Research on collaborative control strategy for simultaneous decommissioning based on multi-branch PCS topology of ESS using second-life EV batteries

doi:10.19799/j.cnki.2095-4239.2021.0095

Abstract

Abstract:

A coordinated control strategy based on a multi-branch power conversion system (PCS) topology was designed to address the problem of simultaneous decommissioning among different battery modules in an energy storage system (ESS) using second-life electric vehicle (EV) batteries. Considering the power demand in different periods and the imbalance degree of battery state of health (SOH) in ESS, the priority output mode to utilize batteries with better health status or common output mode of the entire battery group was selected. Based on the distribution of battery state of charge (SOC), a control strategy employing real-time variable current as a positive feedback regulation value was used to achieve coordinated control and output balancing within the ESS's battery cluster. After multiple charges and discharges, the SOH of each battery module in one cluster tends to be asymptotically consistent, thus achieving the goal of decommissioning different batteries simultaneously. Finally, the simulation analysis confirmed that the strategy could simultaneously decommission each retired power battery pack in the energy storage system.

Key words: second-life batteries, simultaneous decommissioning, state of health, state of charge, multi-branch collaborative control

CLC Number:

TM 911

Yongqiang ZHENG, Yue WU, Panpan ZHANG, Bo LEI, Yaodong ZHENG. Research on collaborative control strategy for simultaneous decommissioning based on multi-branch PCS topology of ESS using second-life EV batteries[J]. Energy Storage Science and Technology, 2021, 10(6): 2283-2292.

Figures/Tables 16

Fig. 1

Fig. 2

Fig. 3

Table 1

Pseudo code of collaborative control strategy for simultaneous decommissioning of ESS using second-life EV batteries based on multi-branch topological"

同期退役协同控制策略

输入：P_Q 当前系统母线功率需求；

C_i 系统中各分支电池簇容量标量值；

SOC_i 系统中各分支电池簇当前荷电状态；

SOH_i 系统中各分支电池簇当前健康状态；

η_i 各分支电池簇DC/DC功率转换效率；

η_total 当前系统AC/DC总功率转换效率；

输出：SOC_j 单次充/放电结束后，系统中各分支电池簇荷电状态；

SOH_j 单次充/放电结束后，系统中分支电池簇健康状态；

初始化：P_QC_i SOC_i SOH_i

执行迭代：

1.将系统中各分支电池簇的SOH_i值与最低值SOH_MIN比较，并从大到小排序；

2.如果系统中各分支电池簇SOH_i差异较大，则选择部分SOH_i较优的分支电池簇工作；

（1）如果部分分支电池簇可满足当前功率需求，选择该部分分支电池簇为工作电池簇；如果本次充/放电过程出现电池故障，从剩余分支电池簇中再次选择较优的SOH_i电池簇补充，完成本次充/放电任务；

