Comparative Analysis of Battery State of Charge Estimation Methods
Lithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00033.pdf |
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| author | Margal Ali El Daoudi Soukaina Khallouq Abdelmounaim Karama Asma |
| author_facet | Margal Ali El Daoudi Soukaina Khallouq Abdelmounaim Karama Asma |
| author_sort | Margal Ali |
| collection | DOAJ |
| description | Lithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC), which indicates the available charge in the battery and ensures its operational range. This paper presents three methods for estimating SoC: the extended Kalman filter (EKF), the adaptive Luenberger observer (ALO), and a neural network model employing nonlinear auto-regressive with eXogenous inputs (NARX). These methods are evaluated under the LA92 driving cycle using metrics like Root Mean Square Error (RMSE) to assess their performance. Results show that the NARX model achieves the highest accuracy with an RMSE of 0.33%, followed by the EKF with 5.34% and finally the ALO with 5.94%. These findings indicate that all three methods are acceptable, and the proposed NARX model shows superior performance. With the NARX model exhibiting superior performance in SoC estimation for electric vehicle applications. |
| format | Article |
| id | doaj-art-da12003ae6c94768aa06498f714a2afa |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-da12003ae6c94768aa06498f714a2afa2025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010003310.1051/e3sconf/202560100033e3sconf_icegc2024_00033Comparative Analysis of Battery State of Charge Estimation MethodsMargal Ali0El Daoudi Soukaina1Khallouq Abdelmounaim2Karama Asma3A2(IS) Research Team, Physics Department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityA2(IS) Research Team, Physics Department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityA2(IS) Research Team, Physics Department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityA2(IS) Research Team, Physics Department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityLithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC), which indicates the available charge in the battery and ensures its operational range. This paper presents three methods for estimating SoC: the extended Kalman filter (EKF), the adaptive Luenberger observer (ALO), and a neural network model employing nonlinear auto-regressive with eXogenous inputs (NARX). These methods are evaluated under the LA92 driving cycle using metrics like Root Mean Square Error (RMSE) to assess their performance. Results show that the NARX model achieves the highest accuracy with an RMSE of 0.33%, followed by the EKF with 5.34% and finally the ALO with 5.94%. These findings indicate that all three methods are acceptable, and the proposed NARX model shows superior performance. With the NARX model exhibiting superior performance in SoC estimation for electric vehicle applications.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00033.pdf |
| spellingShingle | Margal Ali El Daoudi Soukaina Khallouq Abdelmounaim Karama Asma Comparative Analysis of Battery State of Charge Estimation Methods E3S Web of Conferences |
| title | Comparative Analysis of Battery State of Charge Estimation Methods |
| title_full | Comparative Analysis of Battery State of Charge Estimation Methods |
| title_fullStr | Comparative Analysis of Battery State of Charge Estimation Methods |
| title_full_unstemmed | Comparative Analysis of Battery State of Charge Estimation Methods |
| title_short | Comparative Analysis of Battery State of Charge Estimation Methods |
| title_sort | comparative analysis of battery state of charge estimation methods |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00033.pdf |
| work_keys_str_mv | AT margalali comparativeanalysisofbatterystateofchargeestimationmethods AT eldaoudisoukaina comparativeanalysisofbatterystateofchargeestimationmethods AT khallouqabdelmounaim comparativeanalysisofbatterystateofchargeestimationmethods AT karamaasma comparativeanalysisofbatterystateofchargeestimationmethods |