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：电池退化仍是能源存储领域的核心问题，机器学习正成为推动其洞察与解决方案的重要工具。然而，电化学科学与机器学习的交叉领域带来了复杂挑战。机器学习专家常受困于电池科学的深奥机理，而电池研究人员则难以将复杂模型适配到特定数据集。此外，目前缺乏统一的电池退化建模标准，包括数据格式和评估基准。针对这些障碍，我们提出BatteryML——一个一站式、全面且开源的平台，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。这种简化方法有望提升研究应用的实用性与效率。BatteryML旨在填补这一空白，营造一个让不同领域专家协同贡献的环境，从而加深对电池研究的集体认知并推动其进展。项目代码已在GitHub上公开，地址为https://github.com/microsoft/BatteryML。