Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares

An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC,...

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Autores principales:	CHEN Yushan, QIN Linlin, WU Gang, MAO Junxin
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Journal of Shanghai Jiao Tong University 2020
Materias:	recursive least squares state of charge (soc) online estimation electric vehicle Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/83f105db78eb4e438ce5a3d626cb156e
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id	oai:doaj.org-article:83f105db78eb4e438ce5a3d626cb156e
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spelling	oai:doaj.org-article:83f105db78eb4e438ce5a3d626cb156e2021-11-04T09:34:52ZOnline State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares1006-246710.16183/j.cnki.jsjtu.2020.172https://doaj.org/article/83f105db78eb4e438ce5a3d626cb156e2020-12-01T00:00:00Zhttp://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.172https://doaj.org/toc/1006-2467An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC, a first-order resistor-capacitance (RC) equivalent circuit model is used to describe the external characteristic of batteries. The model parameters are identified by forgetting factor recursive least-squares (FFRLS). Open circuit voltage (OCV) is one of the model parameters, and then SOC can be estimated by the SOC-OCV model. The CALCE battery research group in the University of Maryland has proposed some data, which include the data of LNMC/graphite battery working under dynamic stress test (DST) and Beijing dynamic stress test (BJDST) conditions. These data are used to verify the proposed algorithm. The results show that the estimation error does not exceed 3.419 0% in DST and 4.233 5% in BJDST, which indicates that the proposed method can realize online SOC estimation.CHEN YushanQIN LinlinWU GangMAO JunxinEditorial Office of Journal of Shanghai Jiao Tong Universityarticlerecursive least squaresstate of charge (soc)online estimationelectric vehicleEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156Naval architecture. Shipbuilding. Marine engineeringVM1-989ZHShanghai Jiaotong Daxue xuebao, Vol 54, Iss 12, Pp 1340-1346 (2020)
institution	DOAJ
collection	DOAJ
language	ZH
topic	recursive least squares state of charge (soc) online estimation electric vehicle Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	recursive least squares state of charge (soc) online estimation electric vehicle Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 CHEN Yushan QIN Linlin WU Gang MAO Junxin Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
description	An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC, a first-order resistor-capacitance (RC) equivalent circuit model is used to describe the external characteristic of batteries. The model parameters are identified by forgetting factor recursive least-squares (FFRLS). Open circuit voltage (OCV) is one of the model parameters, and then SOC can be estimated by the SOC-OCV model. The CALCE battery research group in the University of Maryland has proposed some data, which include the data of LNMC/graphite battery working under dynamic stress test (DST) and Beijing dynamic stress test (BJDST) conditions. These data are used to verify the proposed algorithm. The results show that the estimation error does not exceed 3.419 0% in DST and 4.233 5% in BJDST, which indicates that the proposed method can realize online SOC estimation.
format	article
author	CHEN Yushan QIN Linlin WU Gang MAO Junxin
author_facet	CHEN Yushan QIN Linlin WU Gang MAO Junxin
author_sort	CHEN Yushan
title	Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
title_short	Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
title_full	Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
title_fullStr	Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
title_full_unstemmed	Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
title_sort	online state of charge estimation for battery in electric vehicles based on forgetting factor recursive least squares
publisher	Editorial Office of Journal of Shanghai Jiao Tong University
publishDate	2020
url	https://doaj.org/article/83f105db78eb4e438ce5a3d626cb156e
work_keys_str_mv	AT chenyushan onlinestateofchargeestimationforbatteryinelectricvehiclesbasedonforgettingfactorrecursiveleastsquares AT qinlinlin onlinestateofchargeestimationforbatteryinelectricvehiclesbasedonforgettingfactorrecursiveleastsquares AT wugang onlinestateofchargeestimationforbatteryinelectricvehiclesbasedonforgettingfactorrecursiveleastsquares AT maojunxin onlinestateofchargeestimationforbatteryinelectricvehiclesbasedonforgettingfactorrecursiveleastsquares
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Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares

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