A review on prognostics approaches for remaining useful life of lithium-ion battery

被引:34
|
作者
Su, C. [1 ]
Chen, H. J. [1 ]
机构
[1] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
UNSCENTED KALMAN FILTER; CHARGE ESTIMATION; HEALTH ESTIMATION; NEURAL-NETWORK; WIENER-PROCESS; STATE; MODEL; PERFORMANCE; PREDICTION; ALGORITHM;
D O I
10.1088/1755-1315/93/1/012040
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lithium-ion (Li-ion) battery is a core component for various industrial systems, including satellite, spacecraft and electric vehicle, etc. The mechanism of performance degradation and remaining useful life (RUL) estimation correlate closely to the operating state and reliability of the aforementioned systems. Furthermore, RUL prediction of Li-ion battery is crucial for the operation scheduling, spare parts management and maintenance decision for such kinds of systems. In recent years, performance degradation prognostics and RUL estimation approaches have become a focus of the research concerning with Li-ion battery. This paper summarizes the approaches used in Li-ion battery RUL estimation. Three categories are classified accordingly, i.e. model-based approach, data-based approach and hybrid approach. The key issues and future trends for battery RUL estimation are also discussed.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Data-driven Prognostics and Remaining Useful Life Estimation for Lithium-ion Battery: A Review
    LIU Datong
    ZHOU Jianbao
    PENG Yu
    [J]. Instrumentation, 2014, 01 (01) : 59 - 70
  • [2] Uncertainty Quantification of Fusion Prognostics for Lithium-ion Battery Remaining Useful Life Estimation
    Liu, Datong
    Luo, Yue
    Guo, Limeng
    Peng, Yu
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT, 2013,
  • [3] Indirect remaining useful life prognostics for lithium-ion batteries
    Li, Lianbing
    Zhu, Yazun
    Wang, Linglong
    Yue, Donghua
    Li, Duo
    [J]. 2018 24TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC' 18), 2018, : 725 - 729
  • [4] A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries
    Ge, Ming-Feng
    Liu, Yiben
    Jiang, Xingxing
    Liu, Jie
    [J]. MEASUREMENT, 2021, 174
  • [5] Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm
    Li, Lyu
    Song, Yuchen
    Peng, Yu
    Liu, Datong
    [J]. 2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018), 2018, : 1094 - 1100
  • [6] Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction
    Lui, Yu Hui
    Li, Meng
    Downey, Austin
    Shen, Sheng
    Nemani, Venkat Pavan
    Ye, Hui
    VanElzen, Collette
    Jain, Gaurav
    Hu, Shan
    Laflamme, Simon
    Hu, Chao
    [J]. Journal of Power Sources, 2021, 485
  • [7] Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction
    Lui, Yu Hui
    Li, Meng
    Downey, Austin
    Shen, Sheng
    Nemani, Venkat Pavan
    Ye, Hui
    VanElzen, Collette
    Jain, Gaurav
    Hu, Shan
    Laflamme, Simon
    Hu, Chao
    [J]. JOURNAL OF POWER SOURCES, 2021, 485
  • [8] Prediction of Remaining Useful Life of Lithium-ion Battery Based on UKF
    Huang, Mengtao
    Zhang, Qibo
    [J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 4502 - 4506
  • [9] Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
    Wu, Lifeng
    Fu, Xiaohui
    Guan, Yong
    [J]. APPLIED SCIENCES-BASEL, 2016, 6 (06):
  • [10] Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model
    Tang, Xuliang
    Wan, Heng
    Wang, Weiwen
    Gu, Mengxu
    Wang, Linfeng
    Gan, Linfeng
    [J]. SUSTAINABILITY, 2023, 15 (07)