锂离子电池低温充电老化建模及其充电策略优化

doi:10.19799/j.cnki.2095-4239.2020.0078

摘要/Abstract

摘要：

为了促进新能源汽车在寒冷地区的推广，对锂离子电池低温充电老化及其充电控制策略的研究具有重要意义。本工作基于大量低温充电实验数据，建立了多应力低温充电老化模型。以温度为主要影响因素，同时考虑充电截止电压和充电倍率及充电循环次数对电池老化的影响。引入衰退加速度因子，将多个充电应力相结合作用于整体模型，并对模型的估计精度进行了仿真测试。在此基础上引入遗传算法对充电控制策略进行优化，以充电电压为基准，将达到充电截止电压前的充电过程均分为多个充电阶段，将各阶段充电电流作为遗传算法的基因序列，以充电老化速率和充电时间作为优化目标，进行迭代优化。仿真结果表明所建立的低温充电老化模型具有较高的参数估计精度，充电控制策略能够有效较少电池老化并节约充电时间。通过设计的充电控制器对充电策略进行了实验测试，测试结果与仿真结果相同。对电池低温充电进行的实验，摸索了低温充电对电池寿命衰退影响的规律，实验数据、老化模型和充电策略优化方法有较为直接的参考价值。

关键词: 锂离子电池, 老化建模, 遗传算法, 低温充电, 充电策略

Abstract:

The aging of lithium-ion batteries in case of low-temperature charging and a control strategy for low-temperature charging should be investigated to promote the use of new energy vehicles in cold regions. Further, a multi-stress low-temperature charging aging model was established using a large number of low-temperature charging experimental data. By considering temperature as the main influencing factor, the influence of the charging cut-off voltage, rate, and cycle times with respect to battery aging was considered. A decay acceleration factor is introduced, and several charging stresses are combined to measure their effects on the model. By introducing the genetic algorithm to optimize the charging control strategy based on the charging voltage, charging to the cut-off voltage is divided into several stages. Each stage’s charging current becomes the genetic sequence of a genetic algorithm. The charge rate of aging and charging time are considered to be the optimization objectives, creating an iterative optimization procedure. The simulation results show that the low-temperature charging aging model exhibits high parameter estimation accuracy and that the charging control strategy can effectively reduce battery aging and the charging time. The charging strategy is verified using the designed charging controller, and the test results are identical to the simulation results. These experiments explore the law of influence of low-temperature charging on the battery-life decline, and the data, the aging model, and the charging strategy optimization method offer direct reference value.

Key words: lithium ion batteries, aging modeling, genetic algorithm, low temperature charge, charging strategy

中图分类号:

TM 912

王泰华, 张书杰, 陈金干. 锂离子电池低温充电老化建模及其充电策略优化[J]. 储能科学与技术, 2020, 9(4): 1137-1146.

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.

