To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.展开更多
The electrical and thermal performances of a simulated 60 kW Proton Exchange Membrane Fuel Cell (PEMFC) cogeneration system are first analyzed and then strategies to make the system operation stable and efficient are ...The electrical and thermal performances of a simulated 60 kW Proton Exchange Membrane Fuel Cell (PEMFC) cogeneration system are first analyzed and then strategies to make the system operation stable and efficient are developed. The system configuration is described first, and then the power response and coordination strategy are presented on the basis of the electricity model. Two different thermal models are used to estimate the thermal performance of this cogeneration system, and heat management is discussed. Based on these system designs, the 60 kW PEMFC cogeneration system is analyzed in detail. The analysis results will be useful for further study and development of the system.展开更多
In this paper,a 60 kW proton exchange membrane fuel cell(PEMFC) generation system is modeled in order to design the system parameters and investigate the static and dynamic characteristics for control purposes.To achi...In this paper,a 60 kW proton exchange membrane fuel cell(PEMFC) generation system is modeled in order to design the system parameters and investigate the static and dynamic characteristics for control purposes.To achieve an overall system model,the system is divided into five modules:the PEMFC stack(anode and cathode flows,membrane hydration,and stack voltage and power),cathode air supply(air compressor,supply manifold,cooler,and humidifier),anode fuel supply(hydrogen valve and humidifier),cathode exhaust exit(exit manifold and water return),and power conditioning(DC/DC and DC/AC) modules.Using a combination of empirical and physical modeling techniques,the model is developed to set the operation conditions of current,temperature,and cathode and anode gas flows and pressures,which have major impacts on system performance.The current model is based on a 60 kW PEMFC power plant designed for residential applications and takes account of the electrochemical and thermal aspects of chemical reactions within the stack as well as flows of reactants across the system.The simulation tests show that the system model can represent the static and dynamic characteristics of a 60 kW PEMFC generation system,which is mathematically simple for system parameters and control designs.展开更多
The control objective and several key parameters of PEMFC hybrid system are analyzed. Control strategy design and energy optimization simulation are made individually for given cycle case and realtime operating case. ...The control objective and several key parameters of PEMFC hybrid system are analyzed. Control strategy design and energy optimization simulation are made individually for given cycle case and realtime operating case. For the given cycle case, genetic algorithm is adopted to solve the multi-constraint combinatorial optimization problem. Simulation result showed the algorithm＇s feasibility. As far as the realtime operation is concerned, based on the original fuzzy control strategy, the fuel cell voltage and voltage variance parameters are introduced to apply result reveals that the improved fuzzy control strategy can enhance the two-level modification on the fuzzy control output. The fuel cell efficiency and reduce the power fluctuations.展开更多
Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal leve...Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.展开更多
As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resist...As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resistance(ASR)via the on-board model is critical to monitor the health state of the automotive PEMFC stack.In this study,we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset,and a long short-term memory(LSTM)deep learning model is developed to predict the dynamic per-formance of PEMFC.The results show that the developed LSTM deep learning model has much better perfor-mance than other models.A sensitivity analysis on the input features is performed,and three insensitive features are removed,that could slightly improve the prediction accuracy and significantly reduce the data volume.The neural structure,sequence duration,and sampling frequency are optimized.We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s,and that for predicting output voltage is 40 s.The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz,which slightly affects the prediction accuracy,but obviously reduces the data volume and computation amount.展开更多
This paper presented a control design methodology for a proton exchange membrane fuel cell (PEMFC) generation system for residential applications. The dynamic behavior of the generation system is complex in such appli...This paper presented a control design methodology for a proton exchange membrane fuel cell (PEMFC) generation system for residential applications. The dynamic behavior of the generation system is complex in such applications. A comprehensive control design is very important for achieving a steady system operation and efficiency. The control strategy for a 60 kW generation system was proposed and tested based on the system dynamic model. A two-variable single neuron proportional-integral (PI) decoupling controller was developed for anode pressure and humidity by adjusting the hydrogen flow and water injection. A similar controller was developed for cathode pressure and humidity by adjusting the exhaust flow and water injection. The desired oxygen excess ratio was kept by a feedback controller based on the load current. An optimal seeking controller was used to trace the unique optimal power point. Two negative feedback controllers were used to provide AC power and a suitable voltage for residential loads by a power conditioning unit. Control simulation tests showed that 60 kW PEMFC generation system responded well for computer-simulated step changes in the load power demand. This control methodology for a 60 kW PEMFC generation system would be a competitive solution for system level designs such as parameter design, performance analysis, and online optimization.展开更多
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
基金Project (No. 2002AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘The electrical and thermal performances of a simulated 60 kW Proton Exchange Membrane Fuel Cell (PEMFC) cogeneration system are first analyzed and then strategies to make the system operation stable and efficient are developed. The system configuration is described first, and then the power response and coordination strategy are presented on the basis of the electricity model. Two different thermal models are used to estimate the thermal performance of this cogeneration system, and heat management is discussed. Based on these system designs, the 60 kW PEMFC cogeneration system is analyzed in detail. The analysis results will be useful for further study and development of the system.
