A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery

Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to...
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Autor*in:	He, Lin [verfasserIn] Hu, Xingwen [verfasserIn] Yin, Guangwei [verfasserIn] Wang, Guoqiang [verfasserIn] Shao, Xingguo [verfasserIn] Liu, Jichao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter

Übergeordnetes Werk:	Enthalten in: Energy - Amsterdam [u.a.] : Elsevier Science, 1976, 288
Übergeordnetes Werk:	volume:288

DOI / URN:	10.1016/j.energy.2023.129701

Katalog-ID:	ELV066442869

Internformat


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024	7		\|a 10.1016/j.energy.2023.129701 \|2 doi
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100	1		\|a He, Lin \|e verfasserin \|0 (orcid)0000-0002-1623-3488 \|4 aut
245	1	0	\|a A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
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520			\|a Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.
650		4	\|a Battery current error
650		4	\|a Equivalent circuit model
650		4	\|a Battery model parameters
650		4	\|a Battery nominal capacity
650		4	\|a Extended Kalman filter
700	1		\|a Hu, Xingwen \|e verfasserin \|4 aut
700	1		\|a Yin, Guangwei \|e verfasserin \|4 aut
700	1		\|a Wang, Guoqiang \|e verfasserin \|4 aut
700	1		\|a Shao, Xingguo \|e verfasserin \|4 aut
700	1		\|a Liu, Jichao \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Energy \|d Amsterdam [u.a.] : Elsevier Science, 1976 \|g 288 \|h Online-Ressource \|w (DE-627)320597903 \|w (DE-600)2019804-8 \|w (DE-576)116451815 \|x 1873-6785 \|7 nnns
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912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 50.70 \|j Energie: Allgemeines \|q VZ
951			\|a AR
952			\|d 288

