Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation

Key Points

• Prediction models improve battery quality assessments through advanced data techniques, enhancing performance.
• A substantial increase in classification accuracy was observed when using data augmentation approaches.
• Analysis was performed using sequence-sampling methods to enhance training datasets for prediction models.
• These findings highlight the potential for improved battery quality classification, calling for further study on augmentation techniques.