Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms
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...
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| Format: | Article |
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MDPI AG
2018-09-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/9/2361 |
| _version_ | 1811303933582245888 |
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| author | H. Eduardo Ariza Chacón Edison Banguero Antonio Correcher Ángel Pérez-Navarro Francisco Morant |
| author_facet | H. Eduardo Ariza Chacón Edison Banguero Antonio Correcher Ángel Pérez-Navarro Francisco Morant |
| author_sort | H. Eduardo Ariza Chacón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:56:50Z |
| format | Article |
| id | doaj.art-6350f7003a90477eb5685083d3fc0bc6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:56:50Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6350f7003a90477eb5685083d3fc0bc62022-12-22T02:55:23ZengMDPI AGEnergies1996-10732018-09-01119236110.3390/en11092361en11092361Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary AlgorithmsH. Eduardo Ariza Chacón0Edison Banguero1Antonio Correcher2Ángel Pérez-Navarro3Francisco Morant4Grupo de Investigación en Sistemas Inteligentes, Corporación Universitaria Comfacauca, Popayán CP 190003, ColombiaInstituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, SpainInstituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, SpainInstituto Universitario de Ingeniería Energética—IUIIE, Universitat Politècnica de València, CP 46022 Valencia, SpainInstituto de Automática e Informática Industrial-ai2, Universitat Politècnica de València, CP 46022 Valencia, SpainAccurate 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.http://www.mdpi.com/1996-1073/11/9/2361modellinglead-acid batteryparameter identificationgenetic algorithmsexperimental validation |
| spellingShingle | H. Eduardo Ariza Chacón Edison Banguero Antonio Correcher Ángel Pérez-Navarro Francisco Morant Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms Energies modelling lead-acid battery parameter identification genetic algorithms experimental validation |
| title | Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms |
| title_full | Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms |
| title_fullStr | Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms |
| title_full_unstemmed | Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms |
| title_short | Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms |
| title_sort | modelling parameter identification and experimental validation of a lead acid battery bank using evolutionary algorithms |
| topic | modelling lead-acid battery parameter identification genetic algorithms experimental validation |
| url | http://www.mdpi.com/1996-1073/11/9/2361 |
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