# Statistical learning for accurate and interpretable battery lifetime prediction

arXiv: https://arxiv.org/abs/2101.01885 · HTML: https://mlsanity.com/paper/2101.01885/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 6 Jan 2021.

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

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Battery health prediction under generalized conditions using a Gaussian process transition model](https://mlsanity.com/paper/1807.06350/) (2018)
- [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, 9 of 9)

- [BatteryML:An Open-source platform for Machine Learning on Battery Degradation](https://mlsanity.com/paper/2310.14714/) (2023)
- [DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis](https://mlsanity.com/paper/2410.23893/) (2024)
- [Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data](https://mlsanity.com/paper/2309.00564/) (2023)
- [Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis](https://mlsanity.com/paper/2110.13661/) (2021)
- [Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training](https://mlsanity.com/paper/2402.00068/) (2024)
- [Accurate battery lifetime prediction across diverse aging conditions with deep learning](https://mlsanity.com/paper/2310.05052/) (2023)
- [Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging](https://mlsanity.com/paper/2308.13996/) (2023)
- [Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers](https://mlsanity.com/paper/2410.05326/) (2024)
- [Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols](https://mlsanity.com/paper/2504.03701/) (2025)

## Abstract (arXiv)

Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the "capacity matrix" concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset. These models also provide insights into the degradation of these cells. Our approaches can be used both to quickly train models for a new dataset and to benchmark the performance of more advanced machine learning methods.