# Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation

arXiv: https://arxiv.org/abs/2410.07458 · HTML: https://mlsanity.com/paper/2410.07458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 114 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (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)
- [Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra](https://mlsanity.com/paper/2302.03362/) (2023)
- [Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics](https://mlsanity.com/paper/2010.07460/) (2020)
- [Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms](https://mlsanity.com/paper/2205.08132/) (2022)
- [Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data](https://mlsanity.com/paper/2309.00564/) (2023)
- [Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More](https://mlsanity.com/paper/2404.04049/) (2024)
- [Differential voltage analysis for battery manufacturing process control](https://mlsanity.com/paper/2303.07088/) (2023)
- [Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data](https://mlsanity.com/paper/2406.19015/) (2024)
- [EVScout2.0: Electric Vehicle Profiling Through Charging Profile](https://mlsanity.com/paper/2106.16016/) (2021)
- [LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm](https://mlsanity.com/paper/2402.17120/) (2024)
- [A dataset of over one thousand computed tomography scans of battery cells](https://mlsanity.com/paper/2403.02527/) (2024)

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

- [Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features](https://mlsanity.com/paper/2411.12060/) (2024)
- [Pretrained battery transformer (PBT): A foundation model for battery life prediction](https://mlsanity.com/paper/2512.16334/) (2025)
- [TimeSeries2Report prompting enables adaptive large language model management of lithium-ion batteries](https://mlsanity.com/paper/2512.16453/) (2025)

## Abstract (arXiv)

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.