# Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network

arXiv: https://arxiv.org/abs/2404.14444 · HTML: https://mlsanity.com/paper/2404.14444/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 20 Apr 2024.

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

- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches](https://mlsanity.com/paper/1803.04386/) (2018)
- [BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling](https://mlsanity.com/paper/2404.05802/) (2024)

## Built on by (most cited first, 3 of 3)

- [GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction](https://mlsanity.com/paper/2501.04997/) (2025)
- [Knowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics](https://mlsanity.com/paper/2510.02839/) (2025)
- [Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation](https://mlsanity.com/paper/2501.16377/) (2025)

## Abstract (arXiv)

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 health and apply distributions, rather than single-point, for each parameter of the models. This allows the models to capture the inherent randomness and uncertainty of battery health, which leads to not only accurate predictions but also quantifiable uncertainty. We conducted an experimental study and demonstrated the effectiveness of our proposed models, with a prediction error rate averaging 13.9%, and as low as 2.9% for certain tested batteries. Additionally, all predictions include quantifiable certainty, which improved by 66% from the initial to the mid-life stage of the battery. This research has practical values for battery technologies and contributes to accelerating the technology adoption in the industry.