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核心问题，机器学习正成为推动相关认知与解决方案发展的有力工具。然而，电化学科学与机器学习的交叉领域带来了复杂挑战：机器学习专家往往难以深入理解电池科学的细微之处，而电池研究人员则在为特定数据集调整复杂模型时面临重重困难。此外，在电池退化建模领域，包括数据格式与评估基准在内的统一标准明显缺失。针对这些障碍，我们提出了BatteryML——一款一站式、全流程、开源的平台，旨在整合数据预处理、特征提取以及传统与最先进模型的实现。这种简化方法有望提升研究应用的实用性与效率。BatteryML致力于弥补这一空白，营造一个让不同领域的专家能够协同贡献的环境，从而加深对电池研究的整体理解并推动其发展。我们的项目代码已在GitHub上公开，访问地址为https://github.com/microsoft/BatteryML。