（2）选择工作分支电池簇中健康状态最好的分支电池簇作为参考目标电池簇；

（3）根据等式(4)计算工作分支电池簇的电流缩放比率K_i；

（4）根据等式(7)计算工作分支电池簇的比例电流I_i_set；

（5）根据等式(8)计算工作分支电池簇的实际工作电流I_i；

（6）该时间段充/放电完成后，采用安时积分法计算分支电池簇当前SOC_i，并计算该充/放电过程分支电池簇SOH_i；

3.如果系统中各分支电池簇SOH_i差异较小，则系统中所有分支电池簇共同工作；

（1）选择工作分支电池簇中健康状态最好的电池簇作为参考目标电池簇；

（2）根据式(4)计算工作分支电池簇的电流缩放比率K_i；

（3）根据式(7)计算工作分支电池簇的比例电流I_i_set；

（4）根据式(8)计算工作分支电池簇的实际工作电流I_i；

（5）该时间段充/放电完成后，采用安时积分法计算分支电池簇当前SOC_i，并计算该充/放电过程分支电池簇SOH_i；

4.如果系统充/放电功率达到 $∑ j = 1 n P Q = P Q t o t a l$ 则停止充/放电。

Table 1

Fig. 4

Fig. 5

Fig. 6

Table 2

Table 3

Fig. 7

Table 4

Fig. 8

Fig. 9

Table 5

Fig. 10

Fig. 11

References 22

1	孙建. 浅谈新能源汽车动力电池应用现状与发展趋势[J]. 汽车实用技术, 2020, 45(17): 11-13.
	SUN J. Discussion on application status and development trend of new energy vehicle power battery[J]. Automobile Applied Technology, 2020, 45(17): 11-13.
2	黄鲲, 宋志鹏, 张思瑶, 等. QC/T 743—2006电动汽车用锂离子蓄电池标准解析及与国际标准体系的比较[J]. 日用电器, 2013(10): 32-36.
	HUANG K, SONG Z P, ZHANG S Y, et al. Analysis and comparison of QC/T 743—2006 lithium-ion batteries for electric vehicles with the international standard[J]. Electrical Appliances, 2013(10): 32-36.
3	孙威, 修晓青, 肖海伟, 等. 退役动力电池梯次利用的容量优化配置[J]. 电器与能效管理技术, 2017(19): 72-76.
	SUN W, XIU X Q, XIAO H W, et al. Capacity allocation optimization of second-use of retired EV batteries[J]. Electrical & Energy Management Technology, 2017(19): 72-76.
4	LI B, ZHANG P P, LI X J, et al. Distributed absorption and half-search approach for economic dispatch problem in smart grids[J]. Energies, 2019, 12(8): doi: 10.3390/en12081527.
5	ZHANG C, WEI Y L, CAO P F, et al. Energy storage system: Current studies on batteries and power condition system[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 3091-3106.
6	韩晓娟, 张婳, 修晓青, 等. 配置梯次电池储能系统的快速充电站经济性评估[J]. 储能科学与技术, 2016, 5(4): 514-521.
	HAN X J, ZHANG H, XIU X Q, et al. Economic evaluation of fast charging electric vehicle station with second-use batteries energy storage system[J]. Energy Storage Science and Technology, 2016, 5(4): 514-521.
7	张广慧. 退役电池储能系统梯次应用研究[D]. 沈阳: 沈阳工程学院, 2018.
	ZHANG G H. Research on bench application of decommissioned battery energy storage system[D]. Shenyang: Shenyang Insitute of Engineering, 2018.
8	张凯, 赵鹏, 王友仁, 等. 基于荷电状态的锂离子电池组主动均衡控制[J]. 中国机械工程, 2020, 31(16): 1931-1939.
	ZHANG K, ZHAO P, WANG Y R, et al. SOC-based active equalization control for lithium-ion battery packs[J]. China Mechanical Engineering, 2020, 31(16): 1931-1939.
9	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.
	LIN W, SHI X M, JIANG L L, et al. Design and practice of A heterogeneous compatible energy storage power station by secondary utilization of retired EV battery[J]. Zhejiang Electric Power, 2020, 39(5): 41-49.
