TY - GEN
T1 - An overview of online implementable soc estimation methods for lithium-ion batteries
AU - Meng, Jinhao
AU - Ricco, Mattia
AU - Luo, Guangzhao
AU - Swierczynski, Maciej
AU - Stroe, Daniel Ioan
AU - Stroe, Ana Irina
AU - Teodorescu, Remus
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/11
Y1 - 2017/7/11
N2 - With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-Time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-Time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.
AB - With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-Time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-Time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.
KW - Comparison
KW - EV
KW - Lithium-ion battery
KW - Online implementation
KW - SOC
UR - http://www.scopus.com/inward/record.url?scp=85027699708&partnerID=8YFLogxK
U2 - 10.1109/OPTIM.2017.7975030
DO - 10.1109/OPTIM.2017.7975030
M3 - 会议稿件
AN - SCOPUS:85027699708
T3 - Proceedings - 2017 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017
SP - 573
EP - 580
BT - Proceedings - 2017 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017
Y2 - 25 May 2017 through 27 May 2017
ER -
