycle. To optimize the OPL strategy parameters, a particle swarm optimization algorithm with compression factor was employed, enhancing the strategy’s optimization efficiency, adaptability, and robustness to better handle various real-world operating conditions. The strategy was evaluated under US06 and WLTC driving cycles and compared with traditional power following (PF) and point line (PL) strategies. Results show that compared to the PL strategy, the OPL strategy achieved a 36.4% and 34.2% reduction in operating costs under US06 and WLTC cycles, respectively. Moreover, PEMFC lifetime degradation decreased by 44.9% and 39.4% in these cycles. In high-power regions, the average operating efficiency of PEMFC improved by 2%. The strategy demonstrated good adaptability to different driving conditions, providing an effective solution for optimizing the performance and durability of fuel cell hybrid electric vehicles.

A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked a lot of interest over the last few years due to their robust efficiency, low operating temperature, and longevity. The PEMFC's electrochemical model has seven unknown parameters, which are not given in the manufacturer's datasheets and need to be accurately estimated to present a more accurate model, leading to improved efficiency and performance of the PEMFC systems. The estimation of those unknown parameters has been dealt with as a complex and non-linear optimization problem that needs a powerful optimization algorithm to solve it. The existing optimization algorithms still have some disadvantages, such as falling into local minima and low convergence speed, which make them ineligible to solve this complicated problem with acceptable accuracy and low computational cost. Therefore, this study presents a new parameter estimation technique for estimating the unknown parameters of the PEMFC model more accurately, thereby achieving precise modeling of PEMFCs. This technique called IKOA is based on integrating the Kepler optimization algorithm (KOA) with a novel Lévy-Normal (LN) mechanism to strengthen its exploration and exploitation capabilities against this multimodal optimization problem. The Lévy flight in this mechanism aims to improve the KOA's exploitation operator to accelerate the convergence speed toward the near-optimal solution, thus minimizing the computational cost; meanwhile, the normal distribution is used to strengthen its exploration operator, thereby aiding in the escape of local minima. The proposed IKOA and KOA are herein evaluated against several rival algorithms using six well-known commercial PEMFC stacks to highlight their efficiency and effectiveness. Key performance metrics such as computational cost, fitness measures, and statistical validation through the Wilcoxon rank-sum test are herein used to highlight IKOA's effective performance in enhancing the predictive accuracy and operational efficiency of PEMFCs. The numerical findings show the high superiority of IKOA against all rival optimizers on all solved benchmarks.

Performance prediction and operating parameters optimization for proton exchange membrane fuel cell based on data-driven surrogate model and particle swarm optimization

The operating parameters of proton exchange membrane fuel cell (PEMFC) are critical to its performance and working life. This study presents a data-driven modeling approach combining a surrogate model with the particle swarm optimization algorithm to optimize operating parameters for PEMFC and obtain the maximum power density. The results show that the operating parameters significantly influence power density under high current densities, with inlet temperature having the most significant effect. Lower inlet temperature, relative humidity in the cathode and anode, along with higher operating pressure yield improved output performance. The Genetic Algorithm-Backpropagation Neural Network based surrogate model exhibits excellent predictive performance with correlation coefficients of 0.99896 and 0.99815 for the training and test sets, respectively. Optimized conditions achieve a 3.3% increase in power density compared to initial settings, with only a 0.15% error in simulation calculations. This data-driven approach provides valuable insights for maximizing PEMFC efficiency and performance.

Computational evaluation of PEMFC performance based on bipolar plate material types

Materials used in bipolar plates are essential for addressing the challenges associated with the large-scale commercialization of Proton Exchange Membrane Fuel Cells (PEMFCs). Therefore, it is necessary to develop effective performance evaluation approaches to evaluate the performance of materials. This work has presented a high-fidelity mathematical model that includes electrochemical, fluid flow, energy, species transport, water formation, and transport aspects to simulate the conditions in a PEMFC system and to theoretically assess the performances of commercial bipolar plate materials. Accordingly, the effects of metallic (aluminum) and carbon-based (graphite and graphite composite) bipolar plate materials on the current collection performance, thermal management, water management, and overall efficiency of PEMFCs are analyzed. The findings suggest that the current density, power density, and operating voltage of each PEMFC are greatly affected by the bipolar plate material used. When compared to graphite bipolar plates, the aluminum and graphite composite bipolar plates have exhibited 16.5 % and 7.5 % higher power densities, respectively. The change in operating conditions and thermal environments caused by a change of bipolar plate materials gives rise to distinct water transport phenomena according to their respective capabilities in the balancing of liquid water movements across the membrane, which in turn affects the species distribution pattern. Specifically, the high imbalance between the electro-osmotic drag and back diffusion in the case of aluminum bipolar plate leads to a severe membrane dry-out on the anode side, limiting the hydrogen concentration in the region.

