A novel method of battery pack energy health estimation based on visual feature learning.

Accurate health estimation of massive battery packs and efficient optimization of data storage have become major technical challenges with the development of big data platforms. In this paper, multi-level energy indicators are defined to reflect the overall health state of the battery pack, and battery pack health assessment is achieved through energy estimation. A visual feature learning method is proposed to extract features from partial cell charging voltage profiles image and the relationship between features and energy indicators is constructed by the hybrid convolutional neural network. The effectiveness of the proposed method is verified on the dataset generated by the battery pack model considering cell inconsistency, and the mean absolute percentage error of each energy indicator estimation is less than 1%. Additionally, validations are carried out on simulated data with sampling noise and two cases of experimental data to verify the stability of the method. The proposed visual feature learning method provides a new idea for the data storage and health monitoring of massive battery packs on the big data platform. • Multi-level energy indicators are proposed to reflect the battery pack health. • Battery pack dataset generated based on orthogonal combination of inconsistency parameters. • Extract visual features from partial cell charging voltage profiles image. • Good anti-interference ability against sampling noise. [ABSTRACT FROM AUTHOR]