Low temperature charging aging modeling and optimization of charging strategy for lithium batteries

doi:10.19799/j.cnki.2095-4239.2020.0078

Abstract

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

CLC Number:

TM 912

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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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%