Enhanced ORR Performance with Biomass-Derived Freestanding Catalyst Layers: Advancing Mass Transport in Gas Diffusion Electrodes

Proton Exchange Membrane Fuel Cells (PEMFCs) are increasingly recognized as a pivotal technology for transitioning the energy sector towards net-zero emissions. The efficiency of PEMFCs largely hinges on the development...

Modeling the Environment-Dependent Kinetics of Oxygen Reduction Reaction – a Continuum Model for Electric Double Layer

For proton-exchange-membrane fuel cells (PEMFCs) to achieve broad commercialization, improved energy-conversion efficiency with minimal Pt-based electrocatalyst is required [1]. Because the sluggish rate of oxygen reduction reaction (ORR) limits the efficiency of PEMFCs [2], the efficiency improvement requires a better understanding of ORR kinetics and mechanism to design better catalyst. To understand the ORR mechanism, theoretical and experimental analyses have been conducted. While previous studies reasonably explained the catalyst-dependent activity on single crystal catalysts in 0.1 M perchloric acid solution [3, 4], the explicit effect of electrolyte and related microenvironments [5, 6] is not thoroughly understood. The change in the electrolyte alters the electric-double-layer (EDL) structure and thus the local microenvironment at the electrode/electrolyte interface. Thus, the structure of the EDL should be carefully analyzed to uncover the electrolyte-dependent reaction kinetics. In this talk, we propose a multiscale continuum model to predict the EDL structure and examine the effect of perchloric acid concentration on ORR activity on Pt (111). The model includes Density Potential Functional Theory (DPFT) for electron density and Modified Poisson Boltzmann equation for species’ density and electric potential [7]. Also, the interaction between adsorbents and electric field is taken into account by minimizing the grand potential. After model validation with experimentally measured double-layer capacity data as a function of applied potential and concentration, the effect of the perchloric acid concentration (0.02 M – 0.2 M) on ORR activity is analyzed and discussed. It is shown that the model reproduces the specific activity obtained in the experiments when assuming the oxygen adsorption is limiting the rate, which can be attributed to the large energetic barrier for solvent reorganization [8]. Then, extension of the model to PEMFC ionomer electrolytes will be introduced. Overall, the model framework and findings provide insights into the ORR mechanism and guidance on how to tailor catalyst materials for increased PEMFC performance. Acknowledgements: This work was partially supported by the Hydrogen and Fuel Cell Technologies Office (HFTO), Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy (DOE) through the Million Mile Fuel Cell Truck (M2FCT) consortia, technology managers G. Kleen and D. Papageorgopoulos, under contract number DE-AC02- 05CH11231, as well as by a CRADA with Toyota Central R&D Labs., Inc.. Reference [1] D. A. Cullen, K. C. Neyerlin, R. K. Ahluwalia, R. Mukundan, K. L. More, R. L. Borup, A. Z. Weber, D. J. Myers and A. Kusoglu, Nature Energy, 6, 462 (2021).[2] I. E. L. Stephens, A. S. Bondarenko, U. Grønbjerg, J. Rossmeisl and I. Chorkendorff, Energy & Environmental Science, 5 (2012).[3] M. Shibata, M. Inaba, K. Shinozaki, K. Kodama and R. Jinnouchi, Journal of The Electrochemical Society, 169 (2022).[4] H. A. Hansen, V. Viswanathan and J. K. Nørskov, The Journal of Physical Chemistry C, 118, 6706 (2014).[5] K. Kodama, K. Motobayashi, A. Shinohara, N. Hasegawa, K. Kudo, R. Jinnouchi, M. Osawa and Y. Morimoto, ACS Catalysis, 8, 694 (2017).[6] M. Luo and M. T. M. Koper, Nature Catalysis, 5, 615 (2022).[7] J. Huang, J Chem Theory Comput, 19, 1003 (2023).[8] D. T. Limmer, A. P. Willard, P. Madden and D. Chandler, Proceedings of the National Academy of Sciences, 110, 4200 (2013).

