Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method
In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/10/e3sconf_icies2020_00097.pdf |
| _version_ | 1818252403852443648 |
|---|---|
| author | Elmahdi Fadlaoui Ismail Lagrat Noureddine Masaif |
| author_facet | Elmahdi Fadlaoui Ismail Lagrat Noureddine Masaif |
| author_sort | Elmahdi Fadlaoui |
| collection | DOAJ |
| description | In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. The polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. The results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation. |
| first_indexed | 2024-12-12T16:23:38Z |
| format | Article |
| id | doaj.art-20662a38e7eb493b9fc144f81b33bfca |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-12T16:23:38Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-20662a38e7eb493b9fc144f81b33bfca2022-12-22T00:18:56ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012340009710.1051/e3sconf/202123400097e3sconf_icies2020_00097Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm methodElmahdi Fadlaoui0Ismail Lagrat1Noureddine Masaif2Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, Ibn Tofail UniversityLaboratory of Advanced Systems Engineering, National Schools of Applied Sciences, Ibn Tofail UniversityLaboratory of Electronic Systems, Information Processing, Mechanics and Energy, Ibn Tofail UniversityIn response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. The polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. The results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/10/e3sconf_icies2020_00097.pdf |
| spellingShingle | Elmahdi Fadlaoui Ismail Lagrat Noureddine Masaif Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method E3S Web of Conferences |
| title | Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method |
| title_full | Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method |
| title_fullStr | Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method |
| title_full_unstemmed | Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method |
| title_short | Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method |
| title_sort | fitting the ocv soc relationship of a battery lithium ion using genetic algorithm method |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/10/e3sconf_icies2020_00097.pdf |
| work_keys_str_mv | AT elmahdifadlaoui fittingtheocvsocrelationshipofabatterylithiumionusinggeneticalgorithmmethod AT ismaillagrat fittingtheocvsocrelationshipofabatterylithiumionusinggeneticalgorithmmethod AT noureddinemasaif fittingtheocvsocrelationshipofabatterylithiumionusinggeneticalgorithmmethod |