基于ECM的电芯电-热耦合建模与验证

doi:10.19799/j.cnki.2095-4239.2023.0080

摘要/Abstract

摘要：

电芯内阻不仅影响电池的电性能，而且影响电池工作过程中的温度。本工作通过恒流充放电搁置测试，建立二阶等效电路模型(ECM)，辨识电芯在不同电流、温度和SOC下的电容、电阻参数，计算电芯电性能，同时给出Bernardi产热模型中的不可逆热，不可逆热结合可逆热计算电芯产热，通过STAR-CCM+将电-热模型耦合，模拟电芯在不同工况下的电响应与温度响应；由于HPPC存在脉冲时间短、内阻体现不完整等缺点，故本工作采取恒流充放电搁置测试获得RC参数，进行等效电路仿真，研究结果表明：将恒流充放电搁置测试得到的RC参数导入电-热耦合模型中，仿真结果对比实验偏差很小。

关键词: 等效电路模型(ECM), Bernardi产热模型, 恒流充放电搁置测试, 电-热耦合, STAR-CCM+

Abstract:

The internal resistance of a battery affects its electrical performance and the temperature during its operation. Herein, the second-order equivalent circuit model (ECM) is established through the constant-current charge-discharge shelving test, which identifies the capacitance and resistance parameters of the cell under different currents, temperatures, and state-of-charge conditions, and calculates the electric performance of the cell. Additionally, Bernardi's heat generation model is introduced to account for the irreversible heat, and the combination of irreversible and reversible heat is calculated to determine the overall heat generation within the cell. By coupling the electric-thermal model in STAR-CCM+, the electrical and temperature responses of the battery are simulated under different working conditions. To overcome the shortcomings of the hybrid pulse power characterization test, such as short pulse time and incomplete characterization of internal resistance, this study adopts a constant-current charge-discharge shelving test to obtain the RC parameters and conducts an equivalent circuit simulation. The research results show that when the RC parameters obtained from the constant-current charge-discharge shelving test are imported into the electric-thermal coupling model, the simulation results have minimal deviation compared with the experimental results.

Key words: equivalent circuit model, Bernardi heat generation model, constant-current charge-discharge shelving test, electro-thermal coupling, STAR-CCM+

中图分类号:

TM 912

狄云, 周正柱, 党会鸿, 葛志浩. 基于ECM的电芯电-热耦合建模与验证[J]. 储能科学与技术, 2023, 12(8): 2638-2648.

Yun DI, Zhengzhu ZHOU, Huihong DANG, Zhihao GE. Modeling and verification of electric-thermal coupling in batteries based on ECM[J]. Energy Storage Science and Technology, 2023, 12(8): 2638-2648.

