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

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  • H. Eduardo Ariza Chacón

    (Grupo de Investigación en Sistemas Inteligentes, Corporación Universitaria Comfacauca, Popayán CP 190003, Colombia
    Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain
    Instituto Universitario de Ingeniería Energética—IUIIE, Universitat Politècnica de València, CP 46022 Valencia, Spain)

  • Edison Banguero

    (Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain)

  • Antonio Correcher

    (Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain)

  • Ángel Pérez-Navarro

    (Instituto Universitario de Ingeniería Energética—IUIIE, Universitat Politècnica de València, CP 46022 Valencia, Spain)

  • Francisco Morant

    (Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain)

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.

Suggested Citation

  • H. Eduardo Ariza Chacón & Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant, 2018. "Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms," Energies, MDPI, vol. 11(9), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2361-:d:168348
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    References listed on IDEAS

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    Cited by:

    1. Jemma J. Makrygiorgou & Antonio T. Alexandridis, 2019. "Power Electronic Control Design for Stable EV Motor and Battery Operation during a Route," Energies, MDPI, vol. 12(10), pages 1-21, May.
    2. Dong Liu & Zhihuai Xiao & Hongtao Li & Dong Liu & Xiao Hu & O.P. Malik, 2019. "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.
    3. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    4. Józef Pszczółkowski, 2021. "Description of Acid Battery Operating Parameters," Energies, MDPI, vol. 14(21), pages 1-17, November.

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