电源学报

基于改进鲸鱼算法的锂电池模型参数辨识

PDF下载

赵家城 , 孙坚 , 徐红伟

电源学报 | 电池与储能 2025,23(7): 283-293

收起

电源学报 |电池与储能 2025 , 23 (7) : 283 -293

基于改进鲸鱼算法的锂电池模型参数辨识

全屏

[Author(id=1321491426542727649, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zhaojiacheng1@outlook.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321491426626613731, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491426542727649, language=EN, stringName=Jiacheng ZHAO, firstName=Jiacheng, middleName=null, lastName=ZHAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491426706305508, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491426542727649, language=CN, stringName=赵家城, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国计量大学机电工程学院，杭州　310018, bio={"content":"

赵家城（1998—　），男，硕士研究生。研究方向：锂电池智能充电管理。E-mail：zhaojiacheng1@outlook.com。

"}, bioImg=null, bioContent=

赵家城（1998—　），男，硕士研究生。研究方向：锂电池智能充电管理。E-mail：zhaojiacheng1@outlook.com。

, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1321491426421092829, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, xref=null, ext=[AuthorCompanyExt(id=1321491426429481438, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, companyId=1321491426421092829, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China), AuthorCompanyExt(id=1321491426442064351, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, companyId=1321491426421092829, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国计量大学机电工程学院，杭州　310018)])]), Author(id=1321491426773414374, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1955692303@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1321491426865689064, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491426773414374, language=EN, stringName=Jian SUN, firstName=Jian, middleName=null, lastName=SUN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491426957963753, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491426773414374, language=CN, stringName=孙坚, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国计量大学机电工程学院，杭州　310018, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1321491426421092829, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, xref=null, ext=[AuthorCompanyExt(id=1321491426429481438, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, companyId=1321491426421092829, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China), AuthorCompanyExt(id=1321491426442064351, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, companyId=1321491426421092829, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国计量大学机电工程学院，杭州　310018)])]), Author(id=1321491427062821355, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, orderNo=2, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=xhw@cjlu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321491427138318829, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491427062821355, language=EN, stringName=Hongwei XU, firstName=Hongwei, middleName=null, lastName=XU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491427238982126, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491422939820504, authorId=1321491427062821355, language=CN, stringName=徐红伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国计量大学机电工程学院，杭州　310018, bio={"content":"

徐红伟（1972—　），男，博士，副教授。研究方向：锂电池智能管理和自动化装置。E-mail：xhw@cjlu.edu.cn。

"}, bioImg=null, bioContent=

徐红伟（1972—　），男，博士，副教授。研究方向：锂电池智能管理和自动化装置。E-mail：xhw@cjlu.edu.cn。

赵家城, 孙坚, 徐红伟

作者信息

中国计量大学机电工程学院，杭州　310018

通讯作者:

孙坚（1964—　），男，通信作者，硕士，教授。研究方向：新能源计量和智能管理。E-mail：1955692303@qq.com。

作者简介:

赵家城（1998—　），男，硕士研究生。研究方向：锂电池智能充电管理。E-mail：zhaojiacheng1@outlook.com。

徐红伟（1972—　），男，博士，副教授。研究方向：锂电池智能管理和自动化装置。E-mail：xhw@cjlu.edu.cn。

Parameter Identification of Lithium Battery Model Based on Improved Whale Optimization Algorithm

Jiacheng ZHAO, Jian SUN, Hongwei XU

Affiliations

College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China

出版时间: 2025-11-30 doi: 10.13234/j.issn.2095-2805.2025.7.283

文章导航

摘要

收起

锂电池模型参数辨识准确性对开发电池管理系统具有重要意义。为提高参数辨识的准确性和辨识效率，采用对立学习策略和Levy变异改进鲸鱼算法，并结合阿伦尼乌斯定律提出锂电池电化学-热耦合模型多步参数辨识方法。该方法以15、25、35 ℃下的恒流数据进行参数辨识，并通过恒流工况和动态工况验证辨识结果。仿真和实验验证显示，参数平均绝对误差百分比降低了7.2%，模型预测的电压最大均方根误差为26 mV，温度最大均方根误差为0.4 ℃，表明基于改进鲸鱼算法的多步参数辨识方法能够有效辨识参数。