Excellent anti-corrosion and conductivity of NbN coated on Ti bipolar plate by controlling N2 flow rates

The metal bipolar plates which play a crucial role in proton exchange membrane fuel cells (PEMFCs) are applied extensively due to their excellent property. However, the degraded corrosion resistance by corrosive ion and the increased interfacial contact resistance by passivation layer under acidic operating environment will affect the efficiency of PEMFCs. Metal nitride can solve that problem in some extent, however, the effect of nitrogen flow rates on the metal nitride formation during coating deposition is unclear. To solve these, NbN coatings with different N2 flow rates were applied to the bare Ti substrate via magnetron sputtering and thereafter studied. Compared with bare Ti substrate, outstanding anticorrosion performance is exhibited for the coated sample, whose corrosion current decreased three orders of magnitude (∼10−8 A/cm2) in simulated PEMFC cathode environment and the interfacial contact resistance decreased to tenth of that bare Ti substrate (∼5 mΩ cm2).

Improving water removal efficiency in a PEM fuel cell: Microstructured surfaces for controlling instability-driven pinching

Proton Exchange Membrane Fuel Cell (PEMFC) is a clean, sustainable energy generation device, and its large-scale usage is becoming popular due to green and secure energy demand worldwide. The performance, efficiency, and lifespan of PEMFC largely depend on the water removal and management within the cell. Under the influence of the cross-air flow, the generated water filaments deform, and as the filament radius lowers, the curvature and capillary pressure increase, ejecting fluid out of the neck at increasing velocities. The moment the filament radius vanishes, the governing equations reach the point of singularity, and the filament breaks. We propose an optimum micro-patterned surface design for efficient water removal from PEMFC. We perform a numerical study of water generation on the surface followed by breakup under shear flow within confinement. We further theoretically identify the breakup behavior with characterization, recognizing the influence of the microstructures toward an efficient design. The hydrophobic microstructures are observed to decrease the dominance of viscous force over inertia and capillary force. This leads to a greater propensity of end-pinching or truncation of the generated droplet at the neck, which reduces the production of undesired satellite droplets that would have otherwise caused flooding of the chamber. In this work, we show that a proper combination of substrate structure and jet velocity-induced shear can mitigate the generation of satellite droplets and reduce the breakup time, significantly increasing the water removal efficiency of the PEMFC.

Experimental study on flow boiling-based micro-channel configurations for the PEMFC cooling

Proton Exchange Membrane Fuel Cell (PEMFC) is one of the most promising energy conversion devices. Efficient thermal management is crucial for maintaining the PEMFC's efficiency and durability. However, cooling the PEMFC can be challenging due to the small temperature differences between the stack's operating temperature and the surrounding environment. In the present study, flow boiling is applied as a cooling technique for the PEMFC. A flow boiling experimental test bench with fluid flow visualization is built for PEMFC cooling. To relieve the negative effect of mal-distribution of working fluid in the micro-channels, the cooling configurations with orifice baffles are also tested. Flow boiling performance of HFE-7100 in the micro-channel-based cooling configuration for PEMFC is experimentally investigated. The flow boiling characteristics of HFE-7100 in the micro-channel are revealed, which is beneficial for the appropriate design of flow boiling-based cooling configuration for the PEMFC. Excellent temperature distribution uniformity of flow boiling-based cooling PEMFC is achieved and is further enhanced by adding orifice baffles.