Indexfelder

author_variant	l h lh x h xh g y gy g w gw x s xs j l jl
matchkey_str	article:18736785:2023----::cretyaisoeadrprinlnerlbevrosaefhresi
hierarchy_sort_str	2023
bklnumber	50.70
publishDate	2023
allfields	10.1016/j.energy.2023.129701 doi (DE-627)ELV066442869 (ELSEVIER)S0360-5442(23)03095-5 DE-627 ger DE-627 rda eng 600 VZ 50.70 bkl He, Lin verfasserin (orcid)0000-0002-1623-3488 aut A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge. Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter Hu, Xingwen verfasserin aut Yin, Guangwei verfasserin aut Wang, Guoqiang verfasserin aut Shao, Xingguo verfasserin aut Liu, Jichao verfasserin aut Enthalten in Energy Amsterdam [u.a.] : Elsevier Science, 1976 288 Online-Ressource (DE-627)320597903 (DE-600)2019804-8 (DE-576)116451815 1873-6785 nnns volume:288 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.70 Energie: Allgemeines VZ AR 288
spelling	10.1016/j.energy.2023.129701 doi (DE-627)ELV066442869 (ELSEVIER)S0360-5442(23)03095-5 DE-627 ger DE-627 rda eng 600 VZ 50.70 bkl He, Lin verfasserin (orcid)0000-0002-1623-3488 aut A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge. Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter Hu, Xingwen verfasserin aut Yin, Guangwei verfasserin aut Wang, Guoqiang verfasserin aut Shao, Xingguo verfasserin aut Liu, Jichao verfasserin aut Enthalten in Energy Amsterdam [u.a.] : Elsevier Science, 1976 288 Online-Ressource (DE-627)320597903 (DE-600)2019804-8 (DE-576)116451815 1873-6785 nnns volume:288 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.70 Energie: Allgemeines VZ AR 288
allfields_unstemmed	10.1016/j.energy.2023.129701 doi (DE-627)ELV066442869 (ELSEVIER)S0360-5442(23)03095-5 DE-627 ger DE-627 rda eng 600 VZ 50.70 bkl He, Lin verfasserin (orcid)0000-0002-1623-3488 aut A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge. Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter Hu, Xingwen verfasserin aut Yin, Guangwei verfasserin aut Wang, Guoqiang verfasserin aut Shao, Xingguo verfasserin aut Liu, Jichao verfasserin aut Enthalten in Energy Amsterdam [u.a.] : Elsevier Science, 1976 288 Online-Ressource (DE-627)320597903 (DE-600)2019804-8 (DE-576)116451815 1873-6785 nnns volume:288 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.70 Energie: Allgemeines VZ AR 288
allfieldsGer	10.1016/j.energy.2023.129701 doi (DE-627)ELV066442869 (ELSEVIER)S0360-5442(23)03095-5 DE-627 ger DE-627 rda eng 600 VZ 50.70 bkl He, Lin verfasserin (orcid)0000-0002-1623-3488 aut A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge. Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter Hu, Xingwen verfasserin aut Yin, Guangwei verfasserin aut Wang, Guoqiang verfasserin aut Shao, Xingguo verfasserin aut Liu, Jichao verfasserin aut Enthalten in Energy Amsterdam [u.a.] : Elsevier Science, 1976 288 Online-Ressource (DE-627)320597903 (DE-600)2019804-8 (DE-576)116451815 1873-6785 nnns volume:288 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.70 Energie: Allgemeines VZ AR 288
allfieldsSound	10.1016/j.energy.2023.129701 doi (DE-627)ELV066442869 (ELSEVIER)S0360-5442(23)03095-5 DE-627 ger DE-627 rda eng 600 VZ 50.70 bkl He, Lin verfasserin (orcid)0000-0002-1623-3488 aut A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge. Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter Hu, Xingwen verfasserin aut Yin, Guangwei verfasserin aut Wang, Guoqiang verfasserin aut Shao, Xingguo verfasserin aut Liu, Jichao verfasserin aut Enthalten in Energy Amsterdam [u.a.] : Elsevier Science, 1976 288 Online-Ressource (DE-627)320597903 (DE-600)2019804-8 (DE-576)116451815 1873-6785 nnns volume:288 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.70 Energie: Allgemeines VZ AR 288
language	English
source	Enthalten in Energy 288 volume:288
sourceStr	Enthalten in Energy 288 volume:288
format_phy_str_mv	Article
bklname	Energie: Allgemeines
institution	findex.gbv.de
topic_facet	Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter
dewey-raw	600
isfreeaccess_bool	false
container_title	Energy
authorswithroles_txt_mv	He, Lin @@aut@@ Hu, Xingwen @@aut@@ Yin, Guangwei @@aut@@ Wang, Guoqiang @@aut@@ Shao, Xingguo @@aut@@ Liu, Jichao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320597903
dewey-sort	3600
id	ELV066442869
language_de	englisch
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author	He, Lin
spellingShingle	He, Lin ddc 600 bkl 50.70 misc Battery current error misc Equivalent circuit model misc Battery model parameters misc Battery nominal capacity misc Extended Kalman filter A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
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topic_title	600 VZ 50.70 bkl A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery Battery current error Equivalent circuit model Battery model parameters Battery nominal capacity Extended Kalman filter
topic	ddc 600 bkl 50.70 misc Battery current error misc Equivalent circuit model misc Battery model parameters misc Battery nominal capacity misc Extended Kalman filter
topic_unstemmed	ddc 600 bkl 50.70 misc Battery current error misc Equivalent circuit model misc Battery model parameters misc Battery nominal capacity misc Extended Kalman filter
topic_browse	ddc 600 bkl 50.70 misc Battery current error misc Equivalent circuit model misc Battery model parameters misc Battery nominal capacity misc Extended Kalman filter
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title	A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
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title_full	A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
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author_browse	He, Lin Hu, Xingwen Yin, Guangwei Wang, Guoqiang Shao, Xingguo Liu, Jichao
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title_sort	a current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
title_auth	A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
abstract	Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.
abstractGer	Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.
abstract_unstemmed	Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.
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title_short	A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery
remote_bool	true
author2	Hu, Xingwen Yin, Guangwei Wang, Guoqiang Shao, Xingguo Liu, Jichao
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doi_str	10.1016/j.energy.2023.129701
up_date	2024-07-06T17:46:37.637Z
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A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery

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