关键词: 锂离子电池, 短期充电数据, 容量预测, 增强鲸鱼优化算法, 高斯过程回归

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