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

doi:10.19799/j.cnki.2095-4239.2020.0395

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (2): 738-743.doi: 10.19799/j.cnki.2095-4239.2020.0395

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

任璞¹(), 王顺利¹(), 何明芳¹, 范永存¹, 曹文¹, 谢伟²

^1.西南科技大学信息工程学院，四川绵阳 621000
^2.四川华泰电气股份有限公司，四川遂宁 629000

收稿日期:2020-12-07 修回日期:2020-12-22 出版日期:2021-03-05 发布日期:2021-03-05
通讯作者: 王顺利 E-mail:2543542553@qq.com;497420789@qq.com
作者简介:任璞（1998—），男，硕士研究生，主要开展锂电池健康状态评估研究、荷电状态估计，E-mail：2543542553@qq.com；
基金资助:
国家自然科学基金项目(61801407);四川省科技厅重点研发项目(2018GZ0390);四川省教育厅科研项目(17ZB0453);西南科技大学素质类教改（青年发展研究）专项项目(18xnsu12)

State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading

Pu REN¹(), Shunli WANG¹(), Mingfang HE¹, Yongcun FAN¹, Wen CAO¹, Wei XIE²

^1.School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, Sichuan, China
^2.Sichuan Huatai Electric Co. Ltd, Suining 629000, Sichuan, China

Received:2020-12-07 Revised:2020-12-22 Online:2021-03-05 Published:2021-03-05
Contact: Shunli WANG E-mail:2543542553@qq.com;497420789@qq.com

摘要/Abstract

摘要：

锂电池健康管理对推动其广泛应用具有重要意义。立足锂电池应用现状，为解决复杂工况下健康状态估算困难、精度低等问题，以三元锂电池为研究对象，建立二阶RC等效模型对电池的工作特性进行表征，从内阻增加及容量衰减两方面分析健康状态变化。考虑荷电状态对内阻的影响，采用标定荷电状态，在放电情况下分为0～1 s及1～10 s两区间分析其内阻变化；并以温度为参量，扩大测量区间，更精确地反映不同温度下容量衰减。实验结果表明，在0～1 s内，锂电池健康状态同荷电状态并无关系；1～10 s内，锂电池健康状态下降速率同荷电状态呈反比；且在不同温度下的完全放电实验表明，实验用锂电池在25 ℃下健康状态最为优良。表明二阶RC模型能够较好地对锂电池健康状态进行估算，收敛速度快且跟踪效果好，基于内阻增加的健康状态估算误差控制在1.0%以内、基于容量衰减的健康状态估算误差控制在0.8%以内，有利于完善锂电池健康状态评估方法，推动锂电池应用。

关键词: 锂电池健康管理, 内阻增加, 容量衰减, 二阶RC等效电路模型, 健康状态评估

Abstract:

The management of the state of health (SOH) and life prediction of Li-ion batteries are of great significance to promote their wide application. In this paper, on the basis of the situation of application of Li-ion batteries, to remedy the real-time estimation difficulty and low precision under various working conditions, a ternary Li-ion battery is taken as the research object, a second-order RC equivalent circuit model is established to characterize the operating characteristics of the battery, and the performance of the Li-ion battery is studied and analyzed on the basis of the experiments under various working conditions. The changes in the state of health were analyzed from two directions: internal resistance increase and capacity fading. The effect of the state of charge (SOC) on internal resistance is considered. The SOC is calibrated under discharge conditions, and the change of internal resistance is analyzed from 0 to 1 s and 1 to 10 s, respectively. We established the corresponding calculation formula of the evaluation method on the basis of the internal resistance increase of the Li-ion battery and determined the changes of state of health under different initial SOCs in two time ranges. The temperature is taken as a parameter, and the measurement interval is extended to reflect the capacity fading at different temperatures more accurately by multiple full charging and discharging experiments. We also established the corresponding mathematical formula of the state of health on the basis of capacity fading for the Li-ion battery and obtained the changes in the battery's state of health at different temperatures. The experimental results show that within 0—1 s, there is no relationship between the state of health of the Li-ion battery and the initial SOC. Within 1—10 s, the decline rate of the state of health is inversely proportional to the initial SOC. The complete discharging experiments at different temperatures show that the Li-ion battery has the best health condition at 25℃, so the test battery should be operated at this temperature as far as possible. It is indicated that the second-order RC equivalent circuit model can estimate the state of health of Li-ion batteries very well; the convergence rate is fast, and the tracking effect is good. The state of health estimation errors based on internal resistance increase and capacity fading are controlled within 1.0% and 0.8%, respectively. Improving the state of health estimation method helps promote the applications of Li-ion batteries.

