High accuracy temperaure-dependent SOC estimation based on real-time parameter identification for rechargeable Li-Ion battery pack

被引:0
作者
Park, Jinhyeong [1 ]
Bae, Hynsu [2 ]
Jang, Sung-Soo [3 ]
Na, Woonki [4 ]
Kim, Jonghoon [1 ]
机构
[1] Chungnam Natl Univ, Elect Engn, Daejeon, South Korea
[2] RIPower, Seoul, South Korea
[3] Korea Aeropspace Res Inst, Daejeon, South Korea
[4] Calif State Univ Los Angeles, Elecet Engn, Los Angeles, CA 90032 USA
来源
THIRTY-FOURTH ANNUAL IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC 2019) | 2019年
关键词
Li-ion battery; Electrical equivalent circuit model; Extended Kalman filter; Online parameter identification; EXTENDED KALMAN FILTER; STATE-OF-CHARGE; HYBRID;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The model-based adaptive control methods such as extended Kalman filter (EKF) are widely used as a typical state of charge (SOC) estimation method for a battery pack. The EKF estimates and corrects the SOC by properly adjusting the control gain, according to a state of the battery pack. However, the performance of the EKF cannot be guaranteed when a temperature of the battery pack is sharply changed. The accuracy of the equivalent circuit model is varied according to the temperature, therefore the performance of the EKF estimation is not consistent. In order to increase the performance of the SOC estimation at various temperatures when using the adaptive control method, techniques for designing accurate systems and the Kalman gain are required. Therefore, in this paper, an observer for accurate parameter identification of the battery pack is designed and the EKF is used to improve the SOC estimation performance at low temperatures.
引用
收藏
页码:2934 / 2938
页数:5
相关论文
共 18 条
[1]  
El Din Menatallla shehab, 2018, IEEE T TRANSPORTATIO, V4
[2]   Bone biomaterials and interactions with stem cells [J].
Gao, Chengde ;
Peng, Shuping ;
Feng, Pei ;
Shuai, Cijun .
BONE RESEARCH, 2017, 5
[3]   A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations [J].
Hannan, M. A. ;
Lipu, M. S. H. ;
Hussain, A. ;
Mohamed, A. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 78 :834-854
[4]   Li-Ion Cell Operation at Low Temperatures [J].
Ji, Yan ;
Zhang, Yancheng ;
Wang, Chao-Yang .
JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2013, 160 (04) :A636-A649
[5]   Nonlinear state of charge estimator for hybrid electric vehicle battery [J].
Kim, Il-Song .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2008, 23 (04) :2027-2034
[6]   Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering [J].
Lee, Jaemoon ;
Nam, Oanyong ;
Cho, B. H. .
JOURNAL OF POWER SOURCES, 2007, 174 (01) :9-15
[7]   State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer [J].
Li, Weilin ;
Liang, Liliuyuan ;
Liu, Wenjie ;
Wu, Xiaohua .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (11) :8557-8565
[8]  
Ma Yan, 2018, IEEE T CONTROL SYSTE
[9]   State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries [J].
Malysz, Pawel ;
Gu, Ran ;
Ye, Jin ;
Yang, Hong ;
Emadi, Ali .
IET ELECTRICAL SYSTEMS IN TRANSPORTATION, 2016, 6 (02) :136-144
[10]   Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online [J].
Ning, Bo ;
Cao, Binggang ;
Wang, Bin ;
Zou, Zhongyue .
ENERGY, 2018, 153 :732-742