Numerical simulation of a direct internal reforming solid oxide fuel cell using computational fluid dynamics methodas

A detailed mathematical model of a direct internal reforming solid oxide fuel cell (DIR-SOFC) incorporating with simulation of chemical and physical processes in the fuel cell is presented. The model is developed based on the reforming and electrochemical reaction mechanisms, mass and energy conservation, and heat transfer. A computational fluid dynamics (CFD) method is used for solving the complicated multiple partial differential equations (PDEs) to obtain the numerical approximations.The resulting distributions of chemical species concentrations, temperature and current density in a cross-flow DIR-SOFC are given and analyzed in detail. Further, the influence between distributions of chemical species concentrations, temperature and current density during the simulation is illustrated and discussed. The heat and mass transfer, and the kinetics of reforming and electrochemical reactions have significant effects on the parameter distributions within the cell. The results show the particularchar acteristics of the DIR-SOFC among fuel cells, and can aid in stack design and control.     

Numerical simulation of a direct internal reforming solid oxide fuel cell using computational fluid dynamics method

A detailed mathematical model of a direct internal reforming solid oxide fuel cell （DIR-SOFC） incorporating with simulation of chemical and physical processes in the fuel cell is presented. The model is developed based on the reforming and electrochemical reaction mechanisms, mass and energy conservation, and heat transfer. A computational fluid dynamics （CFD） method is used for solving the complicated multiple partial differential equations （PDEs） to obtain the numerical approximations. The resulting distributions of chemical species concentrations, temperature and current density in a cross-flow DIR-SOFC are given and analyzed in detail. Further, the influence between distributions of chemical species concentrations, temperature and current density during the simulation is illustrated and discussed. The heat and mass transfer, and the kinetics of reforming and electrochemical reactions have significant effects on the parameter distributions within the cell. The results show the particular characteristics of the DIR-SOFC among fuel cells, and can aid in stack design and control.     

Iterative learning control of SOFC based on ARX identification model

This paper presents an application of iterative learning control (ILC) technique to the voltage control of solid oxide fuel cell (SOFC) stack. To meet the demands of the control system design, an autoregressive model with exogenous input (ARX) is established. Firstly, by regulating the variation of the hydrogen flow rate proportional to that of the current, the fuel utilization of the SOFC is kept within its admissible range. Then, based on the ARX model, three kinds of ILC controllers, i.e. P-, PI- and PD-type are designed to keep the voltage at a desired level. Simulation results demonstrate the potential of the ARX model applied to the control of the SOFC, and prove the excellence of the ILC controllers for the voltage control of the SOFC.     

SOFC temperature evaluation based on an adaptive fuzzy controller

The operating temperature of a solid oxide fuel cell （SOFC） stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative （PID） control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.     

固体氧化物燃料电池的研究进展

文章介绍了几种主要燃料电池的发展和研究现状、固体氧化物燃料电池(SOFC)的工作原理和特点,综述了SOFC的主要组件(阴极、阳极、电解质材料)制备方法及其进展,对SOFC在能源开发利用与市场化的前景进行了展望。     

Nonlinear modelling of a SOFC stack by improved neural networks identification

The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.     

基于遗传算法优化最小二乘支持向量回归机的平板型固体氧化物燃料电池的控制相关动态辨识建模

研究目的：为了同时预测固体氧化物燃料电池（SOFC）的电压、温度动态特性和设计控制器,建立SOFC的控制相关动态辨识模型。创新要点：为了建立SOFC更精确的最小二乘支持向量回归机（LSSVR）动态模型,采用遗传算法（GA）优化LSSVR的参数。所建GA-LSSVR模型可同时预测SOFC的电压和温度动态特性。研究方法：1.分析SOFC的电化学和能量平衡子模型。2.利用所选择的最优LSSVR参数,建立了SOFC的GA-LSSVR动态辨识模型。通过仿真分析和比较,验证了所建模型的有效性（图3和4）。3.利用所建模型的预测结果,与模拟退火算法优化最小二乘支持向量回归机（SAA-LSSVR）和5折交叉验证最小二乘支持向量回归机（5FCV-LSSVR）模型的预测结果进行了比较,表明所建立的GA-LSSVR模型具有较高的预测精度（表3和4）。重要结论：通过比较SAA-LSSVR和5FCV-LSSVR模型的预测结果,发现所建GA-LSSVR模型具有较好的预测性能和精度。基于所建立的GA-LSSVR模型可进行有效的多变量控制器设计。     

Dynamic modeling and direct power control of wind turbine driven DFIG under unbalanced network voltage conditions

This paper proposes an analysis and a direct power control （DPC） design of a wind turbine driven doubly-fed induction generator （DFIG） under unbalanced network voltage conditions. A DFIG model described in the positive and negative synchronous reference frames is presented. Variations of the stator output active and reactive powers are fully deduced in the presence of negative sequence supply voltage and rotor flux. An enhanced DPC scheme is proposed to eliminate stator active power oscillation during network unbalance. The proposed control scheme removes rotor current regulators and the decomposition processing of positive and negative sequence rotor currents. Simulation results using PSCAD/EMTDC are presented on a 2-MW DFIG wind power generation system to validate the feasibility of the proposed control scheme under balanced and unbalanced network conditions.     

基于小波神经网络和自回归模型耦合的河道洪水预测方法

在分析非线性河道洪水预报方法中常用BP神经网络不足的基础上,采用具有快速收敛和更有效非线性逼近能力特性的小波神经网络.为适应洪水演进的时变特性,将所建立的用于河道洪水预报的小波神经网络与自回归实时校正模型耦合,校正值为小波神经网络预报值与自回归模型预报误差之和.自回归实时校正模型的参数通过自适应衰减因子递推最小二乘动态更新以提高校正效果.将该方法应用于西江高要断面洪水预报,计算结果验证了其有效性.     

Adaptive inverse control of air supply flow for proton exchange membrane fuel cell systems

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.