Key words: health management of Li-ion battery, internal-resistance increasing, capacity fading, second-order RC equivalent circuit model, state of health estimation

中图分类号:

TM 911

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.

Pu REN, Shunli WANG, Mingfang HE, Yongcun FAN, Wen CAO, Wei XIE. State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading[J]. Energy Storage Science and Technology, 2021, 10(2): 738-743.

图/表 6

图1

表1

表2

表3

图2

图3

参考文献 22

1	LI Jie, ADEWUYE K, LOTFI 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.
2	LI Nan, GAO Feng, HAO Tianqu, et al. SOH balancing control method for the MMC battery energy storage system[J]. IEEE Transactions on Industrial Electronics, 2018, 65(8): 6581-6591.
3	KIM Jungsoo, CHUN Huiyong, KIM Minho, et al. Data-driven state of health estimation of Li-ion batteries with RPT-reduced experimental data[J]. IEEE Access, 2019, 7: 106987-106997.
4	AUNG Htet, SOON Jing Jun, GOH Shu Ting, et al. Battery management system with state-of-charge and opportunistic state-of-health for a miniaturized satellite[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(4): 2978-2989.
5	LIU Datong, YIN Xuehao, SONG Yuchen, et al. An on-line state of health estimation of lithium-ion battery using unscented particle filter[J]. IEEE Access, 2018, 6: 40990-41001.
6	TIAN Jinpeng, XIONG Rui, YU Quanqing. Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2): 1576-1584.
7	ZHENG Xueying, DENG Xiaogang. State-of-health prediction for lithium-ion batteries with multiple gaussian process regression model[J]. IEEE Access, 2019, 7: 150383-150394.
8	ZHOU Danhua, LI Zhanying, ZHU Jiali, et al. State of health monitoring and remaining useful life prediction of lithium-ion batteries based on temporal convolutional network[J]. IEEE Access, 2020, 8: 53307-53320.
9	SHEN Ping, OUYANG Minggao, LU Languang, et al. The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 92-103.
10	GUHA A, PATRA A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification, 2018, 4(1): 135-146.
11	LEIJEN P, STEYN-ROSS D A, KULARATNA N. Use of effective capacitance variation as a measure of state-of-health in a series-connected automotive battery pack[J]. IEEE Transactions on Vehicular Technology, 2018, 67(3): 1961-1968.
12	TANG Jia, LIU Qisheng, LIU Shiqi, et al. A health monitoring method based on multiple indicators to eliminate influences of estimation dispersion for lithium-ion batteries[J]. IEEE Access, 2019, 7: 122302-122314.
13	VIDAL C, MALYSZ P, KOLLMEYER P, et al. Machine learning applied to electrified vehicle battery state of charge and state of health estimation: state-of-the-art[J]. IEEE Access, 2020, 8: 52796-52814.
14	LI Yuanyuan, ZHONG Shouming, ZHONG Qishui, et al. Lithium-ion battery state of health monitoring based on ensemble learning[J]. IEEE Access, 2019, 7: 8754-8762.
15	MARTINEZ-LASERNA E, SARASKETA-ZABALA E, SARRIA I V, et al. Technical viability of battery second life: a study from the ageing perspective[J]. IEEE Transactions on Industry Applications, 2018, 54(3): 2703-2713.
16	MORAL C G, LABORDA D F, ALONSO L S, et al. Battery internal resistance estimation using a battery balancing system based on switched capacitors[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5363-5374.
17	SALDANA G, MARTIN J I S, ZAMORA I, et al. Empirical electrical and degradation model for electric vehicle batteries[J]. IEEE Access, 2020, 8: 155576-155589.
18	SONG Ziyou, WANG Hao, HOU Jun, et al. Combined state and parameter estimation of lithium-ion battery with active current injection[J]. IEEE Transactions on Power Electronics, 2020, 35(4): 4439-4447.
19	XIANG Ming, HE Ye, ZHANG Hui, et al. State-of-health prognosis for lithium-ion batteries considering the limitations in measurements via maximal information entropy and collective sparse variational gaussian process[J]. IEEE Access, 2020, 8: 188199-188217.
20	YUN Zhonghua, QIN Wenhu. Remaining useful life estimation of lithium-ion batteries based on optimal time series health indicator[J]. IEEE Access, 2020, 8: 55447-55461.
21	ZHANG Shuzhi, GUO Xu, DOU Xiaoxin, et al. A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis[J]. Journal of Power Sources, 2020, 479: 228740.
22	CHEN Dajin, TAO Chuanying, CHEN Tongzhou, et al. Pomegranate-like silicon-based anodes self-assembled by hollow-structured Si/void@C nanoparticles for Li-ion batteries with high performances[J]. Nanotechnology, 2021, 32(9): 095402.

