基于动态综合型等效电路模型的动力电池特性分析

doi:10.19799/j.cnki.2095-4239.2019.0288

储能科学与技术 ›› 2020, Vol. 9 ›› Issue (3): 986-992.doi: 10.19799/j.cnki.2095-4239.2019.0288

基于动态综合型等效电路模型的动力电池特性分析

封居强^1,²(), 司玉文^1,², 伍龙^1,², 黄凯峰^1,², 张星^1,²

^1.淮南师范学院机械与电气工程学院
^2.淮南师范学院轨道交通电源与控制工程技术研究中心，安徽淮南 232038

收稿日期:2019-12-26 修回日期:2020-01-06 出版日期:2020-05-05 发布日期:2020-05-11
作者简介:封居强（1985—），男，讲师，研究方向为信号检测与估计，E-mail：fjq5060912@126.com。
基金资助:
国家自然基金(61801407);安徽高校自然科学研究项目(KJ2019A0692);淮南师范学院校级项目(2018xj17zd)

Analysis of dynamic battery characteristics based on dynamic synthesis equivalent circuit model

FENG Juqiang^1,²(), SI Yuwen^1,², WU Long^1,², HUANG Kaifeng^1,², ZHANG Xing^1,²

^1.College of Mechanical and Electrical Engineering, Huainan Normal University
^2.Rail Transit Power Supply and Control Engineering Technology Research Center, Huainan Normal University, Huainan 232038, An’hui, China

Received:2019-12-26 Revised:2020-01-06 Online:2020-05-05 Published:2020-05-11

摘要/Abstract

摘要：

针对动力电池模型的内部参数随负载和工况的变化而变化，本文综合考虑电池容量、温度、倍率、迟滞、自放电等因素，设计综合性实验。通过分析动力电池端电压与欧姆极化、电化学极化、浓差极化、不平衡电势、迟滞特性等因素之间的关系，确定动力电池的内部状态变量，提出动态综合型等效电路模型（DS-ECM），借助混合脉冲功率特性法（HPPC）进行动态辨识。针对变化的工况，与Rint、PNGV、二阶RC模型进行比较，并借用赤池信息准则（AIC）衡量模型的优良性。结果表明：本文模型估计的端电压较其它模型平均误差最小降低了52.3%，且AIC值和计算量相对适中，是精度和结构复杂度最佳平衡模型。本研究有助于推动等效电路模型在实际工程中的应用。

关键词: 动力电池, 等效电路模型, 迟滞效应, 赤池信息准则

Abstract:

To determine the internal parameters of the power battery model under changing load and working conditions, this work designs comprehensive experiments considering the battery capacity, temperature, multiplier, hysteresis, self-discharge, and other factors into comprehensive consideration. By analyzing the relationship between the terminal voltage of the power battery and factors such as ohmic, electrochemical, and concentration polarizations; unbalanced potential; and the hysteresis characteristics, the internal state variables of the power battery were determined and a dynamic integrated equivalent-circuit model was proposed. Dynamic identification was performed using a hybrid pulse-power characteristic method. The performance of the proposed model under varying operating conditions was compared with those of the Rint model, the partnership for a new generation of vehicle model, and the second-order RC model. The performance index was the Akaike information criterion (AIC). The average error of the proposed model was 52.3% lower than that of the other models. Moreover, the AIC and calculation values were moderate, implying that the proposed method better balances accuracy and structural complexity than the comparison methods. This study will be helpful in promoting the application of equivalent circuit models in practical engineering.

Key words: power battery, equivalent circuit model, hysteresis effect, Akaike information criterion

中图分类号:

TP 302.1

封居强, 司玉文, 伍龙, 黄凯峰, 张星. 基于动态综合型等效电路模型的动力电池特性分析[J]. 储能科学与技术, 2020, 9(3): 986-992.

FENG Juqiang, SI Yuwen, WU Long, HUANG Kaifeng, ZHANG Xing. Analysis of dynamic battery characteristics based on dynamic synthesis equivalent circuit model[J]. Energy Storage Science and Technology, 2020, 9(3): 986-992.

