To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network’s ability of id...To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network’s ability of identifying complex nonlinear systems, a neural network identification model of MCFC stack is developed based on the sampled input-output data. Also, a novel online fuzzy control procedure for the temperature of MCFC stack is developed based on the fuzzy genetic algorithm (FGA). Parameters and rules of the fuzzy controller are optimized. With the neural network identification model, simulation of MCFC stack control is carried out. Validity of the model and the superior performance of the fuzzy controller are demonstrated.展开更多
A kW-scale molten carbonate fuel cells stack was developed and 800-hours’ operating test and performance experimental research had been done. Utilizing domestic materials completely, we developed NiO cathode and Ni-A...A kW-scale molten carbonate fuel cells stack was developed and 800-hours’ operating test and performance experimental research had been done. Utilizing domestic materials completely, we developed NiO cathode and Ni-Al anode with the active area of 336cm 2 and Υ-LiAlO 2 electrolyte tile and bipolar plate with the area of 900cm 2. The stack was composed of thirty cells, with 62%Li 2CO 3+38%K 2CO 3 as its electrolyte. During the 800 hours’ continuous operating, the performance of the stack was stable. With 99.7%(mole fraction) H 2 as fuel and O 2 from air as oxidant, the average operating voltage of a cell was about 0.72 V. The maximal current density attained to 165mA/cm 2, and the maximal output power attained to 1080 Watt. The whole performance of the stack approached to the international level in the early 90’s. This paper gives the main works and experiments results.展开更多
The paper is a summary of researches on molten carbonate fuel cell. On the same time, several key technology difficulties are discussed. Combining with our recent studies, the accessements to these problems are given...The paper is a summary of researches on molten carbonate fuel cell. On the same time, several key technology difficulties are discussed. Combining with our recent studies, the accessements to these problems are given out and they will be references for future works.展开更多
A combined system model is proposed including a molten carbonate fuel cell(MCFC),a graphene thermionic converter(GTIC)and thermally regenerative electrochemical cycles(TRECs).The expressions for power output,energy ef...A combined system model is proposed including a molten carbonate fuel cell(MCFC),a graphene thermionic converter(GTIC)and thermally regenerative electrochemical cycles(TRECs).The expressions for power output,energy efficiency of the subsystems and the couple system are formulated by considering several irreversible losses.Energy conservation equations between the subsystems are achieved leaned on the first law of thermodynamics.The optimum operating ranges for the combined system are determined compared with the MCFC system.Results reveal that the peak power output density(POD)and the corresponding energy efficiency are 28.22%and 10.76%higher than that of the single MCFC system,respectively.The effects of five designing parameters on the power density and energy efficiency of the MCFC/GTIC/TRECs model are also investigated and discussed.展开更多
This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be co...This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.展开更多
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was t...This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.展开更多
Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial...Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.展开更多
文摘To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network’s ability of identifying complex nonlinear systems, a neural network identification model of MCFC stack is developed based on the sampled input-output data. Also, a novel online fuzzy control procedure for the temperature of MCFC stack is developed based on the fuzzy genetic algorithm (FGA). Parameters and rules of the fuzzy controller are optimized. With the neural network identification model, simulation of MCFC stack control is carried out. Validity of the model and the superior performance of the fuzzy controller are demonstrated.
基金ShanghaiScienceandTechnologyDevelopmentFunds (No .9930 12 0 13),andtheNational985ScientificProjectDevelopmentFundsandpartoffundsupportofShanghaiElectricalGroups
文摘A kW-scale molten carbonate fuel cells stack was developed and 800-hours’ operating test and performance experimental research had been done. Utilizing domestic materials completely, we developed NiO cathode and Ni-Al anode with the active area of 336cm 2 and Υ-LiAlO 2 electrolyte tile and bipolar plate with the area of 900cm 2. The stack was composed of thirty cells, with 62%Li 2CO 3+38%K 2CO 3 as its electrolyte. During the 800 hours’ continuous operating, the performance of the stack was stable. With 99.7%(mole fraction) H 2 as fuel and O 2 from air as oxidant, the average operating voltage of a cell was about 0.72 V. The maximal current density attained to 165mA/cm 2, and the maximal output power attained to 1080 Watt. The whole performance of the stack approached to the international level in the early 90’s. This paper gives the main works and experiments results.
文摘The paper is a summary of researches on molten carbonate fuel cell. On the same time, several key technology difficulties are discussed. Combining with our recent studies, the accessements to these problems are given out and they will be references for future works.
文摘A combined system model is proposed including a molten carbonate fuel cell(MCFC),a graphene thermionic converter(GTIC)and thermally regenerative electrochemical cycles(TRECs).The expressions for power output,energy efficiency of the subsystems and the couple system are formulated by considering several irreversible losses.Energy conservation equations between the subsystems are achieved leaned on the first law of thermodynamics.The optimum operating ranges for the combined system are determined compared with the MCFC system.Results reveal that the peak power output density(POD)and the corresponding energy efficiency are 28.22%and 10.76%higher than that of the single MCFC system,respectively.The effects of five designing parameters on the power density and energy efficiency of the MCFC/GTIC/TRECs model are also investigated and discussed.
基金Project (No. 2003 AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.
基金The National High Technology Research and Development Program of China (863 Program) (No.2003AA517020)
文摘This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
文摘Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.
