Nonlinear Observer Designs for State-of-Charge Estimation of Lithium-ion Batteries

被引:0
|
作者
Dey, Satadru [1 ]
Ayalew, Beshah [1 ]
机构
[1] Clemson Univ, Int Ctr Automot Res, Greenville, SC 29607 USA
关键词
SYSTEMS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
State-of-Charge (SOC) information is very crucial for the control, diagnostics and monitoring of Li-ion cells/batteries. Compared to conventional data-driven or equivalent circuit models often employed in battery management systems, electrochemical battery models have the potential to give physically accurate the SOC information by tracking the Li-ion concentration in each electrode. In this paper, two nonlinear observer designs are presented to estimate Li-ion battery State-of-Charge based on reductions of an electrochemical model. The first observer design uses a constant gain Luenberger structure whereas the second one improves it by weighing the gain with the output Jacobian. For both observer designs, Lyapunov's direct method is applied and the design problems are converted to solving LMIs. Simulation results are included to demonstrate the effectiveness of both observer designs.
引用
下载
收藏
页码:248 / 253
页数:6
相关论文
共 50 条
  • [41] A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
    Ren, Xiaoqing
    Liu, Shulin
    Yu, Xiaodong
    Dong, Xia
    ENERGY, 2021, 234
  • [42] State-of-Charge estimation from a thermal-electrochemical model of lithium-ion batteries
    Tang, Shu-Xia
    Camacho-Solorio, Leobardo
    Wang, Yebin
    Krstic, Miroslav
    AUTOMATICA, 2017, 83 : 206 - 219
  • [43] State-of-charge estimation method for lithium-ion batteries based on competitive SIR model
    Xu, Guimin
    FRONTIERS IN ENERGY RESEARCH, 2022, 10
  • [44] State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks
    Wang, Yu-Chun
    Shao, Nei-Chun
    Chen, Guan-Wen
    Hsu, Wei-Shen
    Wu, Shun-Chi
    SENSORS, 2022, 22 (16)
  • [45] Towards fast embedded moving horizon state-of-charge estimation for lithium-ion batteries
    Wan, Yiming
    Du, Songtao
    Yan, Jiayu
    Wang, Zhuo
    JOURNAL OF ENERGY STORAGE, 2024, 78
  • [46] Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries
    Song, Xiangbao
    Yang, Fangfang
    Wang, Dong
    Tsui, Kwok-Leung
    IEEE ACCESS, 2019, 7 : 88894 - 88902
  • [47] Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles
    Li, Xiaoyu
    Wang, Zhenpo
    Zhang, Lei
    ENERGY, 2019, 174 : 33 - 44
  • [48] Evaluation of the Model-based State-of-Charge Estimation Methods for Lithium-ion Batteries
    Zhang, Yongzhi
    Xiong, Rui
    He, Hongwen
    2016 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO (ITEC), 2016,
  • [49] State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
    Yang, Fangfang
    Li, Weihua
    Li, Chuan
    Miao, Qiang
    ENERGY, 2019, 175 : 66 - 75
  • [50] A Combined Data-Model Method for State-of-Charge Estimation of Lithium-Ion Batteries
    Ni, Zichuan
    Yang, Ying
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71