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.

翻译：电池退化仍是储能领域的关键问题，机器学习作为推动相关洞见与解决方案的有力工具正在崭露头角。然而，电化学科学与机器学习的交叉带来了复杂挑战：机器学习专家常受困于电池科学的精微之处，而电池研究人员在针对特定数据集适配复杂模型时亦面临障碍。此外，涵盖数据格式与评估基准的电池退化建模统一标准明显缺失。针对这些困境，我们提出BatteryML——一个一站式、全流程、开源的平台，旨在统一数据预处理、特征提取以及传统与前沿模型的实现。这一精简方法有望提升研究应用的实用性与效率。BatteryML致力于填补这一空白，营造不同领域专家协同贡献的环境，从而提升对电池研究的共同理解与进展。我们的项目代码已在GitHub上公开，网址为https://github.com/microsoft/BatteryML。