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参考文献 16

1	GAO Y , JIANG J C , ZHANG C P , et al . Lithium-ion battery aging mechanisms and life model under different charging stresses[J]. Journal of Power Sources, 2017, 356: 103-114.
2	王嗣慧, 徐中领, 杜锐, 等 . 高镍三元锂离子电池高温存储性能衰退机理[J]. 储能科学与技术, 2017, 6(4): 770-775.
	WANG Sihui , XU Zhongling , DU Rui , et al . Degradation study of Ni-rich NCM batteries operated at high tempertures[J]. Energy Storage Science and Technology, 2017, 6(4): 770-775.
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中国汽车工程学会论文集, 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	朱建功, 孙泽昌, 魏学哲, 等 . 车用锂离子电池低温特性与加热方法研究进展[J]. 汽车工程, 2019, 41(5): 571-581, 589.
	ZHU Jiangong , SUN Zechang , WEI Xuezhe , et al . Research progress on low-temperature characteristics and heating techniques of vehicle lithium-ion battery[J]. Automotive Engineering, 2019, 41(5): 571-581, 589.
5	GITHIN K , PRASAD C D , RAH N . Model based identification of aging parameters in lithium ion batteries[J]. Journal of Power Sources, 2013, 232: 79-85.
6	张明轩, 冯旭宁, 欧阳明高, 等 . 三元锂离子动力电池针刺热失控实验与建模[J]. 汽车工程, 2015, 37(7): 743-750, 756.
	ZHANG Mingxuan , FENG Xuning , OUYANG Minggao , et al . Experiments and modeling of nail penetration thermal runaway in a NCM Li-ion power battery[J]. Automotive Engineering, 2015, 37(7): 743-750, 756.
7	宋凯, 陈旭, 成艾国 . 考虑温度影响和滞回效应的锂电池特性建模[J]. 汽车工程, 2019, 41(3): 334-339.
	SONG Kai , CHEN Xu , CHENG Aiguo . Modeling of lithium battery characteristics considering the influence of temperature and hysteresis effect[J]. Automotive Engineering, 2019, 41(3): 334-339.
8	姚雷, 侯俊剑, 翟洪飞, 等 . 不同温度下锂离子动力电池特性研究[J]. 电源技术, 2019, 43(9): 1445-1447, 1452.
	YAO Lei , HOU Junjian , ZHAO Hongfei , et al . Study on the characteristic of lithium-ion battery under different ambient temperature[J]. Chinese Journal of Power Sources, 2019, 43(9): 1445-1447, 1452.
9	JOHANNES S , STEFAN K , MADELEINE E , et al . A holistic aging model for Li(NiMnCo)O₂ based 18650 lithium-ion batteries[J]. Journal of Power Sources, 2014, 257: doi：10.1016/j.jpowsour.2013.09.143.
10	LIN X K , LIU Z Y , JIA W Q , et al . Health conscious fast charging of Li-ion batteries via a single particle model with aging mechanisms[J]. Journal of Power Sources, 2018, 340: 305-316.
11	JOCHEN B , KAI P B, RAMANATHAN S , et al . Quantitative validation of calendar aging models for lithium-ion batteries[J]. Journal of Power Sources, 2018, 340: 402-414.
12	FICHTNER M , YANG Y , LI D J , et al . Modeling the degradation mechanisms of C-6/LiFePO₄ batteries[J]. Journal of Power Sources， 2018, 375: 106-117.
13	文锋, 姜久春, 张维戈, 等 . 电动汽车用锂离子电池组充电方法[J]. 汽车工程, 2008, 30(9): 792-795.
	WEN Feng , JIANG Jiuchun , ZHANG Weige , et al . Charging method for Li-ion battery pack in electric vehicles[J]. Automotive Engineering, 2008, 30(9): 792-795.
14	LI L L , MU H , LI Z R , et al . An electrochemical model based degradation state identification method of lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy, 2018, 219: 264-275.
15	姚雷, 王震坡 . 锂离子动力电池充电方式的研究[J]. 汽车工程, 2015, 37(1): 72-77.
	YAO Lei , WANG Zhenpo . A Research on the charging protocols of lithium-ion traction battery[J]. Automotive Engineering, 2015, 37(1): 72-77.
16	李夔宁, 张宏济, 谢翌, 等 . 基于电-热-老化耦合模型的自适应多段恒流充电研究[J]. 东北大学学报（自然科学版）, 2019, 40(9): 1323-1329.
	LI Kuining , ZHANG Hongji , XIE Yi , et al . Study on self-adaptive multistage constant current charging based on electro-thermal-aging coupling model[J]. Journal of Northeastern University(Natural Science), 2019, 40(9): 1323-1329.

属性	参数
额定容量/mA·h	2900
标称电压/V	3.62
最大充电截止电压/V	4.2±0.5
最大充电电流/mA	2850
放电截止电压/V	2.5

测试工况	K估计值	K实际值	误差
0 ℃-4.05 V-0.5 C-20cyc	0.099	0.108	7.59%
0 ℃-4.1 V-0.8 C-40cyc	0.244	0.226	8.23%
0 ℃-4.2 V-0.1 C-60cyc	0.527	0.55	4.04%
-5 ℃-4.1 V-0.5 C-15cyc	0.326	0.311	5.11%
-5 ℃-4.2 V-0.8 C-30cyc	1.088	1.169	6.92%
-10 ℃-4.1 V-0.5 C-10cyc	0.678	0.626	8.39%
-10 ℃-4.2 V-0.8 C-15cyc	1.970	2.124	7.24%
-15 ℃-4.1 V-0.5 C-5cyc	0.938	0.95	1.25%
-15 ℃-4.2 V-0.8 C-10cyc	3.410	3.469	1.69%
-20 ℃-4.2 V-0.8 C-5cyc	4.069	4.25	4.25%

测试工况	CC-CV	优化后	衰退减少量
-5 ℃-30cyc-4.2 V	0.8344	0.5964	28.52%
-10 ℃-20cyc-4.1 V	1.9604	0.8581	56.22%
-15 ℃-8cyc-4.2 V	3.0897	1.1059	64.20%
-20 ℃-6cyc-4.2 V	3.6069	1.5804	56.18%

测试工况	CC-CV/h	优化后/h	时间减小量
-5 ℃-30cyc-4.2 V	2.78	2.67	3.96%
-10 ℃-20cyc-4.1 V	3.11	2.41	22.51%
-15 ℃-8cyc-4.2 V	3.58	3.45	3.63%
-20 ℃-6cyc-4.2 V	3.95	3.62	8.35%

-15 ℃	容量衰退	相比CC-CV减少	时间/h	相比CC-CV减少
CC-CV	3.09%	—	3.42	—
遗传优化	1.62%	47.57%	2.85	16.67%