参考文献 17

1	舒印彪, 张智刚, 郭剑波. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报, 2017, 37(1): 1-9.
	SHU Yinbiao, ZHANG Zhigang, GUO Jianbo. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1): 1-9.
2	胡晓松, 唐小林. 电动车辆锂离子动力电池建模方法综述[J]. 机械工程学报, 2017, 53(16): 20-31.
	HU Xiaosong, TANG Xiaolin. Review of modeling techniques for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2017, 53(16): 20-31.
3	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): 66-72.
	YANG Jie, WANG Ting, DU Chunyu, et al. Overview of the modeling of lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(1): 66-72.
4	MA Y, RU J, YIN M, et al. Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries[J]. Journal of Applied Electrochemistry, 2016, 46(11): 1119-1131.
5	刘征宇, 杨昆, 魏自红, 等. 包含液相扩散方程简化的锂离子电池电化学模型[J]. 物理学报, 2019, 68(9): 251-258.
	LIU Zhengyu, YANG Kun, WEI Zihong, et al. Electrochemical model of lithium ion battery with simplified liquid phase diffusion equation[J]. Acta Phys. Sin., 2019, 68(9): 251-258.
6	JOHNSON V H. Battery performance models in ADVISOR[J]. Journal of Power Sources, 2002, 110(8): 321-329.
7	BARAI A, WIDANAGE W D, MARCO J, et al. A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells[J]. Journal of Power Sources, 2015, 295(11): 99-107.
8	孙涛, 龚国庆, 陈勇. 锂电池参数辨识模型的设计与研究[J]. 电子技术应用, 2019, 45(3): 127-131.
	SUN Tao, GONG Guoqing, CHEN Yong. Design and research of lithium battery parameters identification model[J]. Application of Electronic Technique, 2019, 45(3): 127-131.
9	杨世春, 麻翠娟. 基于PNGV改进模型的SOC估计算法[J]. 汽车工程, 2015, 37(5): 582-586.
	YANG Shichun, MA Cuijuan. SOC estimation algorithm based on improved PNGV model[J]. Automotive Engineering, 2015, 37(5): 582-586.
10	熊瑞. 基于数据融合的电动车辆动力电池状态估计研究[D]. 北京: 北京理工大学, 2014.
	XIONG Rui. Estimation of battery pack state for electric vehicles using model-data fusion approach[D]. Beijing: Beijing Institute of Technology, 2014.
11	毕军, 康燕琼, 邵赛. 纯电动汽车动力锂电池Nernst模型参数辨识[J]. 汽车工程, 2015, 37(6): 725-730.
	BI Jun, KANG Yanqiong, SHAO Sai. Parameters identification of nernst model for power lithium-ion battery of pure electric vehicles[J]. Automotive Engineering, 2015, 37(6): 725-730.
12	任晓霞, 郭王娜. 电动汽车锂离子电池集总参数RC等效电路模型[J]. 储能科学与技术, 2019, 8(5): 930-934.
	REN Xiaoxia, GUO Wangna. RC equivalent circuit model of lumped parameters for lithium ion batteries in electric vehicles[J]. Energy Storage Science and Technology, 2019, 8(5): 930-934.
13	ZHANG R, XIA B, LI B, et al. State of the art of lithium-ion battery SOC estimation for electrical vehicles[J]. Energies, 2018, 11(7): 1820-1829.
14	QIU C S, HE G, SHI W K, et al. The polarization characteristics of lithium-ion batteries under cyclic charge and discharge[J]. Journal of Solid State Electrochemistry, 2019, 23(6): 1887-1902.
15	ZHANG C, LI K, PEI L, et al. An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries[J]. Journal of Power Sources, 2015, 283(2): 24-36.
16	黄凯, 郭永芳, 李志刚. 考虑迟滞效应影响的动力锂离子电池特性建模[J]. 电网技术, 2017, 41(8): 2714-2721.
	HUANG Kai, GUO Yongfang, LI Zhigang. Modeling of power lithium-ion battery behavior considering hysteresis effect[J]. Power System Technology, 2017, 41(8): 2714-2721.
17	PANG Y Z, ZHANG X Y, TIAN J T. The SOC estimation for power battery using KF Method which parameters are updated by least square method[J]. Applied Mechanics and Materials, 2014, 694(11): 67-72.

