Proton exchange membrane fuel cells(PEMFCs)are largely used in various applications because of their pollution-free products and high energy conversion efficiency.In order to improve the related design,in the present ...Proton exchange membrane fuel cells(PEMFCs)are largely used in various applications because of their pollution-free products and high energy conversion efficiency.In order to improve the related design,in the present work a new spiral flow field with a bypass is proposed.The reaction gas enters the flow field in the central path and diffuses in two directions through the flow channel and the bypass.The bypasses are arranged incrementally.The number of bypasses and the cross-section size of the bypasses are varied parametrically while a single-cell model of the PEMFC is used.The influence of the concentration of liquid water and oxygen in the cell on the performance of different flow fields is determined by means of Computational fluid dynamics(COMSOL Multiphysics software).Results show that when the bypass number is 48 and its cross-sectional area is 0.5 mm^(2),the cell exhibits the best performances.展开更多
Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidificat...Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.展开更多
Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel ce...Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel cell(PEMFC) vehicles. Presently, platinum metal accounts for ~42% of the total cost of the PEMFC vehicles for usage in the cathode catalyst layer, where the sluggish oxygen reduction reaction(ORR) occurs. An alternative to the platinum catalyst, the Fe-N-C catalyst has attracted considerable interest for PEMFC due to its cost-effectiveness and high catalytic activity towards ORR. However, the excellent ORR activity of Fe-N-C obtained from RDE studies rarely translates the same performance into MEA operating conditions. Such a performance gap is mainly attributed to the lack of atomic-level understanding of Fe-N-C active sites and their ORR mechanism. Besides, unless the cost of expensive electrocatalyst is reduced, the total operation cost of the PEMFC vehicles remains constant. Therefore,developing highly efficient Fe-N-C catalysts from academic and industrial perspectives is critical for commercializing PEMFC vehicles. Here, the scope of the review is three-fold. First, we discussed the atomiclevel insights of Fe-N-C active sites and ORR mechanism, followed by unraveling the different iron-based nanostructured ORR electrocatalysts, including oxide, carbide, nitride, phosphide, sulfide, and singleatom catalysts. And then we bridged their ORR catalytic performance gap between the RDE and MEA tests for real operating conditions of PEMFC vehicles. Second, we focused on bridging the cost barriers of PEMFC vehicles between capital, operation, and end-user. Finally, we provided the path to achieve sustainable development goals by commercializing PEMFC vehicles for a better world.展开更多
To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） s...To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.展开更多
One-dimensional(1D)Pt-based electrocatalysts demonstrate outstanding catalytic activities and stability toward the oxygen reduction reaction(ORR).Advances in three-dimensional(3D)ordered electrodes based on 1D Pt-base...One-dimensional(1D)Pt-based electrocatalysts demonstrate outstanding catalytic activities and stability toward the oxygen reduction reaction(ORR).Advances in three-dimensional(3D)ordered electrodes based on 1D Pt-based nanostructure arrays have revealed great potential for developing highperformance proton exchange membrane fuel cells(PEMFCs),in particular for addressing the mass transfer and durability challenges of Pt/C nanoparticle electrodes.This paper reviews recent progress in the field,with a focus on the 3D ordered electrodes based on self-standing Pt nanowire arrays.Nanostructured thin-film(NSTF)catalysts are discussed along with electrodes made from Pt-based nanoparticles deposited on arrays of polymer nanowires,and carbon and TiO2 nanotubes.Achievements on electrodes from Pt-based nanotube arrays are also reviewed.The importance of size,surface properties,and the distribution control of 1D catalyst nanostructures is indicated.Finally,challenges and future development opportunities are addressed regarding increasing electrochemical surface area(ECSA)and quantifying oxygen mass transport resistance for 1D nanostructure array electrodes.展开更多
This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve t...This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve to predict the voltage. The system control scheme of the voltage and power is introduced. The corresponding schemes for voltage and power control are studied. MATLAB is used to simulate the control system. The results reveal that the adopted control schemes can produce expected effects. Corresponding anti-disturbance and robustness simulation are also carried out. The simulation results show that the implemented control schemes have better robustness and adaptability.展开更多
Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and ...Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.展开更多
Ultrasound is now a widely used method for catalyst synthesis, catalyst support treatment, catalyst layer fabrication, membrane electrode assembly (MEA) fabrication, and humidifier etc. for fuel cell applications. Amo...Ultrasound is now a widely used method for catalyst synthesis, catalyst support treatment, catalyst layer fabrication, membrane electrode assembly (MEA) fabrication, and humidifier etc. for fuel cell applications. Among the abovementioned uses, ultrasonic technology has been utilised mainly for MEA fabrication—especially since it has demonstrated the capability to produce ultralow platinum loadings. This paper reports the power density and cathode mass power density at peak power and 500 mA/cm2 conditions for ultrasonically spray coated MEAs. These MEAs were also produced with various Nafion content ratios and platinum loadings. The results indicate varying optimum values for different conditions.展开更多
The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance d...The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.展开更多
基金Thanks to Major Scientific and Technological Innovation Projects in Shandong Province(2018-CXGC0803)for the financial support of this article.