Design of Radial Flow Channel Proton Exchange Membrane Fuel Cell Based on Topology Optimization

In this paper, the flow channel of the radial proton exchange membrane fuel cell (PEMFC) is optimized by the topological optimization method. Using the SNOPT algorithm, a two-dimensional stable constant temperature model is freely constructed in the cyclic sector design domain. Topology optimization aims to maximize the efficiency of PEMFC and minimize the energy dissipation of reaction gas. We analyze radial topology flow channels’ mass transfer capacity and cell performance with different maximum volume constraints. The results show that under high current density, the performance of the optimized channel is significantly better than that of the traditional channel. Increasing the maximum volume constraint is beneficial for improving the mass transfer of PEMFC. At 0.6 V, the cell performance of Scheme 4 is 14.9% higher than the serpentine flow channel and 9.5% higher than the parallel flow channel. In addition, in the optimal selection, 3D simulation modeling is carried out for more accurate verification.

Design and Analysis of Gas Diffusion Layers in a Proton Exchange Membrane Fuel Cell

A proton exchange membrane fuel cell is an energy convertor that produces environmentally friendly electrical energy by oxidation of hydrogen, with water and heat being byproducts. This study investigates the gas diffusion layer (GDL) of the membrane electrode assembly (MEA) in proton exchange membrane fuel cells (PEMFCs). In this regard, the key design concerns and restraints of the GDL have been assessed, accompanied by an inclusive evaluation of the presently existing models. In addition, the common materials used for the GDL have been explored, evaluating their properties. Moreover, a case study of step-by-step modeling for an optimal GDL has been presented. An experimental test has been carried out on a single cell under various compressions. Lastly, a parametric study has been performed considering many design parameters, such as porosity, permeability, geometrical sizes, and compression of the GDL to improve the overall efficiency of the fuel cell. The results are presented in this paper in order to help ongoing efforts to improve the efficiency of PEMFCs and facilitate their development further.

Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance

• A TSA-based methodology is established to optimize the PEMFC operating parameters for maximizing the efficiency. • Two optimization scenarios are presented to identify the parameters that most affect the PEMFCs stack efficiency. • The obtained accuracy of the TSA is proved against other competitors. • A neuro-fuzzy controller is designed to generate the operating parameters maintaining maximum efficiency irrespective the loading conditions. • The stack’s efficiency is improved at different loading conditions. For example, at 30% loading, the PEMFCs efficiency is improved from 16.27% to 63.47% at 60 °C and from 18.22% to 65.26% at 100 °C. Recently, world endeavors are focused on promoting energy savings by operating both sources and loads at their maximum efficiency points. Thus, this paper presents a novel attempt to optimally determine the operating parameters of an isolated system comprising the proton exchange membrane fuel cells (PEMFCs) stack serving a variable load. A fitness function is adapted to maximize the PEMFCs stack’s efficiency using tuna swarm algorithm (TSA), subjected to set of inequality constraints. A well-known commercial type of PEMFCs stack namely Nedstack PS6 6 kW, are carefully studied over two TSA-based optimization scenarios. The first scenario aims at optimizing five operating parameters, while only two operating parameters are optimized in the second one. Numerical comparisons among the two scenarios are made. It’s worth indicating that the maximum absolute efficiency deviation between both scenarios is equal to 0.8064 at 100 °C. Moreover, statistical tests are executed to appraise the performance of the TSA and others. At later stage, the TSA-based results are employed to train and learn an adaptive neuro-fuzzy controller for extracting the optimal operating parameters over wider range of loading conditions, while keeping the goal of maximum efficiency point in order. This allows predicting the optimal values of the operating parameters according to a certain load with a very low time burden, making it able to simulate the real-time load variations effectively and accurately. It can be reported here at low loading values as actual results for example, at 30% loading condition, the stack’s efficiency is improved from 16.27% to 63.47% at 60 °C, from 17.24% to 64.24% at 80 °C and from 18.22% to 65.26% at 100 °C. While, at load power of 40%, the FC’s efficiency is enhanced from 21.65% to 62.72% at 60 °C, from 22.95% to 63.60% at 80 °C and from 24.25% to 64.76% at 100 °C. It may be established that via this proposed synergy between TSA and neuro-fuzzy controller, the efficiency of PEMFCs can be maximized.