关键词

锂电池 / 电化学-热耦合模型 / 参数辨识 / 鲸鱼算法

Abstract

收起

The parameter identification accuracy of a lithium battery model is of significance for the development of a battery management system. To improve the accuracy and efficiency of parameter identification, a multi-step parameter identification method for the electrochemical-thermal coupling model of lithium battery is proposed in combination with the Arrhenius law, which uses the whale optimization algorithm improved by oppositional learning strategy and Levy mutation. In this method, constant-current data at 15 ℃, 25 ℃ and 35 ℃ are adopted for parameter identification, and constant-current and dynamic working conditions are used to verify the identification results. Simulation and experimental results show that the mean absolute percentage error of parameters is reduced by 7.2%, the maximum root mean square error of voltage predicted by the model is 26 mV and that of temperature is 0.4 ℃, indicating that the multi-step parameter identification method using the whale optimization algorithm based on oppositional learning strategy and Levy mutation can efficiently identify parameters.

Key words

Lithium battery / electrochemical-thermal coupling model / parameter identification / whale optimization algorithm

引用本文

赵家城, 孙坚, 徐红伟. 基于改进鲸鱼算法的锂电池模型参数辨识. 电源学报, 2025 , 23 (7) : 283 -293 . DOI: 10.13234/j.issn.2095-2805.2025.7.283

Jiacheng ZHAO, Jian SUN, Hongwei XU. Parameter Identification of Lithium Battery Model Based on Improved Whale Optimization Algorithm[J]. Journal of Power Supply, 2025 , 23 (7) : 283 -293 . DOI: 10.13234/j.issn.2095-2805.2025.7.283

基金

收起

浙江省科技计划资助项目(2022C01024)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Li Weihan, Limoge D W, Zhang Jiawei, et al. Estimation of potentials in lithium-ion batteries using machine learning models[J]. IEEE Transactions on Control Systems Technology, 2022, 30(2): 680-695.

[2]

Li Junfu, Wang Dafang, Deng Lei, et al. Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries[J]. Journal of Energy Storage, 2020, 31: 101538.

[3]

Li Weihan, Fan Yue, Ringbeck F, et al. Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression[J]. Applied Energy, 2022, 306: 118114.

[4]

Ringbeck F, Garbade M, Sauer D U. Uncertainty-aware state estimation for electrochemical model-based fast charging control of lithium-ion batteries[J]. Journal of Power Sources, 2020, 470: 228221.

[5]

Schmalstieg J, Rahe C, Ecker M, et al. Full cell parameterization of a high-power lithium-ion battery for a physico-chemical model: part I. physical and electrochemical parameters[J]. Journal of the Electrochemical Society, 2018, 165(16): A3799-A3810.

[6]

Schmalstieg J, Sauer D U. Full cell parameterization of a high-power lithium-ion battery for a physico-chemical model: part II. thermal parameters and validation[J]. Journal of the Electrochemical Society, 2018, 165(16): A3811-A3819.

[7]

Deng Zhongwei, Deng Hao, Yang Lin, et al. Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery[J]. Energy, 2017, 138: 509-519.

[8]

马锐, 贾学翠, 张永康, 等. 基于三次样条插值法的储能锂电池建模与参数辨识[J]. 电源学报, 2023, 21(1): 133-141. Ma Rui, Jia Xuecui, Zhang Yongkang, et al. Modeling and parameters identification of Li-ion battery for energy storage based on cubic spline interpolation method[J]. Journal of Power Supply, 2023, 21(1): 133-141. (in Chinese)

[9]

Pang Hui, Mou Lianjing, Guo Long, et al. Parameter identification and systematic validation of an enhanced single-particle model with aging degradation physics for Liion batteries[J]. Electrochimica Acta, 2019, 307: 474-487.

[10]

许志华. 锂电池的分数阶建模及状态估计方法研究[J]. 电源学报, 2025, 23(2): 266-273. Xu Zhihua. Fractional-order modeling and state estimation method for lithium battery[J]. Journal of Power Supply, 2025, 23(2): 266-273. (in Chinese)

[11]

Fan Guodong. Systematic parameter identification of a control-oriented electrochemical battery model and its application for state of charge estimation at various operating conditions[J]. Journal of Power Sources, 2020, 470: 228153.

