A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions

The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation meth...
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Gespeichert in:

Autor*in:	Nan Zhou [verfasserIn] Hong Liang [verfasserIn] Jing Cui [verfasserIn] Zeyu Chen [verfasserIn] Zhiyuan Fang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network

Übergeordnetes Werk:	In: Frontiers in Energy Research - Frontiers Media S.A., 2014, 9(2021)
Übergeordnetes Werk:	volume:9 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fenrg.2021.790295

Katalog-ID:	DOAJ063006855

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allfields	10.3389/fenrg.2021.790295 doi (DE-627)DOAJ063006855 (DE-599)DOAJ63f1c25a7d6f45019a2a3df762460fd4 DE-627 ger DE-627 rakwb eng Nan Zhou verfasserin aut A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions. electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network General Works A Nan Zhou verfasserin aut Nan Zhou verfasserin aut Hong Liang verfasserin aut Jing Cui verfasserin aut Zeyu Chen verfasserin aut Zeyu Chen verfasserin aut Zhiyuan Fang verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.790295 kostenfrei https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
spelling	10.3389/fenrg.2021.790295 doi (DE-627)DOAJ063006855 (DE-599)DOAJ63f1c25a7d6f45019a2a3df762460fd4 DE-627 ger DE-627 rakwb eng Nan Zhou verfasserin aut A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions. electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network General Works A Nan Zhou verfasserin aut Nan Zhou verfasserin aut Hong Liang verfasserin aut Jing Cui verfasserin aut Zeyu Chen verfasserin aut Zeyu Chen verfasserin aut Zhiyuan Fang verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.790295 kostenfrei https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
allfields_unstemmed	10.3389/fenrg.2021.790295 doi (DE-627)DOAJ063006855 (DE-599)DOAJ63f1c25a7d6f45019a2a3df762460fd4 DE-627 ger DE-627 rakwb eng Nan Zhou verfasserin aut A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions. electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network General Works A Nan Zhou verfasserin aut Nan Zhou verfasserin aut Hong Liang verfasserin aut Jing Cui verfasserin aut Zeyu Chen verfasserin aut Zeyu Chen verfasserin aut Zhiyuan Fang verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.790295 kostenfrei https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
allfieldsGer	10.3389/fenrg.2021.790295 doi (DE-627)DOAJ063006855 (DE-599)DOAJ63f1c25a7d6f45019a2a3df762460fd4 DE-627 ger DE-627 rakwb eng Nan Zhou verfasserin aut A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions. electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network General Works A Nan Zhou verfasserin aut Nan Zhou verfasserin aut Hong Liang verfasserin aut Jing Cui verfasserin aut Zeyu Chen verfasserin aut Zeyu Chen verfasserin aut Zhiyuan Fang verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.790295 kostenfrei https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
allfieldsSound	10.3389/fenrg.2021.790295 doi (DE-627)DOAJ063006855 (DE-599)DOAJ63f1c25a7d6f45019a2a3df762460fd4 DE-627 ger DE-627 rakwb eng Nan Zhou verfasserin aut A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions. electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network General Works A Nan Zhou verfasserin aut Nan Zhou verfasserin aut Hong Liang verfasserin aut Jing Cui verfasserin aut Zeyu Chen verfasserin aut Zeyu Chen verfasserin aut Zhiyuan Fang verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.790295 kostenfrei https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
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authorswithroles_txt_mv	Nan Zhou @@aut@@ Hong Liang @@aut@@ Jing Cui @@aut@@ Zeyu Chen @@aut@@ Zhiyuan Fang @@aut@@
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author	Nan Zhou
spellingShingle	Nan Zhou misc electric vehicle misc lithium-ion battery misc state of charge estimation misc extended kalman filter misc neural network misc General Works misc A A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions
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topic_title	A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions electric vehicle lithium-ion battery state of charge estimation extended kalman filter neural network
topic	misc electric vehicle misc lithium-ion battery misc state of charge estimation misc extended kalman filter misc neural network misc General Works misc A
topic_unstemmed	misc electric vehicle misc lithium-ion battery misc state of charge estimation misc extended kalman filter misc neural network misc General Works misc A
topic_browse	misc electric vehicle misc lithium-ion battery misc state of charge estimation misc extended kalman filter misc neural network misc General Works misc A
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title	A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions
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title_full	A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions
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title_sort	fusion-based method of state-of-charge online estimation for lithium-ion batteries under low capacity conditions
title_auth	A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions
abstract	The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.
abstractGer	The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.
abstract_unstemmed	The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.
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title_short	A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions
url	https://doi.org/10.3389/fenrg.2021.790295 https://doaj.org/article/63f1c25a7d6f45019a2a3df762460fd4 https://www.frontiersin.org/articles/10.3389/fenrg.2021.790295/full https://doaj.org/toc/2296-598X
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