储能系统锂电池电热耦合建模及参数辨识方法研究

龙潘; 耿光超; 江全元; 马福元; 赵宇

doi:10.19912/j.0254-0096.tynxb.2022-1860

PDF(3302 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (4) : 318-327. DOI: 10.19912/j.0254-0096.tynxb.2022-1860

储能系统锂电池电热耦合建模及参数辨识方法研究

龙潘¹, 耿光超¹, 江全元¹, 马福元^2,3, 赵宇^2,3

作者信息 +

STUDY ON ELECTROTHERMAL COUPLING MODELING AND PARAMETER IDENTIFICATION OF LITHIUM BATTERY ENERGY STORAGE SYSTEM

Long Pan¹, Geng Guangchao¹, Jiang Quanyuan¹, Ma Fuyuan^2,3, Zhao Yu^2,3

Author information +

文章历史 +

摘要

针对电化学机理模型存在全阶结构复杂度高、电池温度变化过程无法精确描述的问题,该文提出将简化电化学模型与热力学模型耦合,建立简化电化学-热力学耦合模型,通过智能辨识算法获取模型相关参数。实验表明,该方法建立的模型在宽环境温度范围、常用放电倍率工况下仿真曲线与实际测试曲线的拟合效果较好,能较好地描述电池电压、电流和温度变化。该模型以较低的模型复杂度和较高的模型精度描述电池内部电化学、热力学行为,可支撑电池状态估计和剩余寿命预测研究。

Abstract

In order to solve the problems of high full-order structure complexity of the existing electrochemical mechanism models and the inability to accurately describe the battery temperature change process, a simplified electrochemical model coupled with the thermodynamic model is proposed in this paper. Firstly, the simplified electrochemical-thermodynamic coupling model is established. Then, the relevant parameters of the model are obtained through intelligent identification algorithm. The experimental results show that the model established by this method has a good fitting effect between the simulated curve and the actual test curve under a wide ambient temperature range and common discharge rate conditions, and can describe the changes of battery voltage, current and temperature field. This model describes the electrochemical and thermodynamic behavior of batteries with low complexity and high precision, which is beneficial to the subsequent research of battery state estimation and remaining life prediction.

导出引用

龙潘, 耿光超, 江全元, 马福元, 赵宇. 储能系统锂电池电热耦合建模及参数辨识方法研究[J]. 太阳能学报. 2024, 45(4): 318-327 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1860

Long Pan, Geng Guangchao, Jiang Quanyuan, Ma Fuyuan, Zhao Yu. STUDY ON ELECTROTHERMAL COUPLING MODELING AND PARAMETER IDENTIFICATION OF LITHIUM BATTERY ENERGY STORAGE SYSTEM[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 318-327 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1860

