Advances in the Study of Techniques to Determine the Lithium-Ion Battery's State of Charge

被引:8
作者
Liu, Xinyue [1 ,2 ]
Gao, Yang [1 ,3 ]
Marma, Kyamra [4 ]
Miao, Yu [1 ,2 ]
Liu, Lin [4 ]
机构
[1] Ningxia Engn Res Ctr Hybrid Mfg Syst, 204th Wenchang North St, Yinchuan 750021, Peoples R China
[2] North Minzu Univ, Sch Elect & Informat Engn, 204th Wenchang North St, Yinchuan 750021, Peoples R China
[3] North Minzu Univ, Coll Mechatron Engn, 204 Wenchang St North, Yinchuan 750021, Peoples R China
[4] Univ Kansas, Dept Mech Engn, 3138 Learned Hall, 1530 W 15th St, Lawrence, KS 66045 USA
关键词
lithium-ion batteries; SOC; influencing factors; estimation; SOLID-ELECTROLYTE INTERPHASE; EQUIVALENT-CIRCUIT MODELS; SOC ESTIMATION; LITHIUM/POLYMER BATTERY; ELECTROCHEMICAL MODEL; PARAMETERS; DISCHARGE; PREDICTION; PARTICLE; TIME;
D O I
10.3390/en17071643
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
This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.
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页数:16
相关论文
共 81 条
  • [61] Application of Life Cycle Assessment to Lithium Ion Batteries in the Automotive Sector
    Tolomeo, Rosario
    De Feo, Giovanni
    Adami, Renata
    Osseo, Libero Sesti
    [J]. SUSTAINABILITY, 2020, 12 (11)
  • [62] Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter
    Wadi, Ali
    Abdel-Hafez, Mamoun
    Hussein, Ala A.
    [J]. ENERGIES, 2022, 15 (10)
  • [63] Application of electrochemical impedance spectroscopy in battery management system: State of charge estimation for aging batteries
    Wang, Lin
    Zhao, Xiaowei
    Deng, Zhongwei
    Yang, Lin
    [J]. JOURNAL OF ENERGY STORAGE, 2023, 57
  • [64] An electrochemical-mechanical coupled multi-scale modeling method and full-field stress distribution of lithium-ion battery
    Wang, Yanan
    Ni, Ruke
    Jiang, Xingbao
    Yin, Mingyue
    Zhang, Dejun
    Xie, Zongfa
    [J]. APPLIED ENERGY, 2023, 347
  • [65] Xiang-Wu Yan, 2018, Journal of Physics: Conference Series, V1087, DOI 10.1088/1742-6596/1087/5/052027
  • [66] Online state-of-charge estimation refining method for battery energy storage system using historical operating data
    Xiao, Lizhong
    Li, Xining
    Jiang, Quanyuan
    Geng, Guangchao
    [J]. JOURNAL OF ENERGY STORAGE, 2023, 57
  • [67] A retrospective on lithium-ion batteries
    Xie, Jing
    Lu, Yi-Chun
    [J]. NATURE COMMUNICATIONS, 2020, 11 (01)
  • [68] State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter
    Xing, Jie
    Wu, Peng
    [J]. SUSTAINABILITY, 2021, 13 (09)
  • [69] Open circuit voltage and state of charge online estimation for lithium ion batteries
    Xiong, Rui
    Yu, Quanqing
    Wang, Le Yi
    [J]. PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY, 2017, 142 : 1902 - 1907
  • [70] High-Energy Lithium-Ion Batteries: Recent Progress and a Promising Future in Applications
    Xu, Jingjing
    Cai, Xingyun
    Cai, Songming
    Shao, Yaxin
    Hu, Chao
    Lu, Shirong
    Ding, Shujiang
    [J]. ENERGY & ENVIRONMENTAL MATERIALS, 2023, 6 (05)