A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm
Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapaci...
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| Autor*in: |
Zhao, Yanming [verfasserIn] Xie, Wenchao [verfasserIn] Wu, Jinhao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of electrical engineering & technology - Springer Nature Singapore, 2006, 19(2024), 8 vom: 29. März, Seite 4927-4940 |
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| Übergeordnetes Werk: |
volume:19 ; year:2024 ; number:8 ; day:29 ; month:03 ; pages:4927-4940 |
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| DOI / URN: |
10.1007/s42835-024-01883-y |
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SPR057941246 |
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| 520 | |a Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. | ||
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| 700 | 1 | |a Wu, Jinhao |e verfasserin |4 aut | |
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10.1007/s42835-024-01883-y doi (DE-627)SPR057941246 (SPR)s42835-024-01883-y-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ Zhao, Yanming verfasserin aut A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 Xie, Wenchao verfasserin aut Wu, Jinhao verfasserin aut Enthalten in Journal of electrical engineering & technology Springer Nature Singapore, 2006 19(2024), 8 vom: 29. März, Seite 4927-4940 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:19 year:2024 number:8 day:29 month:03 pages:4927-4940 https://dx.doi.org/10.1007/s42835-024-01883-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 19 2024 8 29 03 4927-4940 |
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10.1007/s42835-024-01883-y doi (DE-627)SPR057941246 (SPR)s42835-024-01883-y-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ Zhao, Yanming verfasserin aut A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 Xie, Wenchao verfasserin aut Wu, Jinhao verfasserin aut Enthalten in Journal of electrical engineering & technology Springer Nature Singapore, 2006 19(2024), 8 vom: 29. März, Seite 4927-4940 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:19 year:2024 number:8 day:29 month:03 pages:4927-4940 https://dx.doi.org/10.1007/s42835-024-01883-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 19 2024 8 29 03 4927-4940 |
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10.1007/s42835-024-01883-y doi (DE-627)SPR057941246 (SPR)s42835-024-01883-y-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ Zhao, Yanming verfasserin aut A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 Xie, Wenchao verfasserin aut Wu, Jinhao verfasserin aut Enthalten in Journal of electrical engineering & technology Springer Nature Singapore, 2006 19(2024), 8 vom: 29. März, Seite 4927-4940 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:19 year:2024 number:8 day:29 month:03 pages:4927-4940 https://dx.doi.org/10.1007/s42835-024-01883-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 19 2024 8 29 03 4927-4940 |
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10.1007/s42835-024-01883-y doi (DE-627)SPR057941246 (SPR)s42835-024-01883-y-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ Zhao, Yanming verfasserin aut A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 Xie, Wenchao verfasserin aut Wu, Jinhao verfasserin aut Enthalten in Journal of electrical engineering & technology Springer Nature Singapore, 2006 19(2024), 8 vom: 29. März, Seite 4927-4940 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:19 year:2024 number:8 day:29 month:03 pages:4927-4940 https://dx.doi.org/10.1007/s42835-024-01883-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 19 2024 8 29 03 4927-4940 |
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10.1007/s42835-024-01883-y doi (DE-627)SPR057941246 (SPR)s42835-024-01883-y-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ Zhao, Yanming verfasserin aut A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 Xie, Wenchao verfasserin aut Wu, Jinhao verfasserin aut Enthalten in Journal of electrical engineering & technology Springer Nature Singapore, 2006 19(2024), 8 vom: 29. März, Seite 4927-4940 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:19 year:2024 number:8 day:29 month:03 pages:4927-4940 https://dx.doi.org/10.1007/s42835-024-01883-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 19 2024 8 29 03 4927-4940 |
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Zhao, Yanming @@aut@@ Xie, Wenchao @@aut@@ Wu, Jinhao @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Supercapacitor cell module</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">State of Charge</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">EKF-MF</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Wenchao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Jinhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of electrical engineering & technology</subfield><subfield code="d">Springer Nature Singapore, 2006</subfield><subfield code="g">19(2024), 8 vom: 29. 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Zhao, Yanming |
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Zhao, Yanming ddc 620 misc Supercapacitor cell module misc State of Charge misc Estimation misc EKF-MF A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm |
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620 VZ A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm Supercapacitor cell module (dpeaa)DE-He213 State of Charge (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 EKF-MF (dpeaa)DE-He213 |
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ddc 620 misc Supercapacitor cell module misc State of Charge misc Estimation misc EKF-MF |
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A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm |
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Zhao, Yanming |
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a novel soc estimation method for supercapacitor cell module based on ekf-mf hybrid filtering algorithm |
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A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm |
| abstract |
Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm |
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https://dx.doi.org/10.1007/s42835-024-01883-y |
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Xie, Wenchao Wu, Jinhao |
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10.1007/s42835-024-01883-y |
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2024-10-22T04:52:15.639Z |
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| score |
7.402895 |