Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF a...
Ausführliche Beschreibung
Autor*in: |
Huan Zhou [verfasserIn] Jing Luo [verfasserIn] Zinbin Yu [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: Energy Reports - Elsevier, 2016, 10(2023), Seite 4420-4428 |
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Übergeordnetes Werk: |
volume:10 ; year:2023 ; pages:4420-4428 |
Links: |
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DOI / URN: |
10.1016/j.egyr.2023.11.017 |
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Katalog-ID: |
DOAJ098763628 |
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520 | |a This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. | ||
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10.1016/j.egyr.2023.11.017 doi (DE-627)DOAJ098763628 (DE-599)DOAJ638c1166a8e94bba85eb3a60e7d666e7 DE-627 ger DE-627 rakwb eng TK1-9971 Huan Zhou verfasserin aut Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. State of charge State of health Particle filtering Extended Kalman filtering Electrical engineering. Electronics. Nuclear engineering Jing Luo verfasserin aut Zinbin Yu verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 4420-4428 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:4420-4428 https://doi.org/10.1016/j.egyr.2023.11.017 kostenfrei https://doaj.org/article/638c1166a8e94bba85eb3a60e7d666e7 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723015470 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 4420-4428 |
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10.1016/j.egyr.2023.11.017 doi (DE-627)DOAJ098763628 (DE-599)DOAJ638c1166a8e94bba85eb3a60e7d666e7 DE-627 ger DE-627 rakwb eng TK1-9971 Huan Zhou verfasserin aut Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. State of charge State of health Particle filtering Extended Kalman filtering Electrical engineering. Electronics. Nuclear engineering Jing Luo verfasserin aut Zinbin Yu verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 4420-4428 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:4420-4428 https://doi.org/10.1016/j.egyr.2023.11.017 kostenfrei https://doaj.org/article/638c1166a8e94bba85eb3a60e7d666e7 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723015470 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 4420-4428 |
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10.1016/j.egyr.2023.11.017 doi (DE-627)DOAJ098763628 (DE-599)DOAJ638c1166a8e94bba85eb3a60e7d666e7 DE-627 ger DE-627 rakwb eng TK1-9971 Huan Zhou verfasserin aut Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. State of charge State of health Particle filtering Extended Kalman filtering Electrical engineering. Electronics. Nuclear engineering Jing Luo verfasserin aut Zinbin Yu verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 4420-4428 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:4420-4428 https://doi.org/10.1016/j.egyr.2023.11.017 kostenfrei https://doaj.org/article/638c1166a8e94bba85eb3a60e7d666e7 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723015470 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 4420-4428 |
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10.1016/j.egyr.2023.11.017 doi (DE-627)DOAJ098763628 (DE-599)DOAJ638c1166a8e94bba85eb3a60e7d666e7 DE-627 ger DE-627 rakwb eng TK1-9971 Huan Zhou verfasserin aut Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. State of charge State of health Particle filtering Extended Kalman filtering Electrical engineering. Electronics. Nuclear engineering Jing Luo verfasserin aut Zinbin Yu verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 4420-4428 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:4420-4428 https://doi.org/10.1016/j.egyr.2023.11.017 kostenfrei https://doaj.org/article/638c1166a8e94bba85eb3a60e7d666e7 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723015470 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 4420-4428 |
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10.1016/j.egyr.2023.11.017 doi (DE-627)DOAJ098763628 (DE-599)DOAJ638c1166a8e94bba85eb3a60e7d666e7 DE-627 ger DE-627 rakwb eng TK1-9971 Huan Zhou verfasserin aut Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. State of charge State of health Particle filtering Extended Kalman filtering Electrical engineering. Electronics. Nuclear engineering Jing Luo verfasserin aut Zinbin Yu verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 4420-4428 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:4420-4428 https://doi.org/10.1016/j.egyr.2023.11.017 kostenfrei https://doaj.org/article/638c1166a8e94bba85eb3a60e7d666e7 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723015470 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 4420-4428 |
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Huan Zhou misc TK1-9971 misc State of charge misc State of health misc Particle filtering misc Extended Kalman filtering misc Electrical engineering. Electronics. Nuclear engineering Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
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TK1-9971 Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm State of charge State of health Particle filtering Extended Kalman filtering |
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co-estimation of soc and soh for li-ion battery based on miekpf-ekpf fusion algorithm |
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Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
abstract |
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. |
abstractGer |
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. |
abstract_unstemmed |
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. |
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Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
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|
score |
7.4004955 |