A coarse-to-fine ensemble method for capacity prediction of lithium-ion batteries in production

ABSTRACT

The grading process is a critical stage in the production of lithium-ion batteries. Measuring capacity by full discharge is time-consuming and energy-intensive. Therefore, accurate and efficient capacity prediction is essential. However, current methods still require improvements in data selection, prediction accuracy and handling of dirty data. This paper proposes a coarse-to-fine ensemble learning framework using LightGBM regression algorithm to predict battery capacity. The framework uses raw statistical data directly, instead of complicated features extraction. From different perspectives of the whole data and classified data, the capacity is predicted from coarse to fine, and the semi-dirty data is predicted separately. The method is verified using lithium-ion battery datasets collected from actual industrial production lines. The experimental results show that the mean absolute error and mean absolute percentage error on the test set are 0.31 Ah and 0.11%, respectively, outperforming other regression prediction algorithms. Additionally, semi-dirty data, which accounts for 0.26% of the dataset, is predicted effectively.

KEYWORDS:

• Lithium-ion batteries
• grading process
• capacity prediction
• ensemble learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the National Natural Science Foundation of China (no. 62072024 and 41971396), the Fundamental Research Funds for Municipal Universities of Beijing University of Civil Engineering and Architecture (no. X20084 and ZF17061).