[12]

Doyle M, Newman J, Gozdz A S, et al. Comparison of modeling predictions with experimental data from plastic lithium ion cells[J]. Journal of the Electrochemical Society, 1996, 143(6): 1890-1903.

[13]

Moura S J, Argomedo F B, Klein R, et al. Battery state estimation for a single particle model with electrolyte dynamics[J]. IEEE Transactions on Control Systems Technology, 2017, 25(2): 453-468.

[14]

Chen Changhui, Brosa Planella F, O’Regan K, et al. Development of experimental techniques for parameterzation of multi-scale lithium-ion battery models[J]. Journal of the Electrochemical Society, 2020, 167(8): 080534.

[15]

Lin Xinfan, Perez H E, Mohan S, et al. A lumped-parameter electro-thermal model for cylindrical batteries[J]. Journal of Power Sources, 2014, 257: 1-11.

[16]

韩甜, 时玮, 赵杨梅, 等. 圆柱型锂离子电池三维分层热耦合模型研究[J]. 电源学报, 2021, 19(5): 165-171. Han Tian, Shi Wei, Zhao Yangmei, et al. Research on 3D layered thermal coupling model of cylindrical lithium-ion battery[J]. Journal of Power Supply, 2021, 19(5): 165-171. (in Chinese)

[17]

Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.

[18]

Haupt R L, Haupt S E. Practical Genetic Algorithms[M]. New York: John Wiley & Sons, 2003: 1-25.

[19]

Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). Vienna, Austria, 2005: 695-701.

[20]

梁倩. 基于反向精英保留和Levy变异的多目标鲸鱼优化算法[J]. 现代计算机, 2021, 27(18): 25-31. Liang Qian. Multi-objective whale optimization algorithm based on reverse elite retention and Levy mutation[J]. Modern Computer, 2021, 27(18): 25-31. (in Chinese)

[21]

Nyman A, Behm M, Lindbergh G. Electrochemical characterisation and modelling of the mass transport phenomena in LiPF₆-EC-EMC electrolyte[J]. Electrochimica Acta, 2008, 53(22): 6356-6365.

[22]

Landesfeind J, Gasteiger H A. Temperature and concentration dependence of the ionic transport properties of lithium-ion battery electrolytes[J]. Journal of the Electrochemical Society, 2019, 166(14): A3079-A3097.

[23]

Smekens J, Paulsen J, Yang W, et al. A modified multiphysics model for lithium-ion batteries with a Li_xNi_1/3Mn_1/3Co_1/3O₂ electrode[J]. Electrochimica Acta, 2015, 174: 615-624.

[24]

Rheinfeld A, Sturm J, Noel A, et al. Quasi-isothermal external short circuit tests applied to lithium-ion cells: part II. modeling and simulation[J]. Journal of the Electrochemical Society, 2019, 166(2): A151-A177.

[25]

Nileshwar P R, McGordon A, Ashwin T R, et al. Parametric optimization study of a lithium-ion cell[J]. Energy Procedia, 2017, 138: 829-834.

[26]

Geifes F, Bolsinger C, Mielcarek P, et al. Determination of the entropicheat coefficient in a simple electro-thermal lithium-ion cell model with pulse relaxation measurements and least squares algorithm[J]. Journal of Power Sources, 2019, 419: 148-154.

2025年第23卷第7期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.13234/j.issn.2095-2805.2025.7.283

接收时间：2022-10-17
首发时间：2026-10-26
出版时间：2025-11-30

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2022-10-17
修回日期：2023-02-21
录用日期：2023-03-07

基金

Zhejiang Provincial Science and Technology Plan Project(2022C01024)

浙江省科技计划资助项目(2022C01024)

作者信息

中国计量大学机电工程学院，杭州　310018

通讯作者:

孙坚（1964—　），男，通信作者，硕士，教授。研究方向：新能源计量和智能管理。E-mail：1955692303@qq.com。

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.7.283

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT