锂离子电池模型研究综述

doi:10.12028/j.issn.2095-4239.2018.0143

储能科学与技术 ›› 2019, Vol. 8 ›› Issue (1): 58-64.doi: 10.12028/j.issn.2095-4239.2018.0143

锂离子电池模型研究综述

杨杰¹, 王婷^2,3,4, 杜春雨¹, 闵凡奇^1,3,4, 吕桃林^2,5, 张熠霄^2,3,4, 晏莉琴^2,3,4, 解晶莹^1,2,4, 尹鸽平¹

1 哈尔滨工业大学化工与化学学院, 黑龙江哈尔滨 150001;
2 上海空间电源研究所, 上海 200245;
3 上海动力储能电池系统工程技术有限公司, 上海 200241;
4 上海动力与储能电池系统工程技术研究中心, 上海 200245;
5 同济大学材料科学与工程学院, 上海 201804

收稿日期:2018-08-14 修回日期:2018-10-24 出版日期:2019-01-01 发布日期:2018-11-07
通讯作者: 解晶莹,教授,研究方向为致密储能技术、清洁能源生产、存储、多能互补集成优化设计,E-mail:xiejingying2007@126.com;尹鸽平,教授,研究方向为储能材料与技术、电池系统诊断与管理等,E-mail:yingphit@hit.edu.cn。
作者简介:杨杰(1990-),男,博士研究生,研究方向为锂离子电池衰减机制分析、状态诊断及寿命预测建模、电池大数据分析等,E-mail:JYangHIT@163.com
基金资助:
国家重点研发计划（2017YFB0102204），上海市科委项目（18DZ2284000）。

Overview of the modeling of lithium-ion batteries

YANG Jie¹, WANG Ting^2,3,4, DU Chunyu¹, MIN Fanqi^1,3,4, Lyu Taolin^2,5, ZHANG Yixiao^2,3,4, YAN Liqin^2,3,4, XIE Jingying^1,2,4, YIN Geping¹

1 School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China;
2 Shanghai Institute of Spaceflight Power, Shanghai 200245, China;
3 Shanghai Power & Energy Storage Battery System Engineering Tech. Co. Ltd., Shanghai 200241, China;
4 Shanghai Engineering Center for Power and Energy Storage Systems, Shanghai 200245, China;
5 School of Materials Science and Engineering, Tongji University, Shanghai 201804, China

Received:2018-08-14 Revised:2018-10-24 Online:2019-01-01 Published:2018-11-07

摘要/Abstract

摘要： 简述了锂离子电池等效电路模型和电化学模型的研究进展。由于具有耗时短、技术开发效率高等优点，仿真模型被广泛应用于锂离子电池衰减机制分析、状态诊断及寿命预测。锂离子电池仿真模型主要包括等效电路模型和电化学机理模型。等效电路模型主要应用于锂离子电池荷电状态诊断。电化学机理模型主要应用于锂离子电池衰减机制分析和健康状态诊断，并为寿命预测提供技术支持。等效电路模型的结构过于单一，在锂离子电池寿命后期适用性降低。电化学机理模型结构复杂，计算量大，在线性应用能力较差。总结了现阶段常用的锂离子电池等效电路模型和电化学模型的建模原理及模型结构，阐述了每种模型在电池研究中的具体应用，并分析了其各自的优势及局限性。通过以上分析，并结合最新的建模理论，对建立具有高精度、高适用性锂离子电池仿真模型的研究方向进行了展望。

关键词: 锂离子电池, 仿真模型, 状态诊断

Abstract: The models of lithium-ion batteries, including equivalent circuit models and electrochemical models, are reviewed. Models are used for the degradation mechanisms analysis, state estimation and life prediction of lithium ion batteries due to the time-effectiveness and applicability. The equivalent circuit models are more applicable for state of charge estimation and the electrochemical models are suitable for the degradation analysis and state of health estimation of lithium ion batteries. The simple and fixed model structure for equivalent circuit models and the complicated model structures and heavy computation for electrochemical models limit their application. The authors summarize the principles and structures of equivalent circuit models and electrochemical models. Then the application of these models is described and the merits and limitations of each model are elaborated. Then, based on the analysis mentioned above and the state-of-the-art modelling theory, the future research direction on more accurate and universal battery models is put forward.

Key words: lithium ion batteries, modeling, state diagnosis

中图分类号:

TM912

杨杰, 王婷, 杜春雨, 闵凡奇, 吕桃林, 张熠霄, 晏莉琴, 解晶莹, 尹鸽平. 锂离子电池模型研究综述[J]. 储能科学与技术, 2019, 8(1): 58-64.

YANG Jie, WANG Ting, DU Chunyu, MIN Fanqi, Lyu Taolin, ZHANG Yixiao, YAN Liqin, XIE Jingying, YIN Geping. Overview of the modeling of lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(1): 58-64.