基金Project supported by the National Natural Science Foundation of China (No.10472101)the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20070335184)
文摘In this paper,a 60 kW proton exchange membrane fuel cell(PEMFC) generation system is modeled in order to design the system parameters and investigate the static and dynamic characteristics for control purposes.To achieve an overall system model,the system is divided into five modules:the PEMFC stack(anode and cathode flows,membrane hydration,and stack voltage and power),cathode air supply(air compressor,supply manifold,cooler,and humidifier),anode fuel supply(hydrogen valve and humidifier),cathode exhaust exit(exit manifold and water return),and power conditioning(DC/DC and DC/AC) modules.Using a combination of empirical and physical modeling techniques,the model is developed to set the operation conditions of current,temperature,and cathode and anode gas flows and pressures,which have major impacts on system performance.The current model is based on a 60 kW PEMFC power plant designed for residential applications and takes account of the electrochemical and thermal aspects of chemical reactions within the stack as well as flows of reactants across the system.The simulation tests show that the system model can represent the static and dynamic characteristics of a 60 kW PEMFC generation system,which is mathematically simple for system parameters and control designs.
基金Project (No. 2003AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘The control objective and several key parameters of PEMFC hybrid system are analyzed. Control strategy design and energy optimization simulation are made individually for given cycle case and realtime operating case. For the given cycle case, genetic algorithm is adopted to solve the multi-constraint combinatorial optimization problem. Simulation result showed the algorithm＇s feasibility. As far as the realtime operation is concerned, based on the original fuzzy control strategy, the fuel cell voltage and voltage variance parameters are introduced to apply result reveals that the improved fuzzy control strategy can enhance the two-level modification on the fuzzy control output. The fuel cell efficiency and reduce the power fluctuations.
基金Project (No. 2002AA517020) supported by the Hi-Tech Research and Development Program (863) of China
文摘Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.
文摘为了同时优化质子交换膜燃料电池(proton exchange membrane fuel cells,PEMFC)系统的效率和输出功率,文章首先建立PEMFC系统的机理模型,并分析系统效率和输出功率特性;其次针对传统灰狼算法(grey wolf optimizer,GWO)的初始化种群不均匀和易出现早熟收敛的问题,引入佳点集种群初始化策略和非线性收敛因子策略,并由此提出一种改进多目标灰狼优化算法(multi-objective grey wolf optimizer,MOGWO),有效改善了灰狼算法的搜索精度和收敛性能;然后针对改进多目标灰狼优化算法求得的Pareto最优解集,使用TOPSIS评价法得出逼近理想解的最佳解,确定PEMFC系统的最佳运行条件;最后对所提出的MOGWO算法进行仿真验证,结果表明该算法能够有效提高PEMFC系统在实际运行中的输出功率和系统效率。
基金This research is supported by the National Natural Science Founda-tion of China(No.52176196)the National Key Research and Devel-opment Program of China(No.2022YFE0103100)+1 种基金the China Postdoctoral Science Foundation(No.2021TQ0235)the Hong Kong Scholars Program(No.XJ2021033).
文摘As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resistance(ASR)via the on-board model is critical to monitor the health state of the automotive PEMFC stack.In this study,we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset,and a long short-term memory(LSTM)deep learning model is developed to predict the dynamic per-formance of PEMFC.The results show that the developed LSTM deep learning model has much better perfor-mance than other models.A sensitivity analysis on the input features is performed,and three insensitive features are removed,that could slightly improve the prediction accuracy and significantly reduce the data volume.The neural structure,sequence duration,and sampling frequency are optimized.We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s,and that for predicting output voltage is 40 s.The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz,which slightly affects the prediction accuracy,but obviously reduces the data volume and computation amount.
基金Project supported by the Hi-Tech R&D Program (863) of China (No. 2002AA517020)the National Nature Science Foundation of China (No. 60804031)+1 种基金the Natural Science Foundation of Shandong Province (No. ZR2012BQ016)the Science and Technology Plan of Shandong Province (No. 2013GHY11521), China
文摘This paper presented a control design methodology for a proton exchange membrane fuel cell (PEMFC) generation system for residential applications. The dynamic behavior of the generation system is complex in such applications. A comprehensive control design is very important for achieving a steady system operation and efficiency. The control strategy for a 60 kW generation system was proposed and tested based on the system dynamic model. A two-variable single neuron proportional-integral (PI) decoupling controller was developed for anode pressure and humidity by adjusting the hydrogen flow and water injection. A similar controller was developed for cathode pressure and humidity by adjusting the exhaust flow and water injection. The desired oxygen excess ratio was kept by a feedback controller based on the load current. An optimal seeking controller was used to trace the unique optimal power point. Two negative feedback controllers were used to provide AC power and a suitable voltage for residential loads by a power conditioning unit. Control simulation tests showed that 60 kW PEMFC generation system responded well for computer-simulated step changes in the load power demand. This control methodology for a 60 kW PEMFC generation system would be a competitive solution for system level designs such as parameter design, performance analysis, and online optimization.
