Research on Discharge State Estimation Method of Retired Power Battery

doi:10.16628/j.cnki.2095-8188.2021.10.006

Abstract

Abstract:

Based on the existing battery characteristics data,this paper uses entropy value method to calculate the weight of the battery characreristics,and then uses the grey correlation analysis method to calculate the correlation between the battery character is tics,and lastly takes the dischargeable capaeity of retired power battery as indicators of discharge stare estimation,combining support vector machine (SVR) and particle swarm optimization(PSO) to establish the index evaluation model.Fially,the chargeng and discharging test of the rerired power bactery verifies the effectiveness of the method.The test results show that the estimation method can be used for retired power battery status tracking.

Key words: retired power battery, battery characteristics, discharge state estimation, entropy value method, grey correlation analysis method, support vector machine, particle swarm optimization

CLC Number:

TM912

YAN Hu, HU Jing, WANG Zhe. Research on Discharge State Estimation Method of Retired Power Battery[J]. LOW VOLTAGE APPARATUS, 2021, 0(10): 33-41.

Figures/Tables 11

指标	定义	描述	用途	与电池放电相关性
OCV	u_开=φ₊-φ_-	φ₊—电池正极电位 φ_-—电池负极电位	分析电池性能,包括估计SOC和SOH等参数	与电池放电直接相关性较弱
SOC	SOC= $Q s Q z$	相同条件下: Q_s—剩余容量 Q_z—额定容量充满电时SOC=1 放空电时SOC=0	衡量电池剩余使用容量的相对大小	用于防止电池的过充和过放
SOE	SOE(t)= $E remain E rated$	SOE(t)—t时刻状态 E_remain—剩余能量 E_rated—额定能量	反映电池能量使用情况	与SOC类似,计算难度大
SOH	SOH= $Q discharge Q rated$	Q_discharge—额定容量 Q_rated—剩余容量二者为相同条件下测得	电池当前的特征指标与标称指标的偏离程度	与电池放电直接相关性较弱
SOP	SOP=SOP_c +(SOP_p-SOP_c)·f	P_cha—充电峰值功率 P_dis—放电峰值功率 U_max—充电截止电压 U_min—放电截止电压 R₀—电池内阻	储能单元在当前荷电状态下能够释放和接受的最大功率	峰值功率和容量决定了电池的最大充放电能力,可用于放电能力评估

References 24

[1]	KIM T H, PARK J S, CHANG S K, et al. The current move of lithium ion batteries towards the next phase[J]. Advanced Energy Materials, 2012, 2(7):860-872. doi: 10.1002/aenm.201200028
[2]	刘云, 王海欣, 黄海宏. 退役锂电池充放电系统[J]. 电器与能效管理技术, 2019(6):53-57.
[3]	任军, 王凯, 任宝森. 基于改进模型和无迹卡尔曼滤波的锂离子电池荷电状态估计[J]. 电器与能效管理技术, 2019(4):64-70,78.
[4]	王震坡, 孙逢春, 林程. 不一致性对动力电池组使用寿命影响的分析[J]. 北京理工大学学报, 2006(7):577-580.
[5]	李杰. 纯电动汽车电池均衡管理系统设计与研究[D]. 太原:太原理工大学, 2018.
[6]	张凤麒. 车辆用锂离子动力电池特性分析[J]. 机电技术, 2021(1):48-52,67.
[7]	刘宗锋, 姜宁, 汪卫东, 等. 不同温度下三元锂电池自放电量预测方法[J]. 重庆理工大学学报(自然科学), 2021, 35(1):25-30.
[8]	裴普成, 陈嘉瑶, 吴子尧. 锂离子电池自放电机理及测量方法[J]. 清华大学学报(自然科学版), 2019, 59(1):53-65.
[9]	ZHANG H, ZHAO L, CHEN Y. A lossy counting-based state of charge estimation method and its application to electric vehicles[J]. Energies, 2015, 8(12):13811-13828. doi: 10.3390/en81212395
[10]	PLETT G L. Extended Kalman filtering for battery management systems of LiPB-based HE-V battery packs:Part3.State and parameter estimation[J]. Joumal of Power sources, 2004, 134(2):277-292.
[11]	ZHANG W, SHI W, MA Z. Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery[J]. Journal of Power Sources, 2015, 289:50-62. doi: 10.1016/j.jpowsour.2015.04.148
[12]	ZENG X, LI J, SINGH N. Recycling of spent lithium-ion battery:a critical review[J]. Critical Reviews in Environmental Science and Technology, 2014, 44(10):1129-1165. doi: 10.1080/10643389.2013.763578
[13]	徐虹, 贺鹏, 艾欣. 电动汽车充电功率需求分析模型研究综述[J]. 现代电力, 2012, 29(3):51-56.
[14]	SHANNON C E, A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27(3):379-423. doi: 10.1002/bltj.1948.27.issue-3
[15]	叶宗裕. 关于多指标综合评价中指标正向化和无量纲化方法的选择[J]. 浙江统计, 2003(4):24-25.
[16]	李晴, 陈鹏宇. 指标无量纲化方法对熵权法评价结果的影响[J]. 科技资讯, 2019(7):184-186.
[17]	刘思峰, 杨英杰. 灰色系统研究进展(2004—2014)[J]. 南京航空航天大学学报, 2015, 47(1):1-18.
[18]	CHAWLA N V, JAPKOWICZ N, LCZ A K. Editorial:Special issue on learning from imbalanced data sets[J]. ACM SIGKDD Explorations News Letter, 2004. 6(1):1-6.
[19]	SHAWE-TAYLOR J, CRISTIANINI N. Kernel methods for pattern analysis[M]. Cambridge: Cambridge University Press, 2004.
[20]	POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization[J]. Swarm Intelligence, 2007(1):33-57.
[21]	KENNEDY J. Particle swarm optimization:encyclopedia of machine learning[M]. New York:Springer US, 2010.
[22]	谢宝江, 娄伟明, 罗扬帆, 等. 基于H_∞无迹卡尔曼滤波的退役锂离子电池SOC估计[J]. 浙江电力, 2020, 39(8):53-60.
[23]	林武, 史新民, 蒋丽丽, 等. 动力电池梯次利用的异构储能电站设计与实践[J]. 浙江电力, 2020, 39(5):41-49.
[24]	明彤彤, 王凯, 田冬冬, 等. 基于LSTM神经网络的锂离子电池荷电状态估算[J]. 广东电力, 2020, 33(3):26-33.

