Fractional Order PID Controller Design for Supply Manifold Pressure Control of Proton Exchange Membrane Fuel Cell

2019 ◽  
Vol 14 (3) ◽  
Author(s):  
Srinivasarao Divi ◽  
Shantanu Das ◽  
G. Uday Bhaskar Babu ◽  
S.H. Sonawane

Abstract In this work, fractional order PIλDµ (FOPID) controller designed to enhance the dynamic performance of the Proton Exchange Membrane (PEM) fuel cell. The control objective is to regulate the supply manifold pressure on cathode side to maintain oxygen excess ratio of the PEM fuel cell. The higher order PEM fuel cell model is approximated to First order plus time delay (FOPTD) model for controller design and analysis. The proposed FOPID controller is designed based on minimization of Integral Absolute Error (IAE) with pre specified maximum sensitivity (Ms) as a constraint. Uncertainty and measurement noise analysis is carried out to verify the robustness of the designed controller. The simulation results of proposed FOPID controller is compared with other designing methods. Based on minimization of IAE value, the SP 1.4 FOPID controller produces IAE value of 0.255 where as AMIGO 1.4 tuning method and ZN based FOPID tuning methods produces 0.263 and 3.817 respectively for perfect case. Based on maximum sensitivity Ms is 1.4, the SP 1.4 FOPID controller produces Ms of 1.4 where as AMIGO 1.4 PID and ZN based FOPID tuning methods produces Ms of 1.5 and 1.25 respectively for perfect case, which indicates that the proposed SP 1.4 FOPID controller is robust. The proposed SP 1.4 FOPID provides better values (rise time of 0.331 sec, settling time of 0.692 sec and percentage of peak overshoot of 0.797 for perfect case) when compared with other methods. From simulation results, for the control of supply manifold pressure of PEM fuel cell, the proposed fractional-order PID controllers improves the closed loop performance in terms of rise time, settling time and percentage of peak overshoot when compared to the integer-order PID controllers.

2021 ◽  
Vol 100 ◽  
pp. 104193
Author(s):  
Dalia Yousri ◽  
Seyedali Mirjalili ◽  
J.A. Tenreiro Machado ◽  
Sudhakar Babu Thanikanti ◽  
Osama elbaksawi ◽  
...  

2021 ◽  
Vol 11 (14) ◽  
pp. 6348
Author(s):  
Zijun Yang ◽  
Bowen Wang ◽  
Xia Sheng ◽  
Yupeng Wang ◽  
Qiang Ren ◽  
...  

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.


Author(s):  
Utku Gulan ◽  
Hasmet Turkoglu ◽  
Irfan Ar

In this study, the fluid flow and cell performance in cathode side of a proton exchange membrane (PEM) fuel cell were numerically analyzed. The problem domain consists of cathode gas channel, cathode gas diffusion layer, and cathode catalyst layer. The equations governing the motion of air, concentration of oxygen, and electrochemical reactions were numerically solved. A computer program was developed based on control volume method and SIMPLE algorithm. The mathematical model and program developed were tested by comparing the results of numerical simulations with the results from literature. Simulations were performed for different values of inlet Reynolds number and inlet oxygen mole fraction at different operation temperatures. Using the results of these simulations, the effects of these parameters on the flow, oxygen concentration distribution, current density and power density were analyzed. The simulations showed that the oxygen concentration in the catalyst layer increases with increasing Reynolds number and hence the current density and power density of the PEM fuel cell also increases. Analysis of the data obtained from simulations also shows that current density and power density of the PEM fuel cell increases with increasing operation temperature. It is also observed that increasing the inlet oxygen mole fraction increases the current density and power density.


2006 ◽  
Vol 4 (4) ◽  
pp. 468-473 ◽  
Author(s):  
Alessandra Perna

The purpose of this work is to investigate, by a thermodynamic analysis, the effects of the process variables on the performance of an autothermal reforming (ATR)-based fuel processor, operating on ethanol as fuel, integrated into an overall proton exchange membrane (PEM) fuel cell system. This analysis has been carried out finding the better operating conditions to maximize hydrogen yield and to minimize CO carbon monoxide production. In order to evaluate the overall efficiency of the system, PEM fuel cell operations have been analyzed by an available parametric model.


Author(s):  
Zhongying Shi ◽  
Xia Wang

The gas diffusion layer (GDL) in a proton exchange membrane (PEM) fuel cell has a porous structure with anisotropic and non-homogenous properties. The objective of this research is to develop a PEM fuel cell model where transport phenomena in the GDL are simulated based on GDL’s pore structure. The GDL pore structure was obtained by using a scanning electron microscope (SEM). GDL’s cross-section view instead of surface view was scanned under the SEM. The SEM image was then processed using an image processing tool to obtain a two dimensional computational domain. This pore structure model was then coupled with an electrochemical model to predict the overall fuel cell performance. The transport phenomena in the GDL were simulated by solving the Navier-Stokes equation directly in the GDL pore structure. By comparing with the testing data, the fuel cell model predicted a reasonable fuel cell polarization curve. The pore structure model was further used to calculate the GDL permeability. The numerically predicted permeability was close to the value published in the literature. A future application of the current pore structure model is to predict GDL thermal and electric related properties.


2005 ◽  
Author(s):  
Stella Papasavva ◽  
Chris Sloane ◽  
Fred Wagner ◽  
Mike Steele ◽  
Gerald Voecks ◽  
...  

Author(s):  
Z. Shi ◽  
X. Wang

The gas diffusion layer (GDL) in a proton exchange membrane (PEM) fuel cell has a porous structure with anisotropic and non-homogenous properties. The objective of this research is to develop a PEM fuel cell model where transport phenomena in the GDL are simulated based on GDL’s pore structure. The GDL pore structure was obtained by using a scanning electron microscope (SEM). GDL’s cross-section view instead of surface view was scanned under the SEM. The SEM image was then processed using an image processing tool to obtain a two-dimensional computational domain. This pore structure model was then coupled with an electrochemical model to predict the overall fuel cell performance. The transport phenomena in the GDL were simulated by solving the Navier-Stokes equation directly in the GDL pore structure. By comparing with the testing data, the fuel cell model predicted a reasonable fuel cell polarization curve. The pore structure model was further used to calculate the GDL permeability. The numerically predicted permeability was close to the value published in the literature. A future application of the current pore structure model is to predict GDL thermal and electric related properties.


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