An open-cathode PEMFC efficiency optimization strategy based on exergy analysis and data-driven modeling

In open-cathode proton exchange membrane fuel cell (PEMFC) stacks, the fan is responsible for the air supply on the cathode side of the PEMFC and the heat dissipation of the stack, which is the main source of parasitic power in the stack system and affects the energy utilization efficiency of the stack. Previous studies on the efficiency of open-cathode PEMFCs have rarely considered that the energy itself is also qualitatively different, and the efficiency calculated based on exergy analysis can better reflect the energy utilization of the stack. In this paper, an exergy analysis-based and data-driven PEMFC model is developed and an optimization strategy to solve the constrained planning problem is designed to obtain the optimal exergy efficiency and fan power values at different temperatures. The optimization results show that the maximum improvement of exergy efficiency is 15.40%, 22.65%, and 27.62% for three different load currents, respectively. The proposed combined optimization strategy is expected to provide insight into the control and optimization of PEMFC systems.

Design and Analysis of Spider Bionic Flow Field for Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) is a portable and clean power generation device. The structural arrangement of the flow field has a significant influence on the delivery efficiency of PEMFC. In this article, a new bionic flow channel is designed based on the inspiration of a spider shape. The branch channel width and branch corner are studied as the focus, and its simulation is carried out by the method of computational fluid dynamics (CFD). The results show that when channel width/rib width and corner of the branch are 1.5 and 130°, respectively, it is the best numerical combination and the cell comprehensive performance is excellent. The final model using this numerical combination is compared with the traditional flow channel model to verify the advancement of this scheme.

Research Progress, Trends, and Current State of Development on PEMFC-New Insights from a Bibliometric Analysis and Characteristics of Two Decades of Research Output.

The consumption of hydrogen could increase by sixfold in 2050 compared to 2020 levels, reaching about 530 Mt. Against this backdrop, the proton exchange membrane fuel cell (PEMFC) has been a major research area in the field of energy engineering. Several reviews have been provided in the existing corpus of literature on PEMFC, but questions related to their evolutionary nuances and research hotspots remain largely unanswered. To fill this gap, the current review uses bibliometric analysis to analyze PEMFC articles indexed in the Scopus database that were published between 2000-2021. It has been revealed that the research field is growing at an annual average growth rate of 19.35%, with publications from 2016 to 2012 alone making up 46% of the total articles available since 2000. As the two most energy-consuming economies in the world, the contributions made towards the progress of PEMFC research have largely been from China and the US. From the research trend found in this investigation, it is clear that the focus of the researchers in the field has largely been to improve the performance and efficiency of PEMFC and its components, which is evident from dominating keywords or phrases such as 'oxygen reduction reaction', 'electrocatalysis', 'proton exchange membrane', 'gas diffusion layer', 'water management', 'polybenzimidazole', 'durability', and 'bipolar plate'. We anticipate that the provision of the research themes that have emerged in the PEMFC field in the last two decades from the scientific mapping technique will guide existing and prospective researchers in the field going forward.

Insights into PEMFC Water Management with Different Layer Properties

Hydrogen based proton-exchange-membrane fuel cells (PEMFCs) provide a sustainable, potentially decarbonized solution to meet the future energy needs for mobility applications especially heavy-duty vehicles. Water management plays a critical role in determining the performance and efficiency of PEMFCs.1 Low water content in the membrane-electrode assembly (MEA) can lead to membrane dehydration and subsequently increased ohmic overpotentials, while high water content can result in local flooding of the porous media, thereby leading to mass-transport limitations. The structure and properties of the porous media and membrane govern the water transport in the MEA and thus, parameterizing the effect of these properties on the water balance in the MEA is important for the design of next generation materials, a task that mathematical modeling is ideally suited to tackle.In this work, a 2D half-land half-channel MEA model,2 modified to explicitly include gas crossover, is used to study the effect of different layer properties on PEMFC performance. The model is used to examine both impact of membrane thickness in traditional PEMFCs, as well as water management with thicker electrodes and asymmetric diffusion media with non-platinum-group-metal PEMFCs. For the former, the tradeoffs between crossover and water gradients versus ohmic losses is quantified including explicit membrane-thickness-dependent properties.3 For the latter, specific cases of diffusion media with different pore size distributions and permeabilities is explored. Water-balance and voltage-breakdown analysis elucidate the design parameters limiting the PEMFC performance. References A. Z. Weber et al., Journal of The Electrochemical Society, 161, F1254–F1299 (2014).L. M. Pant, S. Stewart, N. Craig, and A. Z. Weber, Journal of The Electrochemical Society, 168, 074501 (2021).X. Luo et al., Journal of The Electrochemical Society, 168, 104517 (2021).

