PEM fuel cell modeling using differential evolution

Chakraborty, Uday K.; Abbott, Travis E.; Das, Sajal K.

doi:10.1016/j.energy.2012.01.039

Cited by 108 publications

(43 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, strategy pool is composed of many perturbation schemes some of which are given in Eqs. (23)- (28). Inspired by the ensemble learning [60], which aims to combine different solution strategies into a single perturbation scheme, this study proposes an ensemble of mutation strategies including DE/best/1 and DE/best/2 in order to avoid being trapped in local optimum points.…”

Section: Hybrid Tlbo-de Algorithmmentioning

confidence: 99%

“…To conquer this drawback, metaheuristic algorithms such as Genetic Algorithms (GA) [20][21][22][23][24][25], Simulated Annealing (SA) [26,27], Differential Evolution (DE) [28,29], Particle Swarm Optimization (PSO) [30,31], Artificial Immune System (AIS) [5], Seeker Optimization Algorithm (SOA) [32], Harmony Search (HS) [33,34], Hybrid Artificial Bee Colony (HABC) [19], Artificial Bee Swarm Algorithm (ABSA) [35], P System Based Optimization (PSBO) [36], Teaching-learning-based optimization (TLBO) [37], Biogeography-based optimization [38] and Bird Mating Optimization (BMO) [39] have been applied in this problem. Metaheuristics generally do not need domain information and they are derivative free methods which perform stochastic movements to obtain global optimum point.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Turgut

Çoban

2016

Ain Shams Engineering Journal

107

View full text Add to dashboard Cite

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.

show abstract

Section: Hybrid Tlbo-de Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Turgut

Çoban

2016

Ain Shams Engineering Journal

107

View full text Add to dashboard Cite

show abstract

“…The advantages of DE are simple structure, ease of use, speed and robustness. Due to these advantages, it has got many real-world applications, such as large scale economic dispatch (Srinivasa Reddy and Vaisakh 2013), remote sensing image subpixel mapping (Zhong and Zhang 2012), gene regulatory networks (Kozlov and Samsonov 2011), proton exchange membrane fuel cell stack modeling (Chakraborty et al 2012), robot control (Neri and Mininno 2010), antenna design (Goudos et al 2011), feature selection (Khushaba et al 2011), and many others.…”

Section: Introductionmentioning

confidence: 99%

Enhanced differential evolution using random-based sampling and neighborhood mutation

2014

View full text Add to dashboard Cite

Differential evolution (DE) is a simple and efficient global optimization algorithm. When differential evolution is applied in complex optimization problems, it has the shortages of prematurity and stagnation. An enhanced differential evolution using random sampling and neighborhood mutation to solve the above problems is proposed in this paper. The proposed enhanced DE is called random-based differential evolution with neighborhood mutation (NRDE). Random-based sampling is an improvement of center-based sampling. In NRDE, random-based sampling as the new mutation operator to generate the random-based individuals and the designed neighborhood mutation operator are used to search in the neighborhood created by the centers of the population and the sub-population. This paper compares other state-of-the-art evolutionary algorithms with the proposed algorithm, NRDE. Experimental verifications are conducted on 24 benchmark functions and the CEC'05 competition, including detailed analysis for NRDE. The results clearly show that NRDE outperforms other evolutionary algorithms in terms of the solution accuracy and the convergence rate.

show abstract

“…The optimized parameters were temperature dependent, and a comparison between the considered model and one based on Peng-Robison equation of state and Wong-Sandler mixing rule showed that the former is better. Chakraborty et al (2012) used two variants of DEbased algorithm proposed in Das et al (2009) for parameter optimization of a mechanicist model of the proton membrane fuel cell (PEMFC). The idea was to determine the model's optimal seven parameters so that it best fits the PEMFC stack.…”

Section: De Optimization Applied To Deterministic Modelsmentioning

confidence: 99%

The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi

Curteanu

2016

Reviews in Chemical Engineering

View full text Add to dashboard Cite

AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.

show abstract

PEM fuel cell modeling using differential evolution

Cited by 108 publications

References 17 publications

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

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

Enhanced differential evolution using random-based sampling and neighborhood mutation

The use of differential evolution algorithm for solving chemical engineering problems

Contact Info

Product

Resources

About