10	YANG Y, ZHU W C, XIE C J, et al. A layered bidirectional active equalization method for retired power lithium-ion batteries for energy storage applications[J]. Energies, 2020, 13(4): doi: 10.3390/en13040832.
11	李丹. 模块化独立控制梯次利用电池储能系统[D]. 北京: 北京交通大学, 2018.
	LI D. Modularly and independently controlled secondary use battery energy storage system[D]. Beijing: Beijing Jiaotong University, 2018.
12	李金东, 古月圆, 王路阳, 等. 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.
	LI J D, GU Y Y, WANG L Y, et al. Review on state of health estimation of retired lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(5): 807-812.
13	董慧峰, 李文启, 牛文迪, 等. 电池储能系统参与电网削峰填谷实用算法[J]. 电测与仪表, 2019, 56(18): 74-78.
	DONG H F, LI W Q, NIU W D, et al. Practical algorithm applied in peak load shifting of battery energy storage system in power grid[J]. Electrical Measurement & Instrumentation, 2019, 56(18): 74-78.
14	LÜ C, LIU S S, SHEN J, et al. Comparison of equivalent circuit models of lithium-ion batteries[J]. Application of Power Technology, 2014, 17(9): 8-11.
15	罗勇, 祁朋伟, 黄欢, 等. 基于容量修正的安时积分SOC估算方法研究[J]. 汽车工程, 2020, 42(5): 681-687.
	LUO Y, QI P W, HUANG H, et al. Study on battery SOC estimation by ampere-hour integral method with capacity correction[J]. Automotive Engineering, 2020, 42(5): 681-687.
16	万亚坤, 李阳春, 马浩天, 等. 基于扩展卡尔曼滤波算法的锂电池SOC估计[J]. 蓄电池, 2020, 57(5): 243-246, 250.
	WAN Y K, LI Y C, MA H T, et al. Estimation of lithium battery SOC based on extended Kalman filter algorithm[J]. Chinese Labat Man, 2020, 57(5): 243-246, 250.
17	申彩英, 左凯. 基于开路电压法的磷酸铁锂电池SOC估算研究[J]. 电源技术, 2019, 43(11): 1789-1791.
	SHEN C Y, ZUO K. Research on SOC estimation of LiFePO₄ batteries based on open circuit voltage method[J]. Chinese Journal of Power Sources, 2019, 43(11): 1789-1791.
18	田冬冬, 李立伟, 杨玉新. 基于改进BP-EKF算法的SOC估算[J]. 电源技术, 2020, 44(9): 1274-1278.
	TIAN D D, LI L W, YANG Y X. Research on SOC estimation based on improved BP-EKF algorithm[J]. Chinese Journal of Power Sources, 2020, 44(9): 1274-1278.
19	郭宏榆, 姜久春, 王吉松, 等. 功率型锂离子动力电池的内阻特性[J]. 北京交通大学学报, 2011, 35(5): 119-123.
	GUO H Y, JIANG J C, WANG J S, et al. Characteristic on internal resistance of lithium-ion power battery[J]. Journal of Beijing Jiaotong University, 2011, 35(5): 119-123.
20	郭琦沛. 锂离子动力电池健康特征提取与诊断研究[D]. 北京: 北京交通大学, 2018.
	GUO Q P. Study on the health feature extraction and diagnosis of power lithium-ion batteries[D]. Beijing: Beijing Jiaotong University, 2018.
21	纪常伟, 潘帅, 汪硕峰, 等. 动力锂离子电池老化速率影响因素的实验研究[J]. 北京工业大学学报, 2020, 46(11): 1272-1282.
	JI C W, PAN S, WANG S F, et al. Experimental study on effect factors of aging rate for power lithium-ion batteries[J]. Journal of Beijing University of Technology, 2020, 46(11): 1272-1282.
22	周秀文. 电动汽车锂离子电池健康状态估计及寿命预测方法研究[D]. 长春: 吉林大学, 2016.
	ZHOU X W. Research on SOH estimation and RUL prediction methods of lithium-ion battery for electric vehicles[D]. Changchun: Jilin University, 2016.

