Low temperature charging performance optimization of lithium battery based on BP-PSO Algorithm

doi:10.19799/j.cnki.2095-4239.2020.0172

Abstract

Abstract:

In order to improve the low temperature charge performance of lithium-ion battery, reduce the aging rate and charging time in low temperature, thereby promoting the promotion of new energy vehicles in the low temperature region, made a series of lithium-ion battery charge and discharge cycle aging test in low temperature, based on a large number of low temperature test, charge and discharge test data under low temperature environment is analyzed under different charge conditions on the influence of lithium-ion battery aging rate. A BP neural network model for low temperature charge aging rate estimation of lithium-ion batteries was established. On this basis, particle swarm optimization algorithm is introduced to optimize the traditional CC-CV charging strategy, and the whole charging process is divided into two stages. In the first stage, particle swarm optimization algorithm is used to find the approximate optimal charging curve before reaching the charging cut-off voltage, and in the second stage, conventional constant voltage charging is used. Based on the capacity decline rate estimation model at low temperature, low temperature aging rate charging and charging time weighted summation of multi-objective optimization equation as the fitness function of particle swarm optimization algorithm, introduction of the weights coefficient "g" in the fitness function to weigh the two optimization goal, iterative optimization using particle swarm optimization algorithm. The results show that the model has high estimation accuracy for the decline rate of low temperature charging capacity, and the optimized charging strategy can effectively reduce the low temperature charging aging rate and charging time of lithium batteries.

Key words: lithium ion batteries, BP neural network, particle swarm optimization algorithm, low temperature charge, battery aging, the charging strategy

CLC Number:

TP 29

Taihua WANG, Shujie ZHANG, Jin'gan CHEN. Low temperature charging performance optimization of lithium battery based on BP-PSO Algorithm[J]. Energy Storage Science and Technology, 2020, 9(6): 1940-1947.

Figures/Tables 14

Table 1

Table 2

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 3

Table 4

References 14

1	中国汽车工业协会.荷兰研究电动车充电数据低温充电时间更长[EB/OL]..
	China Association of Automobile Manufacturers. Dutch research on ev charging data longer charging time at low temperature[EB/OL]. .
2	中国汽车工业协会.特斯拉挪威栽跟头:电动车现低温电池故障[EB/OL]..
	China Association of Automobile Manufacturers. Tesla stumbles in Norway: Electric cars have low temperature battery failure[EB/OL]. http: //.
3	贺刚, 杨晨戈, 邓柯军, 等. 纯电动汽车用锂离子电池低温充电性能研究[C]//2015中国汽车工程学会年会, 上海, 2015.
	HE Gang, YANG Chenge, DENG Kejun, et al. Investigation of charging characteristic in the low temperature for electric vehicles lithium in battery[C]//2015 Proceedings of China Society of Automotive Engineering, 2015
4	杨莹莹, 刘耀锋, 魏学哲. 车用锂离子电池低温性能研究[J]. 机电一体化, 2016, 22(6): 30-35, 46.
	YANG Y Y, LIU Y F, WEI X Z. Research on low temperature performance of vehicle lithium ion battery[J]. Mechatronics, 2016, 22(6): 30-35, 46.
5	谢晓华, 解晶莹, 夏保佳. 锂离子电池低温充放电性能的研究[J]. 化学世界, 2008(10): 581-583.
	XIE X H, XIE J Y, XIA B J. Study on the performance of lithium ion battery charging and discharging at low temperature[J]. Chemical World, 2008 (10): 581-583.
6	PETZL M, KASPER M, DANZER M A. Lithium plating in a commercial lithium-ion battery- A low-temperature aging study[J]. Journal of Power Sources, 2015, 275: 799-807.
7	DOYLE M, FULLER T J N. Modeling of galvanostatic charge and discharge of the lithium/ polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6): 1526-1533.
8	DOYLE M, NEWMAN J, GOZDZ A S. Comparison of modeling predictions with experimental data from plastic lithium ion cells[J]. Journal of the Electrochemical Society, 1996, 143(6): 533-543.
9	WANG J, LIU P, HICKS-GARNER J, et al. Cycle-life model for graphite-LiFePO₄ cells[J]. Journal of Power Sources, 2011,196(8): 3942-3948.
10	ZHANG C, JIANG J, GAO Y, et al. Charging optimization in lithium-ion batteries based on temperature rise and charge time[J]. Applied Energy, 2017, 194: 569-577.
11	HSIEH G, CHEN L, HUANG K. Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller[J]. IEEE Transactions on Industrial Electronics, 2001, 48(3): 585-593.
12	黄健, 严胜刚. 基于区域划分自适应粒子群优化的超短基线定位算法[J]. 控制与决策, 2019, 34(9): 2023-2030.
	HUANG J, YAN S G. An ultra-short baseline location algorithm based on zonal Adaptive particle Swarm optimization[J]. Control and Decision Making, 2019, 34(9): 2023-2030.
13	EBERHART R, KENNEGY J. A new optimizer using particle swarm theory[C]//Proc of the 6th Symposium on Micro Machine & Human Science. Nagoya, 2002: 39-43.
14	耿焕同, 周山胜, 陈哲, 等. 基于分解的预测型动态多目标粒子群优化算法[J]. 控制与决策, 2019, 34(6): 1307-1318.
	GENG H T, ZHOU S S, CHEN Z, et al. Predictive dynamic multi-objective particle swarm optimization algorithm based on decomposition[J]. Control and Decision Making, 2019, 34(6): 1307-1318.