图/表 15

图1

图2

图3

表1

图4

表2

表3

图5

图6

图7

图8

图9

图10

图11

图12

参考文献 26

1	李斌, 唐连伟, 钟修林. 动力电池包热管理的一维和三维高效耦合仿真[C]// 2019中国汽车工程学会汽车空气动力学分会学术年会论文集. 2019: 98-110.
2	豁长青, 曹铭, 黄菊花, 等. 一种通用的锂电池模型参数辨识方法[J]. 电源技术, 2021, 45(4): 455-458.
	HUO C Q, CAO M, HUANG J H, et al. A general parameter identification method for lithium battery model[J]. Chinese Journal of Power Sources, 2021, 45(4): 455-458.
3	黄伟, 文华, 李亚胜. 三元软包锂离子动力电池热特性测量及应用[J]. 储能科学与技术, 2019, 8(2): 284-291.
	HUANG W, WEN H, LI Y S. Measurements and application of thermal characteristics of soft-packed NCM lithium-ion power battery[J]. Energy Storage Science and Technology, 2019, 8(2): 284-291.
4	王芳, 孙智鹏, 林春景, 等. 能量型磷酸铁锂动力电池直流内阻测试及分析[J]. 重庆理工大学学报(自然科学), 2017, 31(8): 44-50.
	WANG F, SUN Z P, LIN C J, et al. Experimental analysis of internal resistance of energy-type LiFePO₄ power batteries and its influencing factors[J]. Journal of Chongqing University of Technology (Natural Science), 2017, 31(8): 44-50.
5	朱志祥, 尚丽平, 屈薇薇. 基于HPPC的锂电池欧姆内阻最优测试条件研究[J]. 电源技术, 2020, 44(6): 841-843, 848.
	ZHU Z X, SHANG L P, QU W W. Study on optimal test conditions of ohmic internal resistance for lithium battery based on HPPC[J]. Chinese Journal of Power Sources, 2020, 44(6): 841-843, 848.
6	ZHU J G, WANG Y X, HUANG Y A, et al. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation[J]. Nature Communications, 2022, 13: 2261.
7	谢奕展, 程夕明. 锂离子电池简化电化学模型理论误差分析研究[J]. 机械工程学报, 2022, 58(22): 37-55.
	XIE Y Z, CHENG X M. Theoretical error analysis of simplified electrochemical model of lithium ion battery[J]. Journal of Mechanical Engineering, 2022, 58(22): 37-55.
8	吴小慧, 张兴敢. 锂电池二阶RC等效电路模型参数辨识[J]. 南京大学学报(自然科学), 2020, 56(5): 754-761.
	WU X H, ZHANG X G. Parameters identification of second order RC equivalent circuit model for lithium batteries[J]. Journal of Nanjing University (Natural Science), 2020, 56(5): 754-761.
9	杨洋. 基于ARIMA和BP神经网络组合模型的锂电池寿命预测[D]. 海口: 海南大学.
	YANG Y. Life prediction of lithium battery based on ARIMA and BP neural network combined model[D]. Haikou: Hainan University.
10	张涌, 张福明, 吴海啸, 等. 基于改进的二阶阻容等效电路模型的锂电池建模仿真[J]. 物流科技, 2020, 43(1): 59-64.
	ZHANG Y, ZHANG F M, WU H X, et al. Lithium battery modeling and simulation based on improved second-order RC equivalent circuit model[J]. Logistics Sci-Tech, 2020, 43(1): 59-64.
11	JI Y J, QIU S L, LI G. Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance[J]. Journal of Central South University, 2020, 27(9): 2606-2613.
12	詹涵. 基于等效电路模型的锂离子电池成组性能研究[D]. 济南: 山东大学.
	ZHAN H. Research on group performance of lithium-ion batteries based on equivalent circuit model[D]. Jinan: Shandong University.
13	王宇伟, 赵阳, 华迪, 等. 基于二阶等效电路模型的锂电池状态估计方法研究[J]. 节能, 2022, 41(4): 38-42.
	WANG Y W, ZHAO Y, HUA D, et al. Research on state estimation method of lithium battery based on second-order equivalent circuit model[J]. Energy Conservation, 2022, 41(4): 38-42.
14	那红军, 王顺利, 李建超. 基于等效建模和参数辩识的锂电池荷电状态估算研究[J]. 电池工业, 2019, 23(6): 289-291, 304.
	NA/NUO) H J, WANG S L, LI J C. Research on estimation of state of charge of lithium battery based on equivalent modeling and parameter identification[J]. Chinese Battery Industry, 2019, 23(6: 289-291, 304.
15	黄伟男. 基于热电耦合原理的锂离子电池热仿真及在线实现方法研究[D]. 北京: 北京交通大学.
	HUANG W N. Research on thermal simulation and online realization method of lithium ion battery based on thermoelectric coupling principle[D]. Beijing: Beijing Jiaotong University.
16	毛亚, 白清友, 马尚德, 等. 循环老化对锂离子电池在绝热条件下的产热及热失控影响[J]. 储能科学与技术, 2018, 7(6): 1120-1127.
	MAO Y, BAI Q Y, MA S D, et al. Influence of cycling on the heat-release and thermal runaway of the lithium ion battery under adiabatic condition[J]. Energy Storage Science and Technology, 2018, 7(6): 1120-1127.
17	BERNARDI D, PAWLIKOWSKI E, NEWMAN J. A general energy balance for battery systems[J]. Journal of the Electrochemical Society, 1985, 132(1): 5-12.
18	韦海燕, 钟腾云, 潘海鸿, 等. 基于改进HPPC锂离子电池内阻测试方法研究[J]. 电源技术, 2019, 43(8): 1309-1311, 1339.
	WEI H Y, ZHONG T Y, PAN H H, et al. Study on measurement method of internal resistance of lithium-ion battery based on improved HPPC[J]. Chinese Journal of Power Sources, 2019, 43(8): 1309-1311, 1339.
19	李旭. 纯电动汽车锂电池SOC估计算法研究[D]. 西安: 长安大学.
	LI X. Research on SOC estimation algorithm of lithium battery in pure electric vehicle[D]. Xi'an: Changan University.
20	贾春辉. 动力电池热特性分析及冷却系统优化[D]. 长春: 吉林大学.
	JIA C H. Thermal characteristics analysis and cooling system optimization of power battery[D]. Changchun: Jilin University.
21	伍佳佳, 赵又群. 基于Thevenin模型的混合动力镍氢电池参数辨识[J]. 农业装备与车辆工程, 2014, 52(1): 1-5.
	WU J J, ZHAO Y Q. Parameter identification of Ni/MH battery used in hybrid electric vehicles based on Thevenin model[J]. Agricultural Equipment & Vehicle Engineering, 2014, 52(1): 1-5.
22	杨杰, 王婷, 杜春雨, 等. 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 J, WANG T, DU C Y, et al. Overview of the modeling of lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(1): 58-64.
23	李嘉波, 李忠玉, 焦生杰, 等. 基于反馈最小二乘支持向量机锂离子状态估计[J]. 储能科学与技术, 2020, 9(3): 951-957.
	LI J B, LI Z Y, JIAO S J, et al. Lithium-ion state estimation based on feedback least square support vector machine[J]. Energy Storage Science and Technology, 2020, 9(3): 951-957.
24	段慧云, 徐维, 夏威. 基于最小二乘支持向量回归的锂电池能量状态估计[J]. 电池工业, 2022, 26(5): 240-246.
	DUAN H Y, XU W, XIA W. Energy state estimation of lithium battery based on least squares support vector regression[J]. Chinese Battery Industry, 2022, 26(5): 240-246.
25	栗欢欢, 竺玉强, 王效宇, 等. 熵热系数取值方式对锂离子电池热模型精度的影响[J]. 重庆理工大学学报(自然科学), 2021, 35(5): 1-8.
	LI H H, ZHU Y Q, WANG X Y, et al. The influence of entropy heat coefficient simplification on the accuracy of thermal model of LIBs[J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(5): 1-8.
26	殷宝华, 艾亮, 贾明, 等. 锂离子动力电池的三维热模型[J]. 电源技术, 2018, 42(2): 199-201, 307.
	YIN B H, AI L, JIA M, et al. 3D thermal model of lithium ion battery[J]. Chinese Journal of Power Sources, 2018, 42(2): 199-201, 307.

