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.
引用
收藏
页数:16
相关论文
共 81 条
  • [1] A Scaling Approach for Improved Open Circuit Voltage Modeling in Li-ion Batteries
    Ahmed, Mostafa Shaban
    Balasingam, Balakumar
    [J]. 2019 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2019,
  • [2] On-line WSN SoC estimation using Gaussian Process Regression: An Adaptive Machine Learning Approach
    Ali, Omer
    Ishak, Mohamad Khairi
    Ahmed, Ashraf Bani
    Salleh, Mohd Fadzli Mohd
    Ooi, Chia Ai
    Khan, Muhammad Firdaus Akbar Jalaludin
    Khan, Imran
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2022, 61 (12) : 9831 - 9848
  • [3] Estimation of state of charge for lithium-ion batteries - A Review
    Attanayaka, A. M. S. M. H. S.
    Karunadasa, J. P.
    Hemapala, K. T. M. U.
    [J]. AIMS ENERGY, 2019, 7 (02) : 186 - 210
  • [4] Aging effect on the variation of Li-ion battery resistance as function of temperature and state of charge
    Barcellona, Simone
    Colnago, Silvia
    Dotelli, Giovanni
    Latorrata, Saverio
    Piegari, Luigi
    [J]. JOURNAL OF ENERGY STORAGE, 2022, 50
  • [5] Battery SOC estimation from EIS data based on machine learning and equivalent circuit model
    Buchicchio, Emanuele
    De Angelis, Alessio
    Santoni, Francesco
    Carbone, Paolo
    Bianconi, Francesco
    Smeraldi, Fabrizio
    [J]. ENERGY, 2023, 283
  • [6] State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
    Chen, Cheng
    Xiong, Rui
    Yang, Ruixin
    Shen, Weixiang
    Sun, Fengchun
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 234 : 1153 - 1164
  • [7] A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current
    Chen, Jinglong
    Wang, Rixin
    Li, Yuqing
    Xu, Minqiang
    [J]. IEEE ACCESS, 2019, 7 : 186217 - 186227
  • [8] State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter
    Chen, Lin
    Yu, Wentao
    Cheng, Guoyang
    Wang, Jierui
    [J]. ENERGY, 2023, 271
  • [9] Estimation the internal resistance of lithium-ion-battery using a multi-factor dynamic internal resistance model with an error compensation strategy
    Chen, Lin
    Zhang, Mo
    Ding, Yunhui
    Wu, Shuxiao
    Li, Yijing
    Liang, Gang
    Li, Hao
    Pan, Haihong
    [J]. ENERGY REPORTS, 2021, 7 : 3050 - 3059
  • [10] Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter
    Cheng, Ze
    Lv, Jikao
    Liu, Yanli
    Yan, Zhihao
    [J]. JOURNAL OF APPLIED MATHEMATICS, 2014,