# Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data

arXiv: https://arxiv.org/abs/2307.08382 · HTML: https://mlsanity.com/paper/2307.08382/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Insights into Performance Fitness and Error Metrics for Machine Learning](https://mlsanity.com/paper/2006.00887/) (2020)
- [Faster Algorithms for the Constrained k-means Problem](https://mlsanity.com/paper/1504.02564/) (2015)
- [Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning](https://mlsanity.com/paper/2001.09938/) (2019)
- [Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life](https://mlsanity.com/paper/2101.04440/) (2021)
- [Predicting the impact of formation protocols on battery lifetime immediately after manufacturing](https://mlsanity.com/paper/2203.14158/) (2022)
- [Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation](https://mlsanity.com/paper/1911.01399/) (2019)
- [Bayesian hierarchical modelling for battery lifetime early prediction](https://mlsanity.com/paper/2211.05697/) (2022)

## Built on by (most cited first, 7 of 7)

- [BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction](https://mlsanity.com/paper/2502.18807/) (2025)
- [Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction](https://mlsanity.com/paper/2504.18230/) (2025)
- [Pretrained battery transformer (PBT): A foundation model for battery life prediction](https://mlsanity.com/paper/2512.16334/) (2025)
- [BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting](https://mlsanity.com/paper/2605.27044/) (2026)
- [Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting](https://mlsanity.com/paper/2606.23391/) (2026)
- [Early Cycle Charge Trajectory Generative Prediction and Full Life Cycle Health Management of Iron-Chromium Flow Batteries Based on FlowBD-E1](https://mlsanity.com/paper/2608.14637/) (2026)
- [Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap](https://mlsanity.com/paper/2608.26111/) (2026)

## Abstract (arXiv)

Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.