Depth analysis of battery performance based on a data-driven approach

TL;DR

This paper employs a data-driven machine learning approach, specifically the WOA-ELM model, to analyze battery capacity degradation, identify key factors affecting performance, and explain the relationship with electrode damage and failure.

Contribution

It introduces an interpretable machine learning model that accurately predicts capacity loss and links it to structural damage, improving understanding over previous black-box models.

Findings

WOA-ELM model achieves R2 = 0.9999871 in capacity prediction

Key factors affecting capacity are identified and explained

Relationship between electrode damage and battery failure is clarified

Abstract

Capacity attenuation is one of the most intractable issues in the current of application of the cells. The disintegration mechanism is well known to be very complex across the system. It is a great challenge to fully comprehend this process and predict the process accurately. Thus, the machine learning (ML) technology is employed to predict the specific capacity change of the cell throughout the cycle and grasp this intricate procedure. Different from the previous work, according to the WOA-ELM model proposed in this work (R2 = 0.9999871), the key factors affecting the specific capacity of the battery are determined, and the defects in the machine learning black box are overcome by the interpretable model. Their connection with the structural damage of electrode materials and battery failure during battery cycling is comprehensively explained, revealing their essentiality to battery performance, which is conducive to superior research on contemporary batteries and modification.