A new problem formulation for effective identification of fuel cell parameters is proposed. The proposed formulation is solved by applying Genetic Algorithm optimization technique. The algorithm steps are coded in MATLAB and the objective function is solved for PEM fuel cell. In order to estimate the performance of the proposed formulation; extensive simulations are performed with both the proposed and conventional objective functions and the results obtained are compared. Further, a comprehensive evaluation based on objective function value, convergence speed and absolute voltage error value is also made to prove the superiority of the proposed formulation over the conventional curve fitting approach. © 2015 Elsevier Ltd.

Balasubramanian V

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Sathishkumar K

Department of Electrical Engineering

School of Electrical Engineering

Rajasekar N

Department of Energy and Power Electronics

Priya K

Sudhakar Babu T

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Sustainable Energy Technologies and Assessments

The widespread use of Proton Exchange Membrane fuel cell for its unique advantages compelled researchers for precise modelling of its characteristics. Since, modelling becomes extremely important for better understanding, simulation, design, analysis and development of high efficiency fuel cell system. However, due to its non-linearity, multivariate and strongly coupled characteristics; mathematical modelling based on empirical equations was widely adopted. But, the shortage of data, complexity in modelling, and number of unknown parameters favored the use of optimization methods. Many optimization methods have been endeavored to model Proton Exchange Membrane fuel cell characteristics. However, no prior attempt has been made to consolidate the contributions. Hence, this paper comprehensively describes and discusses the various Artificial Intelligence/bio inspired methods applied for fuel cell parameter estimation problem. The methods background theory and its application to the problem is elaborated. It is envisioned that, this review will be a one stop solution to the researchers and engineers working in the area of fuel cell systems. © 2018 Elsevier Ltd

Renewable and Sustainable Energy Reviews

A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling

Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel cell electric vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network-based maximum power point tracking (MPPT) controller for 1.26-kW proton exchange membrane fuel cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain dc-dc boost converter. The proposed neural network MPPT controller uses a radial basis function network (RBFN) algorithm for tracking the maximum power point of the PEMFC. High switching-frequency and high voltage-gain dc-dc converters are essential for the propulsion of FCEV. In order to attain high voltage-gain, a three-phase high voltage-gain interleaved boost converter is also designed for FCEV system. The interleaving technique reduces the input current ripple and voltage stress on the power semiconductor devices. The performance analysis of the FCEV system with RBFN-based MPPT controller is compared with the fuzzy logic controller in MATLAB/Simulink platform.

Fulltext

IEEE Access

High Voltage Gain Interleaved Boost Converter With Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications

The Proportional Integral Derivative (PID) controllers are extensively used as the controllers for controlling the system outputs. The performance of the system is mainly depends on the parameters of the PID controller. To tune these parameters, various techniques are available, and in these methods, there exists drawbacks. To overcome these drawbacks in this paper, the tuning of PID parameters are done by using genetic algorithm, and it is effectively tested on linear inverted pendulum system (LIP). In addition, extensive simulations are performed by varying parameters of the pendulum. For brevity, comparison is made with Z–N methods of tuning, and it is observed that GA-tuned PID controller is giving better performance. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Systems, Control and Automation Lecture Notes in Electrical Engineering

Tuning PID Controller for Inverted Pendulum Using Genetic Algorithm

In order to analyze, model, and control of power generation, precise models of proton exchange membrane fuel cells (PEMFCs) are very important. As we know, there has been a lot of research on nanofluid coolants and how they improve the heat transfer properties of the base fluid. Hence, a PEM fuel cell modeling using genetic algorithm (GA) is presented in this paper that exactly tries to estimate the output of the fuel cell performance when Al2O3 nanofluid coolant is used. In this paper, we determine the thermal conductivity of Al2O3 nanofluid coolant with different concentrations of ethylene glycol and water and simulate the PEMFC performance by means of the data generated using GA. © Springer Nature Singapore Pte Ltd. 2018.

Lecture Notes in Electrical Engineering Advances in Smart Grid and Renewable Energy

PEM Fuel Cell Modeling Using Genetic Algorithm—A Novel Approach

Operating conditions such as temperature and pressure affect the fuel cell performance. Proton Exchange Membrane (PEM) fuel cells work at its maximum efficiency when it is operated within temperature limits. To improve the heat transfer properties of PEM fuel cells, nano coolants are added. Identification of a suitable combination of thermal conductivity, relative humidity, and parametric coefficients for performance assessment of PEM fuel cells in real time is mandatory. Conventional methods follow complex computations and trial and error procedure to model fuel cell without thermal management parameters. Hence, in this paper, the objective of parameter set identification is framed as an optimization problem and solved using Genetic Algorithm method. The performance of the proposed approach is evaluated for different test conditions and validated through simulation findings. © 2017, Copyright © Taylor &amp; Francis Group, LLC.

Electric Power Components and Systems

Genetic Algorithm-based Modeling of PEM Fuel Cells Suitable for Integration in DC Microgrids

Accuracy of fuel cell characteristics mainly depends on its parameters. Generally, estimation is done in a curve fitting procedure. In this paper, the problem of fuel cell parameter extraction is formulated as an objective function and solved using simple Genetic Algorithm. Explanations about the new formulation and its implementation using GA is elaborated. The candidature of the proposed method is explained. The results are compared with existing curve fitting method. © 2015 The Authors. Published by Elsevier Ltd.

Energy Procedia

Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer's datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings. © 2013 Elsevier Ltd.

Journal	Data powered by TypesetSustainable Energy Technologies and Assessments
Publisher	Data powered by TypesetElsevier BV
ISSN	2213-1388
Open Access	0