Lithium battery capacity prediction based on short-term charging data and an enhanced whale optimization algorithm

doi:10.19799/j.cnki.2095-4239.2024.0686

Abstract

Abstract:

The prediction of lithium-ion battery capacity using data-driven methods involves many challenges, such as the difficulty in obtaining complete charging data, low data sampling precision, and poor quality of feature extraction. To overcome these challenges, this study proposes a lithium-ion battery capacity prediction method based on short-term charging data and an enhanced whale optimization algorithm. First, to improve data precision, the charging data were supplemented by cubic spline interpolation. Next, by exploring the relationship between the charging voltage curve and capacity degradation, the voltage increment over a specific charging time interval was identified as the feature factor. An enhanced whale optimization algorithm was then utilized to extract aging features effectively from the short-term charging data. Subsequently, a Gaussian process regression model was constructed for capacity prediction. After determining the amount of training data, the predictive results of different algorithms were compared to verify the effectiveness of the proposed model. Finally, the method was tested on different batteries to validate its prediction accuracy and generalization capability. The results demonstrate that for the laboratory dataset, by using the first 15% of aging features as the training set, the maximum error of this type of battery can be controlled within 2.49%, with 97% of the prediction errors being within 1.5%. For a publicly available dataset, by using only 12 groups of training data, the maximum error of this type of battery can be controlled within 1%, achieving accurate capacity prediction using low-precision and short-term charging data.

Key words: lithium-ion battery, short-term charging data, capacity prediction, enhanced whale optimization algorithm, Gaussian process regression

CLC Number:

TM 912

Zheng CHEN, Yue PENG, Jingyuan HU, Jiangwei SHEN, Renxin XIAO, Xuelei XIA. Lithium battery capacity prediction based on short-term charging data and an enhanced whale optimization algorithm[J]. Energy Storage Science and Technology, 2025, 14(1): 319-330.

Figures/Tables 16

Table 1

Fig.1

Fig.2

Fig.3

Table 2

Table 3

Table 4

Fig.4

Fig.5

Table 5

Evaluation metrics for prediction results with different training set lengths"

训练集长度	$R 2$	MAE/%	AAE/%	RMSE/%	Max/%
10%	0.973	2.479	0.7897	3.007	3.14
15%	0.991	1.281	0.4146	1.655	2.38
20%	0.989	1.291	0.4171	1.651	2.34
25%	0.986	1.312	0.4307	1.706	2.28

Table 5

Fig.6

Table 6

Evaluation metrics for prediction results of different models"

模型	$R 2$	MAE/%	AAE/%	RMSE/%	Max/%
GPR	0.991	1.281	0.415	1.655	2.38
SVR	0.987	1.537	0.499	1.917	2.52
RBF	0.964	2.185	0.684	3.255	-3.77
LSTM	0.969	2.080	0.660	2.995	-4.05
GRU	0.968	2.114	0.670	3.042	-4.11

Table 6

Fig.7

Table 7

Evaluation metrics for prediction results of cells 2 and 3"

电池	$R 2$	MAE/%	AAE/%	RMSE/%	Max/%
电池2	0.993	1.342	0.4169	1.766	2.49
电池3	0.992	1.513	0.4624	1.904	2.21

Table 7

Fig.8

Table 8

Evaluation metrics for prediction results of cells 4 to 7"

电池	$R 2$	MAE/%	AAE/%	RMSE/%	Max/%
电池4	0.999	0.126	0.216	0.141	-0.46
电池5	0.999	0.145	0.230	0.187	-0.99
电池6	0.999	0.119	0.191	0.144	-0.63
电池7	0.999	0.128	0.209	0.166	-0.89

Table 8

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单体电池特性	单体电池参数
额定容量/Ah	4
额定电压/V	3.6
上截止电压/V	4.15
下截止电压/V	3
充电倍率/C	0.5
放电倍率/C	1
测试温度	室温

参数名称	设定值
搜索代理数量	100
决策变量数量	2
最大迭代次数	200
执行次数	5
索引上界	[0,1]
索引下界	[1799,1800]

次数	最优区间	适应度/斯皮尔曼相关性系数
第1次执行结果	[1425,1789]	-0.995097
第2次执行结果	[1425,1789]	-0.995097
第3次执行结果	[1423,1793]	-0.995092
第4次执行结果	[1425,1789]	-0.995097
第5次执行结果	[1425,1789]	-0.995097

电池	EWOA求解特征
电池1	-0.995
电池2	-0.994
电池3	-0.995

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