中图分类号： TM614

参考文献

[1] 张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
ZHANG Z G, KANG C Q.Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[2] 国家发展改革委, 国家能源局. 国家能源局关于加快推动新型储能发展的指导意见[N]. 北京: 发展改革委员会, [2021-7-15], . 国家能源局关于加快推动新型储能发展的指导意见[N]. 北京: 发展改革委员会, [2021-7-15], http://www.gov.cn/zhengce/zhengceku/2021/07/24/content_5627088.htm.
National Development and reform commission, National Energy Administration. Guiding opinions of the national energy administration on accelerating the development of new energy storage[N]. Beijing: National Development and Reform Commission, [2021-7-15], . Guiding opinions of the national energy administration on accelerating the development of new energy storage[N]. Beijing: National Development and Reform Commission, [2021-7-15], http://www.gov.cn/zhengce/zhengceku/2021/07/24/content_5627088.htm.
[3] 贺鸿杰, 张宁, 杜尔顺, 等. 电网侧大规模电化学储能运行效率及寿命衰减建模方法综述[J]. 电力系统自动化, 2020, 44(12): 193-207.
HE H J, ZHANG N, DU E S, et al.Review on modeling method for operation efficiency and lifespan decay of large-scale electrochemical energy storage on power grid side[J].Automation of electric power systems, 2020, 44(12): 193-207.
[4] WEI Z B, XIONG B Y, JI D X, et al.Online state of charge and capacity dual estimation with a multi-timescale estimator for lithium-ion battery[J]. Energy procedia, 2017, 105: 2953-2958.
[5] 何冰琛, 杨薛明, 王劲松, 等. 基于PCa-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.
HE B C, YANG X M, WANG J S, et al.Prediction of remaining useful life of lithium-ion batteries based on PCA-GPA[J]. Acta energiae solaris sinica, 2022, 43(5): 484-491.
[6] HOW D N T, HANNAN M A, HOSSAIN LIPU M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review[J]. IEEE access, 2019, 7: 136116-136136.
WANG Y J, YANG D, ZHANG X, et al.Probability based remaining capacity estimation using data-driven and neural network model[J]. Journal of power sources, 2016, 315: 199-208.
[8] NEJAD S, GLADWIN D T, STONE D A.A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states[J]. Journal of power sources, 2016, 316: 183-196.
[9] ZHANG Q, WANG D F, YANG B W, et al.Electrochemical model of lithium-ion battery for wide frequency range applications[J]. Electrochimica acta, 2020, 343: 136094.
[10] 陈翌, 白云飞, 何瑛. 数据驱动的锂电池健康状态估算方法比较[J]. 储能科学与技术, 2019, 8(6): 1204-1210.
CHEN Y, BAI Y F, HE Y.Comparison of data-driven lithium battery state of health estimation methods[J]. Energy storage science and technology, 2019, 8(6): 1204-1210.
[11] WANG Q K, HE Y J, SHEN J N, et al.A unified modeling framework for lithium-ion batteries: an artificial neural network based thermal coupled equivalent circuit model approach[J]. Energy, 2017, 138: 118-132.
[12] 陈息坤, 孙冬. 锂离子电池建模及其参数辨识方法研究[J]. 中国电机工程学报, 2016, 36(22): 6254-6261.
CHEN X K, SUN D.Research on lithium-ion battery modeling and model parameter identification methods[J]. Proceedings of the CSEE, 2016, 36(22): 6254-6261.
[13] XIA B Z, SUN Z, ZHANG R F, et al.A cubature particle filter algorithm to estimate the state of the charge of lithium-ion batteries based on a second-order equivalent circuit model[J]. Energies, 2017, 10(4): 457.
[14] WANG Y J, TIAN J Q, SUN Z D, et al.A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems[J]. Renewable and sustainable energy reviews, 2020, 131: 110015.
[15] DOYLE M, FULLER T F, NEWMAN J.Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6):1526-1533.
[16] GUO M, SIKHA G, WHITE R E.Single-particle model for a lithium-ion cell:thermal behavior[J]. Journal of the Electrochemical Society, 2011, 158(2): 122-132.
[17] GAMBHIRE P, HARIHARAN K S, KHANDELWAL A, et al.A physics based reduced order aging model for lithium-ion cells with phase change[J]. Journal of power sources, 2014, 270, 281-291.
[18] LI J, WANG L X, LYU C, et al.New method for parameter estimation of an electrochemical-thermal coupling model for LiCoO₂ battery[J]. Journal of power sources, 2016, 307: 220-230.
[19] 严康为, 龙鑫林, 鲁军勇, 等. 高倍率磷酸铁锂电池简化机理建模与放电特性分析[J]. 电工技术学报, 2022, 37(3): 599-609.
YAN K W, LONG X L, LU J Y, et al.Simplified mechanism modeling and discharge characteristic analysis of high C-rate LiFePO₄ battery[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 599-609.
[20] HAN X B, OUYANG M G, LU L G, et al.Simplification of physics-based electrochemical model for lithiumion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation[J]. Journal of power sources, 2015, 278: 814-825.
[21] 李俊夫. 锂离子电池热耦合简化第一原理模型及SOC估计[D]. 哈尔滨: 哈尔滨工业大学, 2018.
LI J F.Thermal coupling simplified first principle model and SOC estimation for lithium-ion battery[D]. Harbin: Harbin Institute of Technology, 2018.
[22] PRADA E, DI DOMENICO D, CREFF Y, et al.Simplified electrochemical and thermal model of LiFePO₄ - graphite Li-ion batteries for fast charge applications[J]. Journal of the Electrochemical Society, 2012, 159(9): a1508-a1519.
[23] WANG Y J, LIU C, PAN R, et al.Experimental data of lithium-ion battery and ultracapacitor under DST and UDDS profiles at room temperature[J]. Data in brief, 2017, 12: 161-163.