[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118252-en":3,"doc-seo-118252-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118252,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","BatteryML - An Open-Source Platform for Machine Learning on Battery Degradation","Battery degradation is a central concern in energy storage, and machine learning offers a powerful route to accelerate understanding and improve predictive performance. The combination of electrochemical science and machine learning introduces multiple practical barriers: ML specialists struggle with battery-specific complexity, while battery researchers need help adapting models to diverse datasets. A unified standard for degradation modeling—covering data formats and evaluation benchmarks—has been missing. BatteryML addresses this gap with an integrated open-source pipeline for preprocessing, feature extraction, and implementation of baseline and state-of-the-art models to support efficient, collaborative research.","arXiv :2310 . 147 14v 5 [ cs .LG] 3 Apr 2024  \nBATTERYML: AN OPEN-SOURCE PLATFORM FOR MACHINE LEARNING ON BATTERY DEGRADATION  \nHan Zhang 1∗, Xiaofan Gui2 , Shun Zheng2 , Ziheng Lu2 , Yuqi Li3 Jiang Bian2  \n1Institute for Interdisciplinary Information Sciences, Tsinghua University  \n2Microsoft Research  \n3Department of Materials Science and Engineering, Stanford University [han-zhan17@mails.tsinghua.edu.cn](han-zhan17@mails.tsinghua.edu.cn),{xiaofangui,shun.zheng,zihenglu,[jiang.bian](jiang.bian}@microsoft.com)[}](jiang.bian}@microsoft.com)[@microsoft.com](jiang.bian}@microsoft.com), [yuqili@stanford.edu](yuqili@stanford.edu)  \nABSTRACT  \nBattery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions.  \nHowever, 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 1 -a onestep, all-encompass, and open-source platform that integrates data preprocessing, feature extraction, and the implementation of both conventional and state-of-theart models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering a collaborative platform where experts from diverse specializations can contribute, thereby accelerating collective progress in battery research.  \n1 INTRODUCTION  \nLithium-ion batteries, characterized by their high energy density and prolonged cycle life, have revolutionized energy storage across sectors like electric vehicles, consumer electronics, and renewable energy solutions. However, the ubiquitous adoption of these batteries comes with inherent challenges surrounding their capacity degradation and performance stability (Edge et al., 2021) . Continuous cycling tends to diminish their charging and discharging capacities, posing dire implications for real-world applications. For instance,“range anxiety” becomes prevalent among electric vehicle owners, and reliability concerns arise for energy storage systems. Beyond the user experience, rapid degradation introduces broader issues, such as escalated maintenance costs, heightened resource usage, environmental strain, and potential economic inefficiencies. As such, decoding and forecasting battery performance degradation has ascended as a pivotal topic in industrial artificial intelligence.  \nPeeling back the layers of lithium-ion batteries reveals their intricate, non-linear electrochemical dynamics (Hu et al., 2020) . Degradation, observed as diminishing performance with increased chargedischarge iterations, branches mainly into losses in lithium-ion inventory (e.g., solid electrolyte interphase film formation and electrolyte decomposition) and active material losses (e.g., graphite delamination and binder decomposition) (Pop et al., 2007; Dubarry et al., 2012; Sarasketa-Zabala et al., 2015) . Moreover, the internal resistance and excessive electrolyte losses further contribute to the battery’s declining health. Such losses in electrolytes, in particular, can precipitate a stark capacity plunge towards a battery’s lifecycle end (Edge et al., 2021) .  \nConfronting this degradation complexity, reliably predicting a battery’s remaining useful life (RUL), state of health (SOH), and state of charge (SOC) becomes a herculean endeavor (Lipu et al., 2018) .  \n∗Han Zhang and Yuqi Li worked on this project during their internship at Microsoft Research. 1Project repository: [https://github.com/microsoft/BatteryML](https://github.com/microsoft/BatteryML)  \nThe significance of RUL, especially in","cbCain1gSDCkXvOg","https://ap.wps.com/l/cbCain1gSDCkXvOg","pdf",1638099,1,29,"English","en",105,"# Introduction\n## Battery degradation in lithium-ion systems\n## Target prediction tasks (RUL, SOH, SOC)\n## Motivation: lack of standardized modeling pipelines\n## Key challenges in applying ML to battery research","[{\"question\":\"What problem does BatteryML aim to solve in battery research?\",\"answer\":\"BatteryML targets the lack of a unified, standardized platform for battery degradation modeling, including data preprocessing, feature extraction, and model implementation, along with consistent evaluation support.\"},{\"question\":\"Why is predicting battery performance degradation challenging?\",\"answer\":\"Battery degradation follows complex, non-linear electrochemical dynamics and involves multiple loss mechanisms, making reliable estimation of RUL, SOH, and SOC difficult across varied datasets and operating conditions.\"},{\"question\":\"How does BatteryML improve practicality for machine learning on battery degradation?\",\"answer\":\"It provides an open-source, end-to-end workflow that streamlines data preprocessing and feature extraction and includes both conventional and state-of-the-art models, helping researchers apply ML more efficiently and consistently.\"}]","BatteryML - An Open-Source Platform for Machine Learning on Battery Degradation | PDF",1785682651,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"batteryml-an-open-source-platform-for-machine-learning-on-battery-degradation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/batteryml-an-open-source-platform-for-machine-learning-on-battery-degradation/118252/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does BatteryML aim to solve in battery research?","Question",{"text":75,"@type":76},"BatteryML targets the lack of a unified, standardized platform for battery degradation modeling, including data preprocessing, feature extraction, and model implementation, along with consistent evaluation support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is predicting battery performance degradation challenging?",{"text":80,"@type":76},"Battery degradation follows complex, non-linear electrochemical dynamics and involves multiple loss mechanisms, making reliable estimation of RUL, SOH, and SOC difficult across varied datasets and operating conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does BatteryML improve practicality for machine learning on battery degradation?",{"text":84,"@type":76},"It provides an open-source, end-to-end workflow that streamlines data preprocessing and feature extraction and includes both conventional and state-of-the-art models, helping researchers apply ML more efficiently and consistently.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]