文摘Proton exchange membrane fuel cells(PEMFCs)are largely used in various applications because of their pollution-free products and high energy conversion efficiency.In order to improve the related design,in the present work a new spiral flow field with a bypass is proposed.The reaction gas enters the flow field in the central path and diffuses in two directions through the flow channel and the bypass.The bypasses are arranged incrementally.The number of bypasses and the cross-section size of the bypasses are varied parametrically while a single-cell model of the PEMFC is used.The influence of the concentration of liquid water and oxygen in the cell on the performance of different flow fields is determined by means of Computational fluid dynamics(COMSOL Multiphysics software).Results show that when the bypass number is 48 and its cross-sectional area is 0.5 mm^(2),the cell exhibits the best performances.
基金Project (No. 2003AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.
基金the financial support from the National Natural Science Foundations of China (21374008)the Beijing Forbidden City Scholarship (2018420021)。
文摘Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel cell(PEMFC) vehicles. Presently, platinum metal accounts for ~42% of the total cost of the PEMFC vehicles for usage in the cathode catalyst layer, where the sluggish oxygen reduction reaction(ORR) occurs. An alternative to the platinum catalyst, the Fe-N-C catalyst has attracted considerable interest for PEMFC due to its cost-effectiveness and high catalytic activity towards ORR. However, the excellent ORR activity of Fe-N-C obtained from RDE studies rarely translates the same performance into MEA operating conditions. Such a performance gap is mainly attributed to the lack of atomic-level understanding of Fe-N-C active sites and their ORR mechanism. Besides, unless the cost of expensive electrocatalyst is reduced, the total operation cost of the PEMFC vehicles remains constant. Therefore,developing highly efficient Fe-N-C catalysts from academic and industrial perspectives is critical for commercializing PEMFC vehicles. Here, the scope of the review is three-fold. First, we discussed the atomiclevel insights of Fe-N-C active sites and ORR mechanism, followed by unraveling the different iron-based nanostructured ORR electrocatalysts, including oxide, carbide, nitride, phosphide, sulfide, and singleatom catalysts. And then we bridged their ORR catalytic performance gap between the RDE and MEA tests for real operating conditions of PEMFC vehicles. Second, we focused on bridging the cost barriers of PEMFC vehicles between capital, operation, and end-user. Finally, we provided the path to achieve sustainable development goals by commercializing PEMFC vehicles for a better world.
基金Project supported by the National Natural Science Foundation of China (Grant No.20576071)the Natural Science Foundation of Shanghai Municipality (Grant No.08ZR1409800)
文摘To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.
基金The author would like to acknowledge the support from the Engineering and Physical Sciences Research Council(EPSRC)(EP/L015749/1).
文摘One-dimensional(1D)Pt-based electrocatalysts demonstrate outstanding catalytic activities and stability toward the oxygen reduction reaction(ORR).Advances in three-dimensional(3D)ordered electrodes based on 1D Pt-based nanostructure arrays have revealed great potential for developing highperformance proton exchange membrane fuel cells(PEMFCs),in particular for addressing the mass transfer and durability challenges of Pt/C nanoparticle electrodes.This paper reviews recent progress in the field,with a focus on the 3D ordered electrodes based on self-standing Pt nanowire arrays.Nanostructured thin-film(NSTF)catalysts are discussed along with electrodes made from Pt-based nanoparticles deposited on arrays of polymer nanowires,and carbon and TiO2 nanotubes.Achievements on electrodes from Pt-based nanotube arrays are also reviewed.The importance of size,surface properties,and the distribution control of 1D catalyst nanostructures is indicated.Finally,challenges and future development opportunities are addressed regarding increasing electrochemical surface area(ECSA)and quantifying oxygen mass transport resistance for 1D nanostructure array electrodes.
文摘This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve to predict the voltage. The system control scheme of the voltage and power is introduced. The corresponding schemes for voltage and power control are studied. MATLAB is used to simulate the control system. The results reveal that the adopted control schemes can produce expected effects. Corresponding anti-disturbance and robustness simulation are also carried out. The simulation results show that the implemented control schemes have better robustness and adaptability.
基金Project (No. 2003AA517020) supported by the National Hi-TechResearch and Development Program (863) of China
文摘Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.
文摘Ultrasound is now a widely used method for catalyst synthesis, catalyst support treatment, catalyst layer fabrication, membrane electrode assembly (MEA) fabrication, and humidifier etc. for fuel cell applications. Among the abovementioned uses, ultrasonic technology has been utilised mainly for MEA fabrication—especially since it has demonstrated the capability to produce ultralow platinum loadings. This paper reports the power density and cathode mass power density at peak power and 500 mA/cm2 conditions for ultrasonically spray coated MEAs. These MEAs were also produced with various Nafion content ratios and platinum loadings. The results indicate varying optimum values for different conditions.
基金supported in part by the Research on Key Technologies of Low Temperature and Long Life Fuel Cells,under Grant 20220301010Gxin part by Qingdao postdoctoral support project under Grant QDBSH20220202020Qingdao Natural Science Foundation under Grant 23-2-1-110-zyyd-jch,Shandong Natural Science Foundation under Grants ZR2023QE208.
文摘The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.