[1]	常泽宇, 张之琦, 张晓东, 李丽, 郁亚娟. 基于数据驱动的动力电池健康状态评估平台[J]. 储能科学与技术, 2022, 11(6): 1847-1853.
[2]	周伟, 符冬菊, 刘伟峰, 陈建军, 胡照, 曾燮榕. 废旧磷酸铁锂动力电池回收利用研究进展[J]. 储能科学与技术, 2022, 11(6): 1854-1864.
[3]	张孝远, 张金浩, 蒋亚俊. 基于改进TCN模型的动力电池健康状态评估[J]. 储能科学与技术, 2022, 11(5): 1617-1626.
[4]	王芳, 王峥, 林春景, 张国振, 张贵平, 马天翼, 刘磊, 刘仕强. 新能源汽车动力电池安全失效潜在原因分析[J]. 储能科学与技术, 2022, 11(5): 1411-1418.
[5]	王苏杭, 李建林, 李雅欣, 熊俊杰, 曾伟. 锂离子电池系统低温充电策略[J]. 储能科学与技术, 2022, 11(5): 1537-1542.
[6]	刘春辉, 任宏斌. 基于SOC的动力电池组主动均衡研究[J]. 储能科学与技术, 2022, 11(2): 667-672.
[7]	马天翼, 王伟哲, 林春景, 白广利, 韦振, 刘仕强. 磷酸铁锂电池多阶段利用可行性[J]. 储能科学与技术, 2022, 11(1): 119-126.
[8]	胡建, 林春景, 郝维健, 郑天雷. 动力电池标准体系建设现状及建议[J]. 储能科学与技术, 2022, 11(1): 313-320.
[9]	郑永强, 吴越, 张盼盼, 雷博, 郑耀东. 基于多分支拓扑的梯次利用储能系统电池同期退役协同控制策略[J]. 储能科学与技术, 2021, 10(6): 2283-2292.
[10]	刘霏霏, 鲍荣清, 程贤福, 李骏, 秦武, 杨超峰. 服役工况下车用锂离子动力电池散热方法综述[J]. 储能科学与技术, 2021, 10(6): 2269-2282.
[11]	任璞, 王顺利, 何明芳, 范永存, 曹文, 谢伟. 基于内阻增加和容量衰减双重标定的锂电池健康状态评估[J]. 储能科学与技术, 2021, 10(2): 738-743.
[12]	熊然, 王顺利, 于春梅, 夏黎黎. 基于Thevenin模型和改进扩展卡尔曼的特种机器人锂离子电池SOC估算方法[J]. 储能科学与技术, 2021, 10(2): 695-704.
[13]	张志鸿, 牟俊彦, 孟玉发. 风冷圆柱形锂离子电池系统热失控扩展特性[J]. 储能科学与技术, 2021, 10(2): 658-663.
[14]	樊彬, 姜成龙, 林春景, 李玉鹏, 余八一, 张晋杰, 张良, 高孟洋, 王伟, 解坤, 常宏. 电动汽车用动力电池系统循环寿命试验[J]. 储能科学与技术, 2021, 10(2): 671-678.
[15]	王海民, 王寓非, 胡峰. 石墨-石蜡复合相变材料的圆柱型动力电池组热管理性能[J]. 储能科学与技术, 2021, 10(1): 210-217.

基于动态综合型等效电路模型的动力电池特性分析

Analysis of dynamic battery characteristics based on dynamic synthesis equivalent circuit model

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 17

相关文章 15

编辑推荐

Metrics

本文评价