Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model
Abstract The precision of equivalent circuit model (ECM)‐based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2022-12-01
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| Series: | IET Electrical Systems in Transportation |
| Online Access: | https://doi.org/10.1049/els2.12056 |
| _version_ | 1797422987169759232 |
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| author | Meriem Ben Lazreg Sabeur Jemmali Bilal Manai Mahmoud Hamouda |
| author_facet | Meriem Ben Lazreg Sabeur Jemmali Bilal Manai Mahmoud Hamouda |
| author_sort | Meriem Ben Lazreg |
| collection | DOAJ |
| description | Abstract The precision of equivalent circuit model (ECM)‐based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of the ECM parameters at different temperatures and SoC levels. The fuzzy inference system is designed to handle the non‐linear deviation of the battery parameters from their reference values. On this basis, the extended Kalman filter and smooth variable structure filter are used to estimate the SoC. The two algorithms with fuzzy parameters (FP), namely FP‐EKF and FP‐SVSF, are tested on a 20 Ah Nickel Manganese Cobalt cell with maximum voltage of 4.2 V. The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP‐EKF and 0.68% with the FP‐SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP‐EKF, and 0.82% with the FP‐SVSF, compared to the same algorithms without the proposed FP method. The executable codes are implemented on a low‐cost controller, and the average computational time is obtained as 215 μs, which confirms the real‐time practicality of the proposed method. |
| first_indexed | 2024-03-09T07:39:57Z |
| format | Article |
| id | doaj.art-187ae72702614798891897d4aba9828f |
| institution | Directory Open Access Journal |
| issn | 2042-9738 2042-9746 |
| language | English |
| last_indexed | 2024-03-09T07:39:57Z |
| publishDate | 2022-12-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj.art-187ae72702614798891897d4aba9828f2023-12-03T04:50:50ZengHindawi-IETIET Electrical Systems in Transportation2042-97382042-97462022-12-0112431532910.1049/els2.12056Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters modelMeriem Ben Lazreg0Sabeur Jemmali1Bilal Manai2Mahmoud Hamouda3Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS‐Laboratory of Advanced Technology and Intelligent Systems Sousse TunisiaUniversité de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS‐Laboratory of Advanced Technology and Intelligent Systems Sousse TunisiaUniversité de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS‐Laboratory of Advanced Technology and Intelligent Systems Sousse TunisiaUniversité de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS‐Laboratory of Advanced Technology and Intelligent Systems Sousse TunisiaAbstract The precision of equivalent circuit model (ECM)‐based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of the ECM parameters at different temperatures and SoC levels. The fuzzy inference system is designed to handle the non‐linear deviation of the battery parameters from their reference values. On this basis, the extended Kalman filter and smooth variable structure filter are used to estimate the SoC. The two algorithms with fuzzy parameters (FP), namely FP‐EKF and FP‐SVSF, are tested on a 20 Ah Nickel Manganese Cobalt cell with maximum voltage of 4.2 V. The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP‐EKF and 0.68% with the FP‐SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP‐EKF, and 0.82% with the FP‐SVSF, compared to the same algorithms without the proposed FP method. The executable codes are implemented on a low‐cost controller, and the average computational time is obtained as 215 μs, which confirms the real‐time practicality of the proposed method.https://doi.org/10.1049/els2.12056 |
| spellingShingle | Meriem Ben Lazreg Sabeur Jemmali Bilal Manai Mahmoud Hamouda Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model IET Electrical Systems in Transportation |
| title | Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model |
| title_full | Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model |
| title_fullStr | Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model |
| title_full_unstemmed | Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model |
| title_short | Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model |
| title_sort | enhanced ekf and svsf for state of charge estimation of li ion battery in electric vehicle using a fuzzy parameters model |
| url | https://doi.org/10.1049/els2.12056 |
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