SOC/％	R_CH/mΩ	R_DIS/mΩ
12.36	0.1941	0.2178
20.31	0.1850	0.2126
30.27	0.1906	0.2020
40.24	0.1764	0.1876
50.20	0.1767	0.1864
60.16	0.1693	0.1702
70.12	0.1747	0.1704
80.08	0.1869	0.1798
90.04	0.1678	0.1533
99.20	0.1470	0.1367

SOC/%	U/V
99.20	3.32945
90.04	3.32866
80.08	3.32996
70.12	3.32125
60.16	3.29464
50.20	3.28867
40.24	3.28835
30.27	3.27352
20.31	3.23714
12.36	3.20087

参数	数值
a	2.4945×10^-14
b	2.2057×10^-11
c	-8.9697×10^-9
d	1.2117×10^-6
e	-7.6262×10^-5
f	2.3008×10^-3
g	-2.7485×10^-2
h	3.3075

NO.	Qty_i	C_i/(A·h)	SOC_i/%	I_i_MAX/A	SOH_i/%
1	10×12S	250	100	50	90.0
2	10×12S	230	100	50	87.5
3	10×12S	210	100	40	85.0
4	10×12S	190	100	40	82.5
5	9×12S	170	100	30	80.0
6	11×12S	150	100	30	77.5
7	12×10S	180	100	40	75.0
8	12×10S	160	100	30	81.0

[1]	Tian WU, Mincheng LIN, Hao HAI, Haiyu SUN, Zhaoyin WEN, Fuyuan MA. Development of high-power Ni-MH battery system for primary frequency modulation [J]. Energy Storage Science and Technology, 2022, 11(7): 2213-2221.
[2]	Yanwen DAI, Aiqing YU. Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation [J]. Energy Storage Science and Technology, 2022, 11(5): 1641-1649.
[3]	Feng TIAN, Zhijiang CHENG, Handi YANG, Tianxiang YANG. Fault-tolerant control strategy for modular multi-level hybrid converter battery energy storage system [J]. Energy Storage Science and Technology, 2022, 11(5): 1583-1591.
[4]	Chunhui LIU, Hongbin REN. Research on active equalization of power batteries based on state of charge [J]. Energy Storage Science and Technology, 2022, 11(2): 667-672.
[5]	Pengchao HUANG, Jiaqiang E. State estimation of lithium-ion battery based on dual adaptive Kalman filter [J]. Energy Storage Science and Technology, 2022, 11(2): 660-666.
[6]	Shuai WANG, Hongyan MA, Jiaming DOU, Yingda ZHANG, Shengyan LI, Lujin HU. Estimation of lithium-ion battery state of charge based on UGOA-BP [J]. Energy Storage Science and Technology, 2022, 11(1): 258-264.
[7]	Xiaozhi GAO, Lei WANG, Jin TIAN, Jialu LIU, Qinghua LIU. Research on hybrid energy storage power distribution strategy based on parameter optimization variational mode decomposition [J]. Energy Storage Science and Technology, 2022, 11(1): 147-155.
[8]	Hang SU, Huaibin GAO, Zhengguang LI, Hongjun LI, Jianfei LIU, Xiaobo ZUO, Linlin JI. State of charge estimation of Li-ion battery based on BCRLS-ACKF [J]. Energy Storage Science and Technology, 2021, 10(6): 2334-2341.
[9]	Fan WANG, Yongsheng SHI, Boqin LIU, Yujie ZUO, Zheng FU, Jamsher ALI. Health state estimation of lithium-ion batteries based on attention augmented BiGRU [J]. Energy Storage Science and Technology, 2021, 10(6): 2326-2333.
[10]	Linxuan HE, Wenyan LI. Simulation of the primary frequency modulation process of thermal power units with the auxiliary of flywheel energy storage [J]. Energy Storage Science and Technology, 2021, 10(5): 1679-1686.
[11]	Yifeng FENG, Jiani SHEN, Haiying CHE, Zifeng MA, Yijun HE, Wen TAN, Qingheng YANG. State of health prediction for sodium-ion batteries [J]. Energy Storage Science and Technology, 2021, 10(4): 1407-1415.
[12]	Bin LI, Lei XU, Zheng ZHENG, Dandan HU, Guobin ZHANG. Multiple staggered symmetric equalization scheme based on Cuk circuits [J]. Energy Storage Science and Technology, 2021, 10(4): 1400-1406.
[13]	Xiaoli ZHANG, Yuetong WANG, Jinsong XIA, Yingying ZHANG. Estimation of the SOC of lithium batteries based on an improved CDKF algorithm [J]. Energy Storage Science and Technology, 2021, 10(4): 1454-1462.
[14]	Chengxin SHAN, Liwei LI, Yuxin YANG. SOC of estimation of lithium battery based on IACO-PF [J]. Energy Storage Science and Technology, 2021, 10(3): 1145-1152.
[15]	Ke LI, Juyi MU, Yi JIN, Jiajia XU, Pengjie LIU, Qingsong WANG, Huang LI. Fire risk of lithium iron phosphate battery [J]. Energy Storage Science and Technology, 2021, 10(3): 1177-1186.