Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification
The genetic algorithm (GA) is one of the most used methods to identify the parameters of Li-ion battery models. However, the parametrization of the GA method is not straightforward and can lead to poor accuracy and/or long calculation times. The Taguchi design method provides an approach to optimize...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/2/72 |
| _version_ | 1797622355995918336 |
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| author | Taha Al Rafei Nadia Yousfi Steiner Daniela Chrenko |
| author_facet | Taha Al Rafei Nadia Yousfi Steiner Daniela Chrenko |
| author_sort | Taha Al Rafei |
| collection | DOAJ |
| description | The genetic algorithm (GA) is one of the most used methods to identify the parameters of Li-ion battery models. However, the parametrization of the GA method is not straightforward and can lead to poor accuracy and/or long calculation times. The Taguchi design method provides an approach to optimize GA parameters, achieving a good balance between accuracy and calculation time. The Taguchi design method is thus used to define the most adapted GA parameters to identify the parameters of model of Li-ion batteries for household applications based on static and dynamic tests in the time domain. The results show a good compromise between calculation time and accuracy (RMSE less than 0.6). This promising approach could be applied to other Li-ion battery applications, resulting from measurements in the frequency domain or different kinds of energy storage. |
| first_indexed | 2024-03-11T09:10:01Z |
| format | Article |
| id | doaj.art-79b5b0398a194d73821f408be4932e35 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T09:10:01Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-79b5b0398a194d73821f408be4932e352023-11-16T19:07:09ZengMDPI AGBatteries2313-01052023-01-01927210.3390/batteries9020072Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter IdentificationTaha Al Rafei0Nadia Yousfi Steiner1Daniela Chrenko2FEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, 90000 Belfort, FranceFEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, 90000 Belfort, FranceFEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, 90000 Belfort, FranceThe genetic algorithm (GA) is one of the most used methods to identify the parameters of Li-ion battery models. However, the parametrization of the GA method is not straightforward and can lead to poor accuracy and/or long calculation times. The Taguchi design method provides an approach to optimize GA parameters, achieving a good balance between accuracy and calculation time. The Taguchi design method is thus used to define the most adapted GA parameters to identify the parameters of model of Li-ion batteries for household applications based on static and dynamic tests in the time domain. The results show a good compromise between calculation time and accuracy (RMSE less than 0.6). This promising approach could be applied to other Li-ion battery applications, resulting from measurements in the frequency domain or different kinds of energy storage.https://www.mdpi.com/2313-0105/9/2/72second-life EV batteriesEECMdigital twingenetic algorithmTaguchi experimental design |
| spellingShingle | Taha Al Rafei Nadia Yousfi Steiner Daniela Chrenko Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification Batteries second-life EV batteries EECM digital twin genetic algorithm Taguchi experimental design |
| title | Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification |
| title_full | Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification |
| title_fullStr | Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification |
| title_full_unstemmed | Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification |
| title_short | Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification |
| title_sort | genetic algorithm and taguchi method an approach for better li ion cell model parameter identification |
| topic | second-life EV batteries EECM digital twin genetic algorithm Taguchi experimental design |
| url | https://www.mdpi.com/2313-0105/9/2/72 |
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