# Bayesian hierarchical modelling for battery lifetime early prediction

arXiv: https://arxiv.org/abs/2211.05697 · HTML: https://mlsanity.com/paper/2211.05697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 10 Nov 2022.

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

- [Hamiltonian Monte Carlo for Hierarchical Models](https://mlsanity.com/paper/1312.0906/) (2013)
- [Faster Algorithms for the Constrained k-means Problem](https://mlsanity.com/paper/1504.02564/) (2015)
- [Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics](https://mlsanity.com/paper/2010.07460/) (2020)

## Built on by (most cited first, 1 of 1)

- [Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data](https://mlsanity.com/paper/2307.08382/) (2023)

## Abstract (arXiv)

Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this -- along with the limited experimental resources usually available for each cycling condition -- makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.