Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis

被引:173
作者
Yang, Jufeng [1 ,2 ]
Xia, Bing [2 ,3 ]
Huang, Wenxin [1 ]
Fu, Yuhong [2 ]
Mi, Chris [2 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Dept Elect Engn, 29 Jiangjun St, Nanjing 211106, Jiangsu, Peoples R China
[2] San Diego State Univ, Dept Elect & Comp Engn, 5500 Campanile Dr, San Diego, CA 92182 USA
[3] Univ Calif San Diego, Dept Elect & Comp Engn, 9500 Gilman Dr, La Jolla, CA 92093 USA
基金
美国国家科学基金会;
关键词
Lithium-ion battery; State-of-health (SoH); Constant-current constant-voltage (CCCV) charge; Equivalent circuit model (ECM); Current time constant; PACK CAPACITY ESTIMATION; ON-BOARD STATE; ELECTRIC VEHICLES; PARAMETER-ESTIMATION; MANAGEMENT-SYSTEMS; CYCLE LIFE; MODEL; IDENTIFICATION; HYBRID; ALGORITHM;
D O I
10.1016/j.apenergy.2018.01.010
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
Battery state-of-health (SoH) estimation is a critical function in a well-designed battery management system (BMS). In this paper, the battery SoH is detected based on the dynamic characteristic of the charging current during the constant-voltage (CV) period. Firstly, according to the preliminary analysis of the battery test data, the time constant of CV charging current is proved to be a robust characteristic parameter related to the battery aging. Secondly, the detailed expression of the current time constant is derived based on the first order equivalent circuit model (ECM). Thirdly, the quantitative correlation between the normalized battery capacity and the current time constant is established to indicate the battery SoH. Specifically, for the uncompleted CV charging process, the logarithmic function-based current time constant prediction model and the reference correlation curve are established to identify the battery capacity fading. At last, experimental results showed that regardless of the adopted data size, the correlation identified from one battery can be used to indicate the SoH of other three batteries within 2.5% error bound except a few outliers.
引用
收藏
页码:1589 / 1600
页数:12
相关论文
共 57 条
[21]   A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation [J].
Klass, Verena ;
Behm, Marten ;
Lindbergh, Goran .
JOURNAL OF POWER SOURCES, 2014, 270 :262-272
[22]   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
[23]   A capacity model based on charging process for state of health estimation of lithium ion batteries [J].
Li, Xue ;
Jiang, Jiuchun ;
Wang, Le Yi ;
Chen, Dafen ;
Zhang, Yanru ;
Zhang, Caiping .
APPLIED ENERGY, 2016, 177 :537-543
[24]   A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm [J].
Lin, Cheng ;
Mu, Hao ;
Xiong, Rui ;
Shen, Weixiang .
APPLIED ENERGY, 2016, 166 :76-83
[25]   Estimation of Battery State of Health Using Probabilistic Neural Network [J].
Lin, Ho-Ta ;
Liang, Tsorng-Juu ;
Chen, Shih-Ming .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (02) :679-685
[26]   A review on the key issues for lithium-ion battery management in electric vehicles [J].
Lu, Languang ;
Han, Xuebing ;
Li, Jianqiu ;
Hua, Jianfeng ;
Ouyang, Minggao .
JOURNAL OF POWER SOURCES, 2013, 226 :272-288
[27]   A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery [J].
Ouyang, Minggao ;
Feng, Xuning ;
Han, Xuebing ;
Lu, Languang ;
Li, Zhe ;
He, Xiangming .
APPLIED ENERGY, 2016, 165 :48-59
[28]   Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries [J].
Pei, Lei ;
Wang, Tiansi ;
Lu, Rengui ;
Zhu, Chunbo .
JOURNAL OF POWER SOURCES, 2014, 253 :412-418
[29]   Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter [J].
Perez, Gustavo ;
Garmendia, Maitane ;
Francois Reynaud, Jean ;
Crego, Jon ;
Viscarret, Unai .
APPLIED ENERGY, 2015, 155 :834-845
[30]   Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries [J].
Petzl, Mathias ;
Danzer, Michael A. .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2013, 28 (03) :675-681