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

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Grupo de Investigación en Sistemas Inteligentes, Corporación Universitaria Comfacauca, Popayán CP 190003, Colombia
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Instituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, Spain
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Instituto Universitario de Ingeniería Energética—IUIIE, Universitat Politècnica de València, CP 46022 Valencia, Spain
*
Authors to whom correspondence should be addressed.
Energies 2018, 11(9), 2361; https://doi.org/10.3390/en11092361
Received: 17 August 2018 / Revised: 5 September 2018 / Accepted: 6 September 2018 / Published: 7 September 2018
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
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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.

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