SOC	a	b	c	I_L/A	τ₁	τ₂
0.8	3.732	0.0638	0.02238	68.4771	27.12	0.4055
0.7	3.631	0.07757	0.02208	68.474	32.34	0.4073
0.6	3.528	0.05834	0.02185	68.4678	25.65	0.4103
0.5	3.456	0.05124	0.02161	68.4709	30.33	0.4025
0.4	3.422	0.01984	0.04562	68.4709	26.85	0.4245
0.3	3.396	0.05502	0.02052	68.474	32.02	0.4251

SOC	U_OC/V	R₀/Ω	R₁/Ω	C₁/F	R₂/Ω	C₂/F
0.8	3.9368	0.003151419	0.000931698	29108.13404	0.000326825	1240.726723
0.7	3.832339457	0.003101023	0.001132839	28547.75248	0.000322458	1263.109611
0.6	3.7252	0.003040845	0.000852079	30102.82945	0.000319128	1285.690542
0.5	3.6527	0.003004196	0.000748347	40529.32078	0.000315609	1275.314079
0.4	3.61845	0.002985940	0.000289758	1465.01497	0.000666268	40299.07201
0.3	3.59065	0.002974122	0.000803517	39849.82697	0.000299676	1418.533012

SOC	0～1 s内拟合曲线参数			1～10 s内拟合曲线参数
SOC	a	b	R²	a	b	R²
0.3	5.2571	25.075	0.9950	1.4455	32.811	0.8845
0.4	5.2552	24.923	0.9919	1.7110	31.854	0.8963
0.5	5.2593	24.995	0.9892	2.1564	31.015	0.9292
0.6	5.2547	24.998	0.9938	2.7394	29.518	0.9687
0.7	5.2557	24.954	0.9934	3.5778	27.510	0.9949
0.8	5.2597	24.912	0.9922	4.9394	22.249	0.9872

[1]	刘杭鑫, 陈现涛, 孙强, 赵晨曦. 软包锂离子电池真空环境下循环性能特性[J]. 储能科学与技术, 2022, 11(6): 1806-1815.
[2]	程广玉, 刘新伟, 梅悦旎, 顾洪汇, 杨丞, 王可. 锂离子电池高温贮存容量衰减分析[J]. 储能科学与技术, 2022, 11(5): 1339-1349.
[3]	赵鹤, 韩策, 程小露, 郝维健, 徐涵颖, 耿萌萌, 杨凯, 赵丰刚, 邱新平. 采用阳极预锂化技术的锂离子电池高倍率老化容量衰减机理研究[J]. 储能科学与技术, 2021, 10(2): 454-461.
[4]	卢婷, 杨文强. 锂离子电池全生命周期内评估参数及评估方法综述[J]. 储能科学与技术, 2020, 9(3): 657-669.
[5]	李金东, 古月圆, 王路阳, 吴旭. 退役锂离子电池健康状态评估方法综述[J]. 储能科学与技术, 2019, 8(5): 807-812.
[6]	赵保国，李克锋，王冠，刘虹，史佳超，尤梅，张晓霞，谢巧. 锌银贮备电池加速寿命试验方法研究[J]. 储能科学与技术, 2018, 7(1): 141-.

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

State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 22

相关文章 6

编辑推荐

Metrics

本文评价