Parameter Optimization of PEMFC with Genetic Algorithm

2016 ◽  
Vol 12 (03) ◽  
pp. 241-249 ◽  
Author(s):  
Puja Bhatt ◽  
Neha Agarwal ◽  
Uday K. Chakraborty
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuel Cell ◽  
Parameter Optimization ◽  
Proton Exchange Membrane ◽  
Cell Parameter ◽  
Proton Exchange ◽  
Exchange Membrane

This paper provides a review of recent research in the application of genetic algorithms to proton exchange membrane fuel cell parameter optimization.

scholarly journals Optimization of high-temperature proton exchange membrane fuel cell flow channel based on genetic algorithm

2021 ◽  
Vol 7 ◽  
pp. 1374-1384 ◽  
Author(s):  
Taiming Huang ◽  
Wei Wang ◽  
Yao Yuan ◽  
Jie Huang ◽  
Xi Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
High Temperature ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Flow Channel ◽  
Proton Exchange ◽  
Exchange Membrane

Modeling of Proton Exchange Membrane Fuel Cell (PEMFC) Performance by Using Genetic Algorithm-Polynomial Neural Network (GA-PNN) Hybrid System

2012 ◽  
Author(s):  
Mehdi Mehrabi ◽  
Sajad Rezazadeh ◽  
Mohsen Sharifpur ◽  
Josua P. Meyer
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuel Cell ◽  
Relative Humidity ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Cathode Side ◽  
Data Set ◽  
Polynomial Neural Network ◽  
Exchange Membrane

In the present study, a genetic algorithm-polynomial neural network (GA-PNN) was used for modeling proton exchange membrane fuel cell (PEMFC) performance, based on some numerical results which were correlated with experimental data. Thus, the current density was modeled in respect of input (design) variables, i.e., the variation of pressure at the cathode side, voltage, membrane thickness, anode transfer coefficient, relative humidity of inlet fuel and relative humidity of inlet air. The numerical data set for the modeling was divided into train and test sections. The GA-PNN model was introduced with 80% of the numerically-validated data and the remaining data was used for testing the appropriateness of the GA-PNN model by means of two statistical criteria.

Parameter derivation of a proton exchange membrane fuel cell based on coevolutionary ribonucleic acid genetic algorithm

2019 ◽  
Vol 35 (4) ◽  
pp. 1021-1041 ◽  
Author(s):  
Tian Erlin ◽  
Abdol Ghaffar Ebadi ◽  
Dinesh Mavaluru ◽  
Mohammed Alshehri ◽  
Ahmed Abo‐Bakr Mohamed ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Ribonucleic Acid ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Exchange Membrane

Parameter Optimization of a Linear-Quadratic-Gaussian Controller for a Proton Exchange Membrane Fuel Cell Using Genetic Algorithms

2014 ◽  
Author(s):  
Jairo A. Rodríguez-Barrera ◽  
Jaime A. Parra-Raad ◽  
Sebastián Roa-Prada
Keyword(s):  
Genetic Algorithms ◽  
Fuel Cell ◽  
State Space ◽  
Proton Exchange Membrane ◽  
Clean Energy ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Exchange Membrane

Fuel cells are sources of clean energy which have become a key enabling technology in a wide spectrum of applications, ranging from automotive and aerospace applications to power supply for off-grid communities. The adequate functioning of a fuel cell requires permanent electrical power delivery to its load, operating at its maximum possible efficiency, even under load variations. Controlling the operating point of the fuel cell to manage changes in load conditions allows extending its service life. Several variables must be monitored and/or controlled to achieve optimal operating conditions of the fuel cell. This work deals with the design of a linear-quadratic-Gaussian, LQG, state-space controller for a proton exchange membrane fuel cell. The LQG controller is commonly used in fuel cell applications because it features an observer which can reconstruct states that are needed for the control strategy and that many times are difficult or too expensive to measure. The tuning of the parameters of the controller is performed by means of genetic algorithms procedures. The goal of the optimization is to prevent low levels of reactant gases due to sudden increases in the load. This will avoid damages to the membrane and other components of the stack while improving the overall performance of the system. The open loop and closed loop system response are presented using the lineal and non-lineal model of the plant. The response of the compensated system using the LQG controller is compared to the response using a basic state space controller, designed by the pole placing method, to assess the robustness of the LQG controller under disturbances. The results demonstrate the ability of the genetic algorithm technique to design a controller that can help preserving the integrity of the fuel cell while optimizing its performance.

Sign in / Sign up

Export Citation Format

Share Document