Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation
Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknow...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Lin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion - Solanki, Nayan ELSEVIER, 2017, the international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:215 ; year:2021 ; day:15 ; month:01 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.energy.2020.119078 |
|---|
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ELV052286983 |
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| 520 | |a Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. | ||
| 520 | |a Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. | ||
| 700 | 1 | |a Wang, Huimin |4 oth | |
| 700 | 1 | |a Liu, Bohao |4 oth | |
| 700 | 1 | |a Wang, Yijue |4 oth | |
| 700 | 1 | |a Ding, Yunhui |4 oth | |
| 700 | 1 | |a Pan, Haihong |4 oth | |
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10.1016/j.energy.2020.119078 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001223.pica (DE-627)ELV052286983 (ELSEVIER)S0360-5442(20)32185-X DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Chen, Lin verfasserin aut Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Wang, Huimin oth Liu, Bohao oth Wang, Yijue oth Ding, Yunhui oth Pan, Haihong oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:215 year:2021 day:15 month:01 pages:0 https://doi.org/10.1016/j.energy.2020.119078 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 215 2021 15 0115 0 |
| spelling |
10.1016/j.energy.2020.119078 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001223.pica (DE-627)ELV052286983 (ELSEVIER)S0360-5442(20)32185-X DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Chen, Lin verfasserin aut Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Wang, Huimin oth Liu, Bohao oth Wang, Yijue oth Ding, Yunhui oth Pan, Haihong oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:215 year:2021 day:15 month:01 pages:0 https://doi.org/10.1016/j.energy.2020.119078 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 215 2021 15 0115 0 |
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10.1016/j.energy.2020.119078 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001223.pica (DE-627)ELV052286983 (ELSEVIER)S0360-5442(20)32185-X DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Chen, Lin verfasserin aut Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Wang, Huimin oth Liu, Bohao oth Wang, Yijue oth Ding, Yunhui oth Pan, Haihong oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:215 year:2021 day:15 month:01 pages:0 https://doi.org/10.1016/j.energy.2020.119078 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 215 2021 15 0115 0 |
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10.1016/j.energy.2020.119078 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001223.pica (DE-627)ELV052286983 (ELSEVIER)S0360-5442(20)32185-X DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Chen, Lin verfasserin aut Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Wang, Huimin oth Liu, Bohao oth Wang, Yijue oth Ding, Yunhui oth Pan, Haihong oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:215 year:2021 day:15 month:01 pages:0 https://doi.org/10.1016/j.energy.2020.119078 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 215 2021 15 0115 0 |
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10.1016/j.energy.2020.119078 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001223.pica (DE-627)ELV052286983 (ELSEVIER)S0360-5442(20)32185-X DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Chen, Lin verfasserin aut Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. Wang, Huimin oth Liu, Bohao oth Wang, Yijue oth Ding, Yunhui oth Pan, Haihong oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:215 year:2021 day:15 month:01 pages:0 https://doi.org/10.1016/j.energy.2020.119078 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 215 2021 15 0115 0 |
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Enthalten in Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion Amsterdam [u.a.] volume:215 year:2021 day:15 month:01 pages:0 |
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Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
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Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. 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A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. 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battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation |
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Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation |
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Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. |
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Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. |
| abstract_unstemmed |
Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. |
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