Sparrow search algorithm applied to temperature control in PEM fuel cell systems

Working temperature is a critical parameter that influences the performance of proton exchange membrane fuel cells (PEMFCs). Appropriate thermal management can improve the efficiency of PEMFCs and prevent irreversible internal damage such as membrane degradation. To solve the slow response and poor dynamic performance of traditional temperature control strategies, the dynamic temperature mode of PEMFCs is established and a temperature control strategy based on the sparrow search algorithm-proportional integral differential (SSA-PID) is proposed in this study. The performance of the SSA-PID temperature control is verified by the simulations of current step change, vehicle dynamic load, and working parameter variation. The results show that the proposed method has the advantages of fast convergence, better dynamic performance, and anti-disturbance ability. The maximum power density of the SSA-PID temperature controller is 6.58% and 12.94% higher than GA (genetic algorithm)-PID and traditional PID controllers, respectively. Moreover, the proposed method can ensure temperature fluctuations within 0.5 K under dynamic disturbance.

Design and evaluation of adaptive neural fuzzy-based pressure control for PEM fuel cell system

In the proton-exchange membrane fuel cell (PEMFC), the partial pressure equalization between hydrogen and oxygen is one of the most significant problems influencing PEMFC efficiency and lifetime. In this paper, an adaptive neural fuzzy inference system (ANFIS)-based pressure tracking controller for a PEMFC system is investigated under the variation of load current. The suggested controller is designed based on the dynamic model to enhance the efficiency and prolong the stack life of the PEMFC system. First, the pressure control system model is established on the premise of the electrochemical and react flow model. Next, the ANFIS controller is developed to further improve the transient performance and minimize the partial pressures of hydrogen and oxygen. Finally, the simulation and experimental results are provided to demonstrate the effectiveness of the suggested method. Compared to the super-twisting sliding mode controller (STSMC), the proposed ANFIS controller has increased the tracking performance by 89.5% in simulation and 72.06% in the experiment under the multi-step load current.

The influence of degradation effects in proton exchange membrane fuel cells on life cycle assessment modelling and environmental impact indicators

Although proton exchange membrane fuel cell (PEMFC) systems are expected to have lower environmental impacts in the operational phase, compared to conventional energy conversion systems, there are still certain economic, operational, and environmental setbacks. Durability under a wide range of operating conditions presents a challenge because degradation processes affect the PEMFC efficiency. Typically, life cycle assessment (LCA) of PEMFC systems do not include performance degradation. Thus, a novel semi-empirical PEMFC model is developed, which includes degradation effects caused by different operational regimes (dynamic and steady-state). The model is integrated into LCA through life cycle inventory (LCI) to achieve a more realistic and accurate evaluation of environmental impacts. Verification of the model clearly showed that the use of existing LCI models underestimates the environmental impacts. This is especially evident when green hydrogen is used in PEMFC operational phase, where manufacturing phase and maintenance (stack replacements) become more influential. Input parameters of the model can be modified to reflect technological improvements (e.g. platinum loading or durability) and evaluate the effects of future scenarios.

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Maximum power point tracking (MPPT) is an effective method to improve the power generation efficiency and power supply quality of a proton exchange membrane fuel cell (PEMFC). Due to the inherent nonlinear characteristics of PEMFC, conventional MPPT methods are often difficult to achieve a satisfactory control effect. Considering this, artificial bee colony algorithm combining fuzzy control (ABC-fuzzy) was proposed to construct a MPPT control scheme for PEMFC. The global optimization ability of ABC algorithm was used to approach the maximum power point of PEMFC and solve the problem of falling into local optimization, and fuzzy control was used to eliminate the problems of large overshoot and slow convergence speed of ABC algorithm. The testing results show that compared with perturb & observe algorithm, conductance increment and ABC methods, ABC-fuzzy method can make PEMFC obtain greater output power, faster regulation speed, smaller steady-state error, less oscillation and stronger anti-interference ability. The MPPT scheme based on ABC-fuzzy can effectively realize the maximum power output of PEMFC, and plays an important role in improving the service life and power supply efficiency of PEMFC.