# BatteryML:An Open-source platform for Machine Learning on Battery Degradation

arXiv: https://arxiv.org/abs/2310.14714 · HTML: https://mlsanity.com/paper/2310.14714/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 103 references that are in the map.
- First posted on arXiv 23 Oct 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Statistical learning for accurate and interpretable battery lifetime prediction](https://mlsanity.com/paper/2101.01885/) (2021)

## Built on by (most cited first, 9 of 9)

- [DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis](https://mlsanity.com/paper/2410.23893/) (2024)
- [BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction](https://mlsanity.com/paper/2502.18807/) (2025)
- [Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training](https://mlsanity.com/paper/2402.00068/) (2024)
- [BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting](https://mlsanity.com/paper/2605.27044/) (2026)
- [Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation](https://mlsanity.com/paper/2605.29560/) (2026)
- [BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking](https://mlsanity.com/paper/2607.09762/) (2026)
- [Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity](https://mlsanity.com/paper/2608.16612/) (2026)
- [Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap](https://mlsanity.com/paper/2608.26111/) (2026)
- [PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics](https://mlsanity.com/paper/2608.27978/) (2026)

## Abstract (arXiv)

Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degradation modeling, inclusive of data formats and evaluative benchmarks, is conspicuously absent. Recognizing these impediments, we present BatteryML - a one-step, all-encompass, and open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering an environment where experts from diverse specializations can collaboratively contribute, thus elevating the collective understanding and advancement of battery research.The code for our project is publicly available on GitHub at https://github.com/microsoft/BatteryML.