序号	电流 I/A	电压 U/V	SOC/%	OCV /V	内阻 /mΩ	SOP /W
1	100.6	41.917	97.86	40.106	18.00	419
2	100.6	41.920	97.88	40.108	18.01	4 412
3	100.6	41.928	97.90	40.110	18.07	4 392
4	100.6	41.934	97.93	40.111	18.12	4 378
5	100.6	41.946	97.95	40.113	18.22	4 350
6	100.6	41.954	97.97	40.115	18.28	4 331
7	100.6	41.959	97.99	40.117	18.31	4 319
8	100.6	41.965	98.01	40.119	18.35	4 305
9	100.6	41.978	98.03	40.123	18.43	4 275
10	100.6	41.984	98.05	40.128	18.45	4 262
11	100.6	41.992	98.07	40.132	18.49	4 243
12	100.6	41.999	98.09	40.136	18.52	4 228

序号	电流 I/A	电压 U/V	SOC/%	OCV /V	内阻 /mΩ	SOP /W
1	100.6	3.601	95.48	3.333	2.66	361
2	100.6	3.577	92.00	3.314	2.61	394
3	100.6	3.571	95.10	3.329	2.41	406
4	100.6	3.589	92.57	3.316	2.71	377
5	100.6	3.502	83.00	3.305	1.96	542
6	100.6	3.510	87.00	3.307	2.02	523
7	100.6	3.593	95.57	3.334	2.57	372
8	100.6	3.588	95.76	3.336	2.50	379
9	100.6	3.496	81.00	3.304	1.91	558
10	100.6	3.564	93.88	3.321	2.42	416
11	100.6	3.595	85.00	3.306	2.87	368
12	100.6	3.548	85.00	3.306	2.41	440

电池序号	开路状态/V	放电/C	充电/C
1	0.037 3	0.792 3	0.815 7
2	0.000 0	0.618 0	0.741 6
3	0.052 8	0.740 1	0.764 1
4	0.000 0	0.611 7	0.772 9
5	0.000 0	0.593 0	0.633 0
6	0.024 6	0.605 5	0.642 7
7	0.039 9	0.779 2	0.811 5
8	0.091 4	0.824 8	0.811 5
9	0.010 3	0.599 2	0.618 5
10	0.042 6	0.656 1	0.737 0
11	0.007 2	0.599 2	0.695 3
12	0.005 0	0.593 0	0.662 0
U	3.525 6	0.347 2	0.271 8