Parameters Of Proton Exchange Membrane Fuel Cell Model Research Articles

Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.

Bi-subgroup optimization algorithm for parameter estimation of a PEMFC model

Proton exchange membrane fuel cell (PEMFC) has the advantages of cleanliness, environmental protection, and high stability, and is widely used in the fields of astronautics, military, and so on. How to extract the PEMFC model parameters more precisely is a key problem, which can be transformed into a highly nonlinear optimization problem. Therefore, an efficient optimization algorithm is needed. Swarm intelligence algorithms are used for solving various complex optimization problems. In this paper, a bi-subgroup optimization algorithm (BSOA) is proposed. The BSOA divides the population into two different sub-populations, one of which is to develop more optimal solutions and the other is to mine more optimal solutions within the population. Hence, it can overcomes the drawback that a single population search tends to fall into local optimal solutions and enhances the diversity of the population. By constructing and analyzing the Markov model, it is proved that the BSOA is a global convergence algorithm. Finally, the BSOA is applied to the estimation of the unknown parameters of the PEMFC modules and compared with the state-of-the-art algorithms. The simulation results show that BSOA is an effective algorithm for estimating the unknown parameters of the PEMFC model.

Online Modeling of a Fuel Cell System for an Energy Management Strategy Design

An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.

Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm

This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.

System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm

Parameter identification of the proton exchange membrane fuel cell (PEMFC) is a good way of increasing their efficiency in the next designs. In this study, an optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for this purpose. The proposed algorithm is a hybrid algorithm based on a combination of two newly algorithms, the world cup optimization (WCO) and the fluid Search Optimization (FSO) algorithms. The proposed method is applied to improve the method efficiency for estimating the PEMFC model parameters. The method is then validated by four different operational conditions. The optimization algorithm efficiency is also analyzed by comparison with some popular algorithms. Simulation results showed that using the designed method gives higher accuracy forecast for the PEMFC model parameters.

Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm

Abstract Simulation proton exchange membrane fuel cell (PEMFC) performance accurately is a challenging process. Many mathematical models have been existed, yet due to lack of accurate parameter estimations, considerable errors might occur. Nowadays, meta-heuristic optimization algorithms have been successfully applied for parameter identification of PEMFC models. In this study, Teaching Learning Based Optimization method (TLBO) is hybridized with Differential Evolution (DE) algorithm for successful estimation of unknown PEMFC model parameters. Efficiency of the proposed algorithm is tested with several benchmark problems. A case study taken from the literature has been performed by hybrid TLBO–DE algorithm and other optimization methods such as Melody Search (MS), Backtracking Search (BS), Artificial Cooperative Search (ACS), Quantum behaved Particle Swarm Optimization (QPSO), Bat algorithm (BAT), Intelligent Tuned Harmony Search (ITHS) and Cuckoo Search (CS). TLBO–DE algorithm surpasses all these optimizers in terms of solution quality and accuracy.

Hybrid artificial bee colony algorithm for parameter estimation of proton exchange membrane fuel cell

The accurate electrochemical model plays an important role in design and analysis of hydrogen fuel cell systems. For the purpose of estimating parameters of the proton exchange membrane fuel cell (PEMFC) model, and inspired by the foraging behavior of bacteria and bees, a hybrid artificial bee colony (HABC) algorithm is proposed. The HABC uses an improved solution search equation that mimics the chemotactic effect of bacteria to enhance the local search ability. To avoid premature convergence and improve search accuracy, the adaptive Boltzmann selection scheme is adopted, which adjusts selective probabilities in different stages. Performance testing has been conducted on some typical benchmark functions. The results demonstrate that the HABC outperforms other methods (BIPOA, PSOPS and two improved GAs) in both convergence speed and accuracy. The proposed approach is applied to estimate the PEMFC model parameters and the satisfactory model predictive curves are obtained. More experimental results in different search ranges and validate strategies indicate that HABC is an efficient technique for the parameter estimation problem of PEMFC.

A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model

Accurate kinetic models are of great significance for the simulation and analysis for hydrogen fuel cells. The proton exchange membrane (PEM) fuel cell is a complex nonlinear, multi-variable system. The mathematical modeling of PEM fuel cell usually leads to nonlinear parameter estimation problems which often contain more than one minimum. In this paper, a novel bio-inspired P systems based optimization algorithm, named BIPOA, is proposed to solve PEM fuel cell model parameter estimation problems. In BIPOA, the nested membrane structure and new rules such as adaptive mutation rule, partial migration rule and autophagy rule are combined to improve the algorithm's global search capacities and convergence accuracy. Studies on some benchmark test functions indicate that the BIPOA outperforms the other two methods (PSOPS and GAs) in both convergence speed and accuracy. In addition, experimental results reveal that the model predictive outputs are in better agreement with the actual experimental data. Therefore, the BIPOA is a helpful and reliable technique for estimating the PEM fuel cell model parameters and is available to other complex parameter estimation problems of fuel cell models.

A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters

An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)

In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.