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

arXiv: https://arxiv.org/abs/2308.15833 · HTML: https://mlsanity.com/paper/2308.15833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness](https://mlsanity.com/paper/1905.13472/) (2019)
- [Machine learning pipeline for battery state of health estimation](https://mlsanity.com/paper/2102.00837/) (2021)

## Abstract (arXiv)

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.