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.

应用机器学习技术，研究了电池容量削减的机制，通过 WOA-ELM 模型，确定了影响电池特定容量的关键因素，并解释了它们与电极材料的结构损伤和电池循环期间的故障之间的联系。