Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

被引：7

作者：

Zhang, Xiaohua ^{[1
,2
]}

Li, Haolin ^{[1
,3
]}

Zhang, Wenfeng ^{[2
,4
]}

Lopes, Antonio M. ^{[5
]}

Wu, Xiaobo ^{[6
]}

Chen, Liping ^{[6
]}

机构：

[1] Zhongkai Univ Agr & Engn, Coll Automat, Guangzhou 510225, Peoples R China

[2] Guangdong Hong Kong Macao Greater Bay Area Agr Pro, Guangzhou 510225, Peoples R China

[3] Zhongkai Univ Agr & Engn, Coll Sch Mech & Elect Engn, Guangzhou 510225, Peoples R China

[4] Guangdong Agr Prod Cold Chain Transportat & Logist, Guangzhou 510225, Peoples R China

[5] Univ Porto, Fac Engn, LAETA, INEGI, Rua Dr Roberto Frias, P-4200465 Porto, Portugal

[6] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China

来源：

MICROMACHINES | 2023年 / 14卷 / 02期

关键词：

FO equivalent circuit; parameter identification; beetle swarm optimization; OF-CHARGE ESTIMATION; STATE;

D O I：

10.3390/mi14020413

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters' identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO.

引用

页数：10

共 50 条

[31] Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter
Xian, Weiming
Long, Bing
Li, Min
Wang, Houjun
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2014, 63 (01) : 2 - 17
[32] Physics-based parameter identification of an electrochemical model for lithium-ion batteries with two-population optimization method
Tian, Aina
Dong, Kailang
Yang, Xiao-Guang
Wang, Yuqin
He, Luyao
Gao, Yang
Jiang, Jiuchun
APPLIED ENERGY, 2025, 378
[33] State of charge estimation for lithium-ion batteries based on fractional order multiscale algorithm
Guo, Haisheng
Han, Xudong
Yang, Run
Shi, Jinjin
JOURNAL OF ENERGY STORAGE, 2022, 55
[34] CSTR parameter identification and PID control optimization based on improved swarm intelligence algorithm
Wang, Ronglin
Wang, Haibo
Liu, Jieting
Li, Pengtao
Zhao, Chuanzhe
Song, Yadi
ENGINEERING RESEARCH EXPRESS, 2025, 7 (01):
[35] Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization
Wang, Bing-Chuan
He, Yan-Bo
Liu, Jiao
Luo, Biao
ENERGY, 2024, 288
[36] Lithium-ion battery modeling and parameter identification based on fractional theory
Hu, Minghui
Li, Yunxiao
Li, Shuxian
Fu, Chunyun
Qin, Datong
Li, Zonghua
ENERGY, 2018, 165 : 153 - 163
[37] Fifth-order resistance-capacitance-based optimal equivalent circuit model of lithium-ion batteries with improved transient search optimization algorithm
Hasanien, Hany M.
Alqahtani, Ayedh H.
Fahmy, Hend M.
Alharbi, Mohammed
Kim, Jonghoon
ENERGY, 2025, 322
[38] Parameter identification of PMSM based on dung beetle optimization algorithm
Yang, Xiaoliang
Cui, Yuyue
Jia, Lianhua
Sun, Zhihong
Zhang, Peng
Zhao, Jiane
Wang, Rui
ARCHIVES OF ELECTRICAL ENGINEERING, 2023, 72 (04) : 1055 - 1072
[39] An Information Analysis Based Online Parameter Identification Method for Lithium-ion Batteries in Electric Vehicles
Guo, Ruohan
Shen, Weixiang
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2024, 71 (07) : 7095 - 7105
[40] Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries
Zhou, Daming
Zhang, Ke
Ravey, Alexandre
Gao, Fei
Miraoui, Abdellatif
ENERGIES, 2016, 9 (03)

← 1 2 3 4 5 →