Fractional-order modeling and parameter identification for lithium-ion batteries

被引:181
|
作者
Wang, Baojin [1 ,2 ]
Li, Shengbo Eben [2 ,3 ]
Peng, Huei [2 ]
Liu, Zhiyuan [1 ]
机构
[1] Harbin Inst Technol, Dept Control Sci & Engn, Harbin 150001, Peoples R China
[2] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
[3] Tsinghua Univ, Dept Automot Engn, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China
关键词
Lithium-ion batteries; Fractional-order model; Differentiation order identification; Electrochemical impedance spectroscopy; Hybrid multi-swarm particle swarm optimization; ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY; EQUIVALENT-CIRCUIT MODELS; SINGLE-PARTICLE MODEL; CHARGE; DISCHARGE; TIME; MANAGEMENT; EXTENSION; STATE;
D O I
10.1016/j.jpowsour.2015.05.059
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This paper presents a fractional-order model (FOM) for lithium-ion batteries and its parameter identification using time-domain test data. The FOM is derived from a modified Randles model and takes the form of an equivalent circuit model with free non-integer differentiation orders. The coefficients and differentiation orders of the FOM are identified by hybrid multi-swarm particle swarm optimization. The influence of approximation degree on model accuracy is discussed. Battery datasets under a range of conditions are used to analyze model performance. The accuracy and robustness of the FOM are benchmarked against the commonly used first-order RC equivalent circuit model. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:151 / 161
页数:11
相关论文
共 50 条
  • [41] Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation
    Wang, Yujie
    Li, Mince
    Chen, Zonghai
    APPLIED ENERGY, 2020, 278
  • [42] Fractional-order modeling and parameter estimation of lithium-ion battery via multi-strategy and mean factor differential evolution
    Yu, Kunjie
    Zhang, Kai
    Zhong, Yazhe
    Yang, Duo
    Liang, Jing
    MEASUREMENT, 2025, 246
  • [43] Linear parameter variation modeling and online model identification algorithms for lithium-ion batteries
    Jiang, Yuchao
    Hou, Jie
    Li, Penghua
    Xiang, Sheng
    Chen, Liping
    Ilolov, Mamadsho
    Shokir, Farhod
    Xie, Lirong
    39TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION, YAC 2024, 2024, : 1645 - 1651
  • [44] A comparative study of modeling and parameter identification for lithium-ion batteries in energy storage systems
    Fan, Yuan
    Zhang, Zepei
    Yang, Guozhi
    Pan, Tianhong
    Tian, Jiaqiang
    Li, Mince
    Liu, Xinghua
    Wang, Peng
    MEASUREMENT, 2025, 243
  • [45] State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter
    Wei, Ying
    Ling, Liuyi
    IEEE ACCESS, 2022, 10 : 37131 - 37148
  • [46] An adaptive fractional-order extended Kalman filtering approach for estimating state of charge of lithium-ion batteries
    Song, Dandan
    Gao, Zhe
    Chai, Haoyu
    Jiao, Zhiyuan
    Journal of Energy Storage, 2024, 85
  • [47] An improved unscented Kalman filter for SOC estimation of lithium-ion batteries based on fractional-order model
    Wang, Yingying
    Ding, Jie
    Tu, Taotao
    IONICS, 2025,
  • [48] State-of-health diagnosis of lithium-ion batteries using the fractional-order electrochemical impedance model
    Laribi, Slimane
    Arama, Fatima Zohra
    Mammar, Khaled
    Aoun, Nouar
    Ghaitaoui, Touhami
    Hamouda, Messaoud
    MEASUREMENT, 2023, 211
  • [49] State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter
    Chen, Liping
    Chen, Yu
    Lopes, Antonio M.
    Kong, Huifang
    Wu, Ranchao
    FRACTAL AND FRACTIONAL, 2021, 5 (03)
  • [50] An adaptive fractional-order extended Kalman filtering approach for estimating state of charge of lithium-ion batteries
    Song, Dandan
    Gao, Zhe
    Chai, Haoyu
    Jiao, Zhiyuan
    JOURNAL OF ENERGY STORAGE, 2024, 85