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.

翻译：容量衰减是当前电池应用中最为棘手的问题之一。众所周知，电池系统的降解机制极为复杂，全面理解这一过程并准确预测其演变是一项巨大挑战。为此，本文采用机器学习技术预测电池在整个循环过程中比容量的变化，以把握这一复杂过程。与以往研究不同，本文提出的WOA-ELM模型（R² = 0.9999871）确定了影响电池比容量的关键因素，并通过可解释模型克服了机器学习黑箱的缺陷。该研究全面阐释了这些因素与电池循环过程中电极材料结构损伤及电池失效之间的关联，揭示了它们对电池性能的本质影响，这有助于推进当代电池的优化研究与改进。