BatteryML:An Open-source platform for Machine Learning on Battery Degradation
This work addresses the problem of fragmented standards and collaboration barriers in battery degradation modeling for researchers in energy storage and machine learning, though it is incremental as it builds on existing methods.
The paper tackles the challenge of applying machine learning to battery degradation by introducing BatteryML, an open-source platform that unifies data preprocessing, feature extraction, and model implementation, resulting in enhanced practicality and efficiency for research applications.
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.