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

[21] Parameter Identification of Fractional-order Model for Lithium-ion Batteries via a Neighborhood Differential Evolution Algorithm
Yu, Kun-Jie
Zhong, Ya-Zhe
Yang, Duo
Liang, Jing
Liao, Yue-Feng
2022 4TH INTERNATIONAL CONFERENCE ON SMART POWER & INTERNET ENERGY SYSTEMS, SPIES, 2022, : 1772 - 1777
[22] Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer
Li, Jiarong
Lin, Cheng-Jian
Wang, Haiyu
Kan, Jiarong
SENSORS AND MATERIALS, 2022, 34 (09) : 3649 - 3670
[23] A simplified fractional order impedance model and parameter identification method for lithium-ion batteries
Yang, Qingxia
Xu, Jun
Cao, Binggang
Li, Xiuqing
PLOS ONE, 2017, 12 (02):
[24] An Online Condition-Based Parameter Identification Switching Algorithm for Lithium-Ion Batteries in Electric Vehicles
Huang, Cong-Sheng
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (02) : 1701 - 1709
[25] Parameter Estimation for Lithium-ion Batteries Based on the Weighted Gradient Descent Algorithm
Hu, Chong
Ji, Yan
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 4789 - 4794
[26] Lithium plating detection of lithium-ion batteries based on the improved variance entropy algorithm
Chen, Yingjie
Zhang, Huaqin
Hong, Jichao
Hou, Yankai
Yang, Jingsong
Zhang, Chi
Ma, Shikun
Zhang, Xinyang
Yang, Haixu
Liang, Fengwei
Li, Kerui
ENERGY, 2024, 299
[27] Particle swarm optimization algorithm using complex-order derivative concept: A comprehensive study
Pahnehkolaei, Seyed Mehdi Abedi
Alfi, Alireza
Machado, J. A. Tenreiro
APPLIED SOFT COMPUTING, 2021, 111
[28] Novel Parameter Identification Method for Lithium-Ion Batteries Based on Curve Fitting
Lukic, Milos
Giangrande, Paolo
Klumpner, Christian
Galea, Michael
2020 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2020,
[29] Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method
Rahman, Md Ashiqur
Anwar, Sohel
Izadian, Afshin
JOURNAL OF POWER SOURCES, 2016, 307 : 86 - 97
[30] On Parameter Identification of an Equivalent Circuit Model for Lithium-Ion Batteries
Tian, Ning
Wang, Yebin
Chen, Jian
Fang, Huazhen
2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), 2017, : 187 - 192

← 1 2 3 4 5 →