参考文献

[1] 侯朝勇, 数见昌弘, 许守平, 等. 基于微分曲线的LiFePO₄电池SOC估计算法研究[J]. 储能科学与技术, 2017, 6(6):1321-1327. HOU Chaoyong, MASAHIRO Kazumi, XU Shouping, et al. Research of SOC estimation algorithm for LiFePO₄ battery based on differential curves[J]. Energy Storage Science and Technology, 2017, 6(6):1321-1327.
[2] 林俊豪, 古雄文, 马丽. 基于优化调度的用户侧电池储能配置及控制方法[J]. 储能科学与技术, 2018, 7(1):90-99. LIN Junhao, GU Xiongwen, MA Li. Optimal sizing and control of demand-side battery energy storage system[J]. Energy Storage Science and Technology, 2018, 7(1):90-99.
[3] 刘伟龙, 王丽芳, 王立业. 基于电动汽车工况识别预测的锂离子电池SOE估计[J]. 电工技术学报, 2018, 33(1):17-25. LIU Weilong, WANG Lifang, WANG Liye. Estimation of state-of-energy for electric vehicles based on the identification and prediction of driving condition[J]. Transactions of China Electrotechnical Society, 2018, 33(1):17-25.
[4] 胡飞, 林明翔, 刘曙光, 等. 锂离子储能电池Li₄Ti₅O₁₂的失效分析[J]. 储能科学与技术, 2016, 5(4):454-461. HU Fei, LIN Mingxiang, LIU Shuguang, et al. The degradation analysis of lithium-ion storage battery with Li₄Ti₅O₁₂ anode[J]. Energy Storage Science and Technology, 2016, 5(4):454-461.
[5] CHRISTOPHE F, DINH V D, GUY F, et al. Thermal modeling of a cylindrical LiFePO₄/graphite lithium-ion battery[J]. Journal of Power Sources, 2010, 195(9):2961-2968.
[6] KONG S N, CHIN-SIEN M, CHEN Y, et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy, 2009, 86(9):1506-1511.
[7] ZOU C, CHRIS M, DRAGAN N, et al. Multi-time-scale observer design for state-of-charge and state-ofhealth of a lithium-ion battery[J]. Journal of Power Sources, 2016, 335:121-130.
[8] TORSTEN W, BJÖRN F, HANNES K. Implementation and robustness of an analytically based battery state of power[J]. Journal of Power Sources, 2015, 287:448-457.
[9] DONG G, ZHANG X, ZHANG C, et al. A method for state of energy estimation of lithium-ion batteries based on neural network model[J]. Energy, 2015, 90:879-888.
[10] WIDANAGE W D, BARAI A, CHOUCHELAMANE G H, et al. Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 1:Signal design[J]. Journal of Power Sources, 2016, 324:70-78.
[11] WIDANAGE W D, BARAI A, CHOUCHELAMANE G H, et al. Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 2:Model estimation[J]. Journal of Power Sources, 2016, 324:61-69.
[12] NANDHINI G, SUMAN B, KRISHNAN S H, et al. Physics based modeling of a series parallel battery pack for asymmetry analysis, predictive control and life extension[J]. Journal of Power Sources, 2016, 322:57-67.
[13] RAJABLOO B, DÉSILETS M, CHOQUETTE Y. Parameter estimation of single particle model using COMSOL Multiphysics^® and MATLAB^® optimization toolbox[C]//The 2015 COMSOL Conference, 2015.
[14] RAJABLOO B, JOKAR A, WAKEM W, et al. Lithium iron phosphate electrode semi-empirical performance model[J]. Journal of Applied Electrochemistry, 2018, 48(6):663-674.
[15] GREGORY L P. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1. Background[J]. Journal of Power Sources, 2004, 134(2):252-261.
[16] GREGORY L P. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2. Modeling and identification[J]. Journal of Power Sources, 2004, 134(2):262-276.
[17] GREGORY L P. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation[J]. Journal of Power Sources, 2004, 134(2):277-292.
[18] SUNGWOO C, HYEONSEOK J, CHONGHUN H, et al. State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model[J]. Computers and Chemical Engineering, 2012, 41:1-9.
[19] WENHUA H. Z, YING Z, BRUCE J T. A simplified equivalent circuit model for simulation of Pb-acid batteries at load for energy storage application[J]. Energy Conversion and Management, 2011, 52(89):2794-2799.
[20] HE H, XIONG R, FAN J. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies, 2011, 4(4):582-598.
[21] JOHNSON V H. Battery performance models in ADVISOR[J]. Journal of Power Sources, 2002, 110(2):321-329.
[22] LIA J, ADEWUYIA K, LOTFIB N, et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health (SOH) estimation[J]. Applied Energy, 2018, 212:1178-1190.
[23] GOPALUNI R B, BRAATZ R D. State of charge estimation in Li-ion batteries using an isothermal pseudo two-dimensional model[J]. Ifac Proceedings Volumes, 2013, 46(32):135-140.
[24] ALI J, BARZIN R, MARTIN D, et al. Review of simplified Pseudo-two-Dimensional models of lithium-ion batteries[J]. Journal of Power Sources, 2016, 327:44-55.
[25] DAO T, VYASARAYANI C P, MCPHEE J. Simplification and order reduction of lithium-ion battery model based on porous-electrode theory[J]. Journal of Power Sources, 2012, 198:329-337.
[26] ALDO R, LUIS A. Comparison of discretization methods applied to the single-particle model of lithium-ion batteries[J]. Journal of Power Sources, 2011, 196(23):10267-10279.
[27] LUO W, LYU C, WANG L, et al. A new extension of physics-based single particle model for higher chargeedischarge rates[J]. Journal of Power Sources, 2013, 241:295-310.
[28] RAMADESIGAN V, BOOVARAGAVAN V, PIRKLE J C, et al. Efficient reformulation of solid-phase diffusion in physics-based lithium-ion battery models[J]. Journal of the Electrochemical Society, 2010, 157(7):A854-A860.
[29] ZHANG Q, WHITE R E. Comparison of approximate solution methods for the solid phase diffusion equation in a porous electrode model[J]. Journal of Power Sources, 2007, 165(2):880-886.

