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.

Modeling and control of a small solar fuel cell hybrid energy system

This paper describes a solar photovoltaic fuel cell (PVEC) hybrid generation system consisting of a photovoltaic (PV) generator, a proton exchange membrane fuel cell (PEMFC), an electrolyser, a supercapacitor, a storage gas tank and power conditioning unit (PCU). The load is supplied from the PV generator with a fuel cell working in parallel. Excess PV energy when available is converted to hydrogen using an electrolyser for later use in the fuel cell. The individual mathematical model for each component is presented. Control strategy for the system is described. MATLAB/Simulink is used for the simulation of this highly nonlinear hybrid energy system. The simulation results are shown in the paper.

Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Fuzzy Variable Structure Control of Photovoltaic MPPT System

In order to reduce chattering phenomenon of variable structure control, a fuzzy variable structure control method is adopted and applied in the photovoltaic maximum power point tracking (MPPT) control system. Firstly, the electric features of PV cells and a dynamic model of photovoltaic system with a DC-DC buck converter are analysed. Then a hybrid fuzzy variable structure controller is designed. The controller is composed of a fuzzy variable structure control term and a supervisory control term. The former is the main part of the controller and the latter is used to ensure the stability of the system. Finally, the conventional variable structure control method and the fuzzy variable structure control method are applied respectively. The comparing of simulation results shows the superiority of the latter.

A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic(PV) energy system,the real-time maximum power point(MPP) of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller(FNNC),which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks,to track the MPP. With a derived learning algorithm,the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the fuzzy logic control algorithm.

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（英文）

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。