scholarly journals Realisation of Optimal Parameters of PEM Fuel Cell Using Simple Genetic Algorithm (SGA) and Simulink Modeling

2019 ◽  
Vol 8 (6) ◽  
pp. 1542-1548 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Absolute Error ◽  
Parameter Extraction ◽  
Cell Parameters ◽  
Simple Genetic Algorithm ◽  
Fitting Method ◽  
Proposed Model ◽  
Curve Fitting Method

A methodology to solve parameter extraction of PEM Fuel cell by an optimisation process using simple genetic algorithm and Simulink is proposed. The results are validated using the traditional curve fitting method where in the initial values are compared with the existing curve for its convergence and exactitude. In this work the modelling and extensive simulation of the PEM Fuel cell has been undertaken using MATLAB-SIMULINK. The steps have been elaborated further in order to explain the incorporation and efficacy of Genetic algorithm codes in FC model. Simple Genetic Algorithm (SGA) is a reliable methodology towards optimisation of fuel cell parameters. It is inferred from the simulated results that the process is precise and absolute error is generated to showcase the subtleness of the algorithm. The proposed model can be utilised to study and develop steady state performances of PEMFC stacks

scholarly journals Comparative study of PEM fuel cell parameter extraction using Genetic Algorithm

2015 ◽  
Vol 6 (4) ◽  
pp. 1187-1194 ◽  
Author(s):  
N. Rajasekar ◽  
Basil Jacob ◽  
Karthik Balasubramanian ◽  
K. Priya ◽  
K. Sangeetha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Comparative Study ◽  
Cell Parameter ◽  
Pem Fuel Cell ◽  
Parameter Extraction

Application of Radial Basis Function Networks Combined with Genetic Algorithm in Predicting the Geometric Parameters of Drawbead

2011 ◽  
Vol 101-102 ◽  
pp. 790-794
Author(s):  
Jie Lai Chen ◽  
Da Zhi Jiang ◽  
Ya Yan Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Geometric Parameters ◽  
The Neural Network ◽  
Fitting Method ◽  
Radial Basis ◽  
Curve Fitting Method ◽  
Restraining Force

The drawbead plays a very important role in automobile covering part forming processes. Traditional drawbead design mainly depends on designers’ experience. In order to obtain proper restraining force during die try-out, it is often necessary to adjust the drawbead through a very complicated procedure. It is thus meaningful to study the relationship between the parameters used to reflect metal forming effects and the geometric parameters of drawbead and then create a prediction model for them. This paper employs the radial basis function neural network technology to predict the geometric parameters of drawbead used in forming processes, where the genetic algorithm is used to optimize the neural network structure. Simulation results show that the proposed approach outperforms the curve fitting method.

Design and optimization of bio-inspired wave-like channel for a PEM fuel cell applying genetic algorithm

Energy ◽  
2020 ◽  
Vol 192 ◽  
pp. 116670 ◽  
Author(s):  
Genchun Cai ◽  
Yunmin Liang ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Design And Optimization

A novel approach for fuel cell parameter estimation using simple Genetic Algorithm

2015 ◽  
Vol 12 ◽  
pp. 46-52 ◽  
Author(s):  
K. Priya ◽  
T. Sudhakar Babu ◽  
Karthik Balasubramanian ◽  
K. Sathish Kumar ◽  
N. Rajasekar
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Fuel Cell ◽  
Cell Parameter ◽  
Simple Genetic Algorithm ◽  
Novel Approach

scholarly journals Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

2015 ◽  
Vol 75 ◽  
pp. 1975-1982 ◽  
Author(s):  
Karthik Balasubramanian ◽  
Basil Jacob ◽  
K. Priya ◽  
K. Sangeetha ◽  
N. Rajasekar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Cell Parameter ◽  
Critical Evaluation ◽  
Parameter Extraction
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