质子交换膜燃料电池系统电气输出的动态控制

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.     

New hybrid model of proton exchange membrane fuel cell

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.     

质子交换膜燃料电池水管理的建模和分析

质子交换膜燃料电池的水管理是影响其性能的重要因素之一。电池水管理的目的就是要实现尽可能高的膜的水合程度,降低膜的阻抗。为了更好实现以上目标,文中建立了电池水传输模型,基于模型利用工程逼近分析方法,分析了阴阳极湿度、反应气体流量对膜的水含量和阴阳极水分压的影响。仿真结果通过与其他模型相比较,取得了一致的结果,因此也证明了该模型的有效和实用性。基于以上的分析结果为建立简化的膜水含量控制模型和实现水管理的控制目标奠定基础。     

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell （PEMFC） generator. This work researched the anode system ofa 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.     

基于参数自调整模糊神经网络的直接甲醇燃料电池电特性分析

This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.     

MCFC燃料电池的非线性建模及基于FGA的模糊控制

1Introduction Moltencarbonatefuelcell(MCFC)isaclearelec tricitygeneratingtechniquewithhighefficiency,which istobeusedwidely.Withoutcombustion,MCFCcon vertschemicalenergycontainedinfuelandoxidantin toelectricenergyviaelectro chemicalreaction.Per formanceof…     

PEMFC分散发电系统的压力控制研究

NomenclatureA-area ( m2)Dw-membrane water diffusivity ( m2/s)F-=96 487I-current (A)M-molecule mass (kg/mol)T-temperature (K)W-mass flowrate (kg/s)cw-water concentration in membrane ( mol/m3)m-mass (kg)n-cell numbernd-electro-osmotic drag coefficientp-pres…     

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ＇standard neural network model＇ （SNNM） is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities （LMIs）, which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks （including the chaotic neural network） and nonlinear systems modeled by neural networks or Takagi and Sugeno （T-S） fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.