Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy
This paper introduces the use of a new low-computation cost algorithm combining neural networks with the Nelder–Mead simplex method to monitor the variations of the parameters of a previously selected equivalent circuit calculated from Electrochemical Impedance Spectroscopy (EIS) corresponding to a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/8/11/238 |
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| author | Javier Olarte Jaione Martinez de Ilarduya Ekaitz Zulueta Raquel Ferret Joseba Garcia-Ortega Jose Manuel Lopez-Guede |
| author_facet | Javier Olarte Jaione Martinez de Ilarduya Ekaitz Zulueta Raquel Ferret Joseba Garcia-Ortega Jose Manuel Lopez-Guede |
| author_sort | Javier Olarte |
| collection | DOAJ |
| description | This paper introduces the use of a new low-computation cost algorithm combining neural networks with the Nelder–Mead simplex method to monitor the variations of the parameters of a previously selected equivalent circuit calculated from Electrochemical Impedance Spectroscopy (EIS) corresponding to a series of battery aging experiments. These variations could be correlated with variations in the battery state over time and, therefore, identify or predict battery degradation patterns or failure modes. The authors have benchmarked four different Electrical Equivalent Circuit (EEC) parameter identification algorithms: plain neural network mapping EIS raw data to EEC parameters, Particle Swarm Optimization, Zview, and the proposed new one. In order to improve the prediction accuracy of the neural network, a data augmentation method has been proposed to improve the neural network training error. The proposed parameter identification algorithms have been compared and validated through real data obtained from a six-month aging test experiment carried out with a set of six commercial 80 Ah VLRA batteries under different cycling and temperature operation conditions. |
| first_indexed | 2024-03-09T18:28:35Z |
| format | Article |
| id | doaj.art-a7f2344ebb0a4a57a61092f8e8a651e4 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T18:28:35Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-a7f2344ebb0a4a57a61092f8e8a651e42023-11-24T07:43:43ZengMDPI AGBatteries2313-01052022-11-0181123810.3390/batteries8110238Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance SpectroscopyJavier Olarte0Jaione Martinez de Ilarduya1Ekaitz Zulueta2Raquel Ferret3Joseba Garcia-Ortega4Jose Manuel Lopez-Guede5Centre for Cooperative Research on Alternative Energies (CIC EnergiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Álava, SpainCentre for Cooperative Research on Alternative Energies (CIC EnergiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Álava, SpainDepartment of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, Álava, SpainCentre for Cooperative Research on Alternative Energies (CIC EnergiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Álava, SpainDepartment of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, Álava, SpainDepartment of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, Álava, SpainThis paper introduces the use of a new low-computation cost algorithm combining neural networks with the Nelder–Mead simplex method to monitor the variations of the parameters of a previously selected equivalent circuit calculated from Electrochemical Impedance Spectroscopy (EIS) corresponding to a series of battery aging experiments. These variations could be correlated with variations in the battery state over time and, therefore, identify or predict battery degradation patterns or failure modes. The authors have benchmarked four different Electrical Equivalent Circuit (EEC) parameter identification algorithms: plain neural network mapping EIS raw data to EEC parameters, Particle Swarm Optimization, Zview, and the proposed new one. In order to improve the prediction accuracy of the neural network, a data augmentation method has been proposed to improve the neural network training error. The proposed parameter identification algorithms have been compared and validated through real data obtained from a six-month aging test experiment carried out with a set of six commercial 80 Ah VLRA batteries under different cycling and temperature operation conditions.https://www.mdpi.com/2313-0105/8/11/238lead-acid batteryelectrochemical impedance spectroscopy (EIS)neural networks (NN)electrical equivalent circuit (EEC)State of Charge (SOC)State of Health (SOH) |
| spellingShingle | Javier Olarte Jaione Martinez de Ilarduya Ekaitz Zulueta Raquel Ferret Joseba Garcia-Ortega Jose Manuel Lopez-Guede Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy Batteries lead-acid battery electrochemical impedance spectroscopy (EIS) neural networks (NN) electrical equivalent circuit (EEC) State of Charge (SOC) State of Health (SOH) |
| title | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy |
| title_full | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy |
| title_fullStr | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy |
| title_full_unstemmed | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy |
| title_short | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy |
| title_sort | online identification of vlra battery model parameters using electrochemical impedance spectroscopy |
| topic | lead-acid battery electrochemical impedance spectroscopy (EIS) neural networks (NN) electrical equivalent circuit (EEC) State of Charge (SOC) State of Health (SOH) |
| url | https://www.mdpi.com/2313-0105/8/11/238 |
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