属性	参数
正极材料	NCA
尺寸型号	18650
额定容量	2900mA·h
标称电压	3.6V
最大充电截止电压	(4.2±0.5) V
最大充电电流	1C
放电截止电压	2.75V

步骤	电流/C	步骤结束条件	温度
静置	0	3 h	预设
CC-CV充电	预设倍率	达到预设的截止电压，充电电流下降到0.2 C	预设
静置	0	3 h	25 ℃
CC放电	0.5	电压下降到2.5 V	25 ℃
放置	0	1 h	25 ℃

测试工况	CC-CV	优化后	衰退速率减少
0℃_20cyc_4.2V	0.5402	0.308	42.98%
0℃_40cyc_4.2V	0.6961	0.4339	37.66%
-5℃_15cyc_4.2V	0.7335	0.6862	6.44%
-5℃_30cyc_4.2V	0.8344	0.8288	0.67%
-10℃_10cyc_4.1V	1.4762	0.9924	24.67%
-10℃_20cyc_4.1V	1.9604	1.1703	40.30%
-15℃_8cyc_4.2V	3.0896	1.5769	48.96%
-20℃_6cyc_4.2V	3.6069	2.0462	43.27%

测试工况	CC-CV	优化后	充电时间减小
0℃_20cyc_4.2 V	3.03 h	2.769 h	8.61%
0℃_40cyc_4.2 V	2.97 h	2.642 h	11.04%
-5℃_15cyc_4.2 V	2.88 h	2.63 h	8.68%
-5℃_30cyc_4.2 V	3.15 h	2.797 h	11.2%
-10℃_10cyc_4.1 V	3.18 h	3.167 h	0.41%
-10℃_20cyc_4.1 V	3.32 h	3.25 h	2.11%
-15℃_8cyc_4.2 V	3.58 h	3.457 h	3.44%
-20℃_6cyc_4.2 V	3.95 h	3.624 h	8.25%

[1]	Zhongmin REN, Bin WANG, Shuaishuai CHEN, Hua LI, Zhenlian CHEN, Deyu WANG. Mechanics-induced degradation on layer-structured cathodes and remedies to address it [J]. Energy Storage Science and Technology, 2022, 11(3): 948-956.
[2]	Mengmeng GENG, Maosong FAN, Kai YANG, Guangjin ZHAO, Zhen TAN, Fei GAO, Mingjie ZHANG. Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network [J]. Energy Storage Science and Technology, 2022, 11(2): 673-678.
[3]	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.
[4]	Chengzhi KE, Bensheng XIAO, Miao LI, Jingyu LU, Yang HE, Li ZHANG, Qiaobao ZHANG. Research progress in understanding of lithium storage behavior and reaction mechanism of electrode materials through in situ transmission electron microscopy [J]. Energy Storage Science and Technology, 2021, 10(4): 1219-1236.
[5]	Dechao GUO, Yimin GUO, Qiwen ZHANG, Xiangyun CI, Fengrong HE. Preparation and characterization of solvent-free dry electrodes for lithium ion batteries [J]. Energy Storage Science and Technology, 2021, 10(4): 1311-1316.
[6]	Yilong LIN, Min XIAO, Dongmei HAN, Shuanjin WANG, Yuezhong MENG. Research progress in formation technique for LIBs [J]. Energy Storage Science and Technology, 2021, 10(1): 50-58.
[7]	Yuanjin ZHANG, Huawei WU, Congjin YE. Estimation of the SOC of a battery based on the AUKF-BP algorithm [J]. Energy Storage Science and Technology, 2021, 10(1): 237-241.
[8]	Xintong LI, Linchen ZHANG, Huanrui ZHANG, Botao ZHANG, Guanglei CUI. Research progress of liquid-crystalline electrolytes in lithium ion batteries [J]. Energy Storage Science and Technology, 2020, 9(6): 1595-1605.
[9]	Xingang MA, Yuwei ZANG, Lianke XIE, Jianguang YIN, Guoying ZHANG, Rongchun MA, Xianzheng YUAN. Engineering pseudocapacitive lithium storage based on ultra-fine SnS₂-carbon3D microstructure [J]. Energy Storage Science and Technology, 2020, 9(5): 1467-1471.
[10]	Xuejiao NIE, Jinzhi GUO, Meiyi WANG, Zhenyi GU, Xinxin ZHAO, Xu YANG, Haojie LIANG, Xinglong WU. Using spent lithium manganate to prepare Li_0.25Na_0.6MnO₂as cathode material in sodium-ion batteries [J]. Energy Storage Science and Technology, 2020, 9(5): 1402-1409.
[11]	MA Tengfei, MA Chao, SUN Rui, JI Hongmei, YANG Gang. Freeze-drying assisted synthesis of mno/reduced graphene composite and the improved rate cyclic performance for lithium ion batteries [J]. Energy Storage Science and Technology, 2020, 9(4): 1044-1051.
[12]	WANG Taihua, ZHANG Shujie, CHEN Jingan. Low temperature charging aging modeling and optimization of charging strategy for lithium batteries [J]. Energy Storage Science and Technology, 2020, 9(4): 1137-1146.
[13]	ZOU Jian, WANG Bojun, YANG Jiachao, NIU Xiaobin, WANG Liping. Electrochemical performance of β-Li_0.3V₂O₅ as a lithium-ion battery cathode material [J]. Energy Storage Science and Technology, 2020, 9(2): 353-360.
[14]	ZHOU Xiaolong, OU Xuewu, LIU Qirong, TANG Yongbing. Research progress on dual-ion batteries [J]. Energy Storage Science and Technology, 2020, 9(2): 551-568.
[15]	MAO Shulan, WU Qian, WANG Zhuoya, LU Yingying. Research progress on high-voltage electrolytes for ternary NCM lithium-ion batteries [J]. Energy Storage Science and Technology, 2020, 9(2): 538-550.