State of health estimation of lithium-ion batteries based on the constant voltage charging curve

被引:141
作者
Wang, Zengkai [1 ]
Zeng, Shengkui [1 ,2 ]
Guo, Jianbin [1 ,2 ]
Qin, Taichun [1 ]
机构
[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China
[2] Sci & Technol Reliabil & Environm Engn Lab, Beijing 100191, Peoples R China
关键词
Lithium-ion battery; State of health; Constant voltage charging; Equivalent circuit model; PARTICLE SWARM OPTIMIZATION; USEFUL LIFE PREDICTION; CAPACITY ESTIMATION; CYCLE LIFE; MODEL; DEGRADATION; PERFORMANCE; EXTRACTION; PARAMETERS; REGRESSION;
D O I
10.1016/j.energy.2018.11.008
中图分类号
O414.1 [热力学];
学科分类号
摘要
State of health estimation is critical for ensuring the safety and dependability of lithium-ion batteries. In practical usage, batteries are seldom completely discharged. With a constant current-constant voltage charging mode, the incomplete discharging process influences the initial charging voltage and the charging time of the subsequent constant current charging, greatly hindering the applications of many traditional health indicators that require a full cycling process. However, the charging data of the constant voltage charging is fully reserved, and is not affected by the previous incomplete discharging process. Furthermore, the charging current curve during the constant voltage profile is discovered to relate with the battery state of health in this study. Therefore, a new health indicator is extracted only from the monitoring parameters of the constant voltage profile for state of health estimation. The battery aging phenomena during the constant voltage profile are firstly characterized by the equivalent circuit model, and a new indicator is then constructed. A framework for the online extraction of this indicator of is proposed. Additionally, the correlation analysis and performance assessment prove the adaptability and effectiveness of the proposed method for estimating state of health of lithium-ion batteries. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:661 / 669
页数:9
相关论文
共 41 条
[21]   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
[22]   A generalized cycle life model of rechargeable Li-ion batteries [J].
Ning, G ;
White, RE ;
Popov, BN .
ELECTROCHIMICA ACTA, 2006, 51 (10) :2012-2022
[23]   State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model [J].
Pan, Haihong ;
Lu, Zhiqiang ;
Lin, Weilong ;
Li, Junzi ;
Chen, Lin .
ENERGY, 2017, 138 :764-775
[24]   Open circuit voltage characterization of lithium-ion batteries [J].
Pattipati, B. ;
Balasingam, B. ;
Avvari, G. V. ;
Pattipati, K. R. ;
Bar-Shalom, Y. .
JOURNAL OF POWER SOURCES, 2014, 269 :317-333
[25]  
Saha B., 2007, BATTERY DATA SET NAS
[26]   Forecasting urban water demand: A meta-regression analysis [J].
Sebri, Maamar .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2016, 183 :777-785
[27]   State-of-health estimation of LiFePO4/graphite batteries based on a model using differential capacity [J].
Torai, Soichiro ;
Nakagomi, Masaru ;
Yoshitake, Satoshi ;
Yamaguchi, Shuichiro ;
Oyama, Noboru .
JOURNAL OF POWER SOURCES, 2016, 306 :62-69
[28]   Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application [J].
Waag, Wladislaw ;
Kaebitz, Stefan ;
Sauer, Dirk Uwe .
APPLIED ENERGY, 2013, 102 :885-897
[29]   On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis [J].
Wang, Limei ;
Pan, Chaofeng ;
Liu, Liang ;
Cheng, Yong ;
Zhao, Xiuliang .
APPLIED ENERGY, 2016, 168 :465-472
[30]   Probability based remaining capacity estimation using data-driven and neural network model [J].
Wang, Yujie ;
Yang, Duo ;
Zhang, Xu ;
Chen, Zonghai .
JOURNAL OF POWER SOURCES, 2016, 315 :199-208