步骤	程序	截止条件
1	0.33 C恒流放电(25 ℃)	电压≤3 V
2	静置(25 ℃)	时间≥2 h
3	0.5 C恒流充电(25 ℃)	单步容量≥5.2 Ah
4	静置(25 ℃)	时间≥1 h
循环步骤3～4	…	循环数≥10
S1	0.33 C恒流充电(25 ℃)	电压≥4.2 V
S2	4.2 V恒压充电(25 ℃)	电流≤0.05 C
S3	静置(25 ℃)	时间≥2 h
S4	0.5 C恒流放电(25 ℃)	单步容量≥5.2 Ah
S5	静置(25 ℃)	时间≥1 h
循环步骤S4～S5	…	循环数≥10
倒数第二步	0.33 C恒流放电(25 ℃)	电压≤3 V
最后一步	静置(25 ℃)	时间≥1 h

步骤	程序	截止条件
1	0.33 C恒流充电(室温)	电压≥4.2 V
2	4.2 V恒压充电(室温)	电流≤0.05 C
3	静置(室温)	时间≥5 h
4	0.5 C恒流放电(室温)	电压≤3 V
5	静置(室温)	时间≥1 h

步骤	程序	截止条件
1	0.33 C恒流充电(25 ℃)	电压≥4.2 V
2	4.2 V恒压充电(25 ℃)	电流≤0.05 C
3	静置(25 ℃)	时间≥2 h
4	0.33 C恒流放电(25 ℃)	单步容量≥5.2 Ah
5	静置(10 ℃)	时间≥8 h
6	静置(25 ℃)	时间≥8 h
7	静置(55 ℃)	时间≥8 h
循环步骤3～6	…	循环数≥10

[1]	罗勇, 周振雨, 申付涛, 黄欢, 邱晓斌, 翁勇永. 考虑参数时变的电池包电热耦合建模[J]. 储能科学与技术, 2022, 11(10): 3180-3190.
[2]	匡勇, 刘霞, 钱振, 郭成龙, 黄丛亮, 饶中浩. 锂离子电池产热特性理论模型研究进展[J]. 储能科学与技术, 2015, 4(6): 599-608.