Next Article in Journal
Physical and Mathematical Modeling of a Wave Energy Converter Equipped with a Negative Spring Mechanism for Phase Control
Previous Article in Journal
A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2018, 11(9), 2361; https://doi.org/10.3390/en11092361

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

1
Grupo de Investigación en Sistemas Inteligentes, Corporación Universitaria Comfacauca, Popayán CP 190003, Colombia
2
Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain
3
Instituto Universitario de Ingeniería Energética—IUIIE, Universitat Politècnica de València, CP 46022 Valencia, Spain
*
Authors to whom correspondence should be addressed.
Received: 17 August 2018 / Revised: 5 September 2018 / Accepted: 6 September 2018 / Published: 7 September 2018
Full-Text   |   PDF [2238 KB, uploaded 7 September 2018]   |  

Abstract

Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at “El Chocó”, Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems. View Full-Text
Keywords: modelling; lead-acid battery; parameter identification; genetic algorithms; experimental validation modelling; lead-acid battery; parameter identification; genetic algorithms; experimental validation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ariza Chacón, H.E.; Banguero, E.; Correcher, A.; Pérez-Navarro, Á.; Morant, F. Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies 2018, 11, 2361.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top