锂离子电池模型研究综述

Overview of the modeling of lithium-ion batteries

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李海涛, 孔令丽, 张欣, 余传军, 王纪威, 徐琳. N/P设计对高镍NCM/Gr电芯性能的影响[J]. 储能科学与技术, 2022, 11(7): 2040-2045.
[2]	陈龙, 夏权, 任羿, 曹高萍, 邱景义, 张浩. 多物理场耦合下锂离子电池组可靠性研究现状与展望[J]. 储能科学与技术, 2022, 11(7): 2316-2323.
[3]	易顺民, 谢林柏, 彭力. 基于VF-DW-DFN的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2022, 11(7): 2305-2315.
[4]	祝庆伟, 俞小莉, 吴启超, 徐一丹, 陈芬放, 黄瑞. 高能量密度锂离子电池老化半经验模型[J]. 储能科学与技术, 2022, 11(7): 2324-2331.
[5]	王宇作, 王瑨, 卢颖莉, 阮殿波. 孔结构对软碳负极储锂性能的影响[J]. 储能科学与技术, 2022, 11(7): 2023-2029.
[6]	孔为, 金劲涛, 陆西坡, 孙洋. 对称蛇形流道锂离子电池冷却性能[J]. 储能科学与技术, 2022, 11(7): 2258-2265.
[7]	霍思达, 薛文东, 李新丽, 李勇. 基于CiteSpace知识图谱的锂电池复合电解质可视化分析[J]. 储能科学与技术, 2022, 11(7): 2103-2113.
[8]	邓健想, 赵金良, 黄成德. 高能量锂离子电池硅基负极黏结剂研究进展[J]. 储能科学与技术, 2022, 11(7): 2092-2102.
[9]	刘显茜, 孙安梁, 田川. 基于仿生翅脉流道冷板的锂离子电池组液冷散热[J]. 储能科学与技术, 2022, 11(7): 2266-2273.
[10]	于春辉, 何姿颖, 张晨曦, 林贤清, 肖哲熙, 魏飞. 硅基负极与电解液化学反应的分析与抑制策略[J]. 储能科学与技术, 2022, 11(6): 1749-1759.
[11]	丁奕, 杨艳, 陈锴, 曾涛, 黄云辉. 锂离子电池智能消防及其研究方法[J]. 储能科学与技术, 2022, 11(6): 1822-1833.
[12]	张言, 王海, 刘朝孟, 张德柳, 王佳东, 李建中, 高宣雯, 骆文彬. 锂离子电池富镍三元正极材料NCM的研究进展[J]. 储能科学与技术, 2022, 11(6): 1693-1705.
[13]	欧宇, 侯文会, 刘凯. 锂离子电池中的智能安全电解液研究进展[J]. 储能科学与技术, 2022, 11(6): 1772-1787.
[14]	韩俊伟, 肖菁, 陶莹, 孔德斌, 吕伟, 杨全红. 致密储能：基于石墨烯的方法学和应用实例[J]. 储能科学与技术, 2022, 11(6): 1865-1873.
[15]	辛耀达, 李娜, 杨乐, 宋维力, 孙磊, 陈浩森, 方岱宁. 锂离子电池植入传感技术[J]. 储能科学与技术, 2022, 11(6): 1834-1846.