Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network

arXiv:2404.14444v1 Announce Type: new
Abstract: Battery health monitoring and prediction are critically important in the era of electric mobility with a huge impact on safety, sustainability, and economic aspects. Existing research often focuses on prediction accuracy but tends to neglect practical factors that may hinder the technology's deployment in real-world applications. In this paper, we address these practical considerations and develop models based on the Bayesian neural network for predicting battery end-of-life. Our models use sensor data related to battery …

abstract accuracy applications arxiv battery bayesian cs.ai cs.et cs.lg deployment economic electric health hinder impact mobility monitoring network neural network practical prediction research safety sustainability technology type uncertainty world