[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85700-en":3,"doc-seo-85700-105":28,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},85700,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","BatteryLake Agentic Physics Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking","Public battery aging datasets are vital for battery health management, yet real-world use is constrained by inconsistent file formats, unclear data schemas, and fragmented metadata across repositories and publications. Existing curation is mostly manual and dataset specific, and general integration tools cannot reliably capture electrochemical time-series semantics. BatteryLake introduces a governed data lakehouse with agentic, evidence-grounded automated curation, including abstention, human verification, and physics-validated benchmarks and tasks.","arXiv :2607 .09762v 1 [ cs .AI] 6 Jul 2026  \nBatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data  \nand Benchmarking  \nTianwen Zhu, Hao Wang, and Yonggang Wen, Fellow, IEEE  \nAbstract—Public battery aging datasets are a critical asset for advanced health management. However, their practical use is often limited by inconsistent file formats, unclear data schemas, and metadata that are dispersed across repositories an publications.  \nCurrent curation practices remain largely manual and dataset-specific, making them difficult to reproduce. Meanwhile, general-purpose data integration tools often fail to capture the domain-specific semantics of electrochemical time-series data. In this paper, we presents BatteryLake, a governed data lakehouse that turns raw public battery data into benchmark-ready assets through an agentic, evidence-grounded curation framework. We make three main contributions to the automated curation of public battery datasets. The first capability combines evidence-grounded metadata extraction with explicit abstention, and schema mapping with program synthesis for dataset-specific converters. Both tasks are handled by large language model (LLM) agents. Each agent output must be grounded in verbatim source evidence. If such evidence is unavailable, the agent abstains rather than producing an unsupported result. Second, we design a human-in-the-loop verification mechanism that frames field extraction as a selective prediction problem, deferring  \nlow-confidence extractions to human review. Low-confidence fields are routed to domain experts, while high-confidence fields are accepted only when the residual error rate is bounded below a prescribed threshold. The admitted data must then pass a 26-rule validation gate covering schema consistency, statistical validity, and physical plausibility. Third, on top of the curated data lake we release an open benchmark spanning 41 curated datasets from over 25 institutions, covering various chemistries across cylindrical, pouch, and prismatic formats and spanning cycle-aging, drive-cycle, impedance, calendar-aging, and thermal-abuse regimes, with standardized state of health (SOH) and remaining useful life (RUL) tasks, three split protocols, and eight baseline model families. The platform, benchmark, and curation protocol are publicly available at [https://tianwen1209.github.io/batterylake/](https://tianwen1209.github.io/batterylake/) .  \nIndex Terms—Data curation, data lakes, large language models, human-in-the-loop, benchmarks, battery health management.  \n~~ ~~ ✦ ~~ ~~  \n1 INTRODUCTION  \nLITHIUM-ION batteries are now widely deployed in elec  \ntric vehicles, grid-scale storage, and portable electronics, and in all of these settings their capacity and safety margins decline as the cells age. How quickly a cell loses capacity, and how close it is to end of life, determines when it must be retired, how much range or backup it can still deliver, and whether it can be reused or must be replaced. These quantities are hard to obtain from first principles, because degradation couples chemistry, temperature, load, and usage history, and no compact physical model captures all of these effects across cell types and operating conditions. As a result, degradation behavior is increasingly learned from data, using machine learning over large-scale cycling experiments [1],[2] . Over the past two decades, laboratories worldwide have released dozens of aging datasets, from the NASA Prognostics Center of Excellence [3] and CALCE [4] to the 124-cell fast-charging study of Severson et al. [1], which together contain millions of charge–discharge cycles. In principle, this is ample data for state of health (SOH) estimation, remaining useful life (RUL) prediction, and crosscondition generalization research. In practice, however, its  \n• T. Zhu, H. Wang, and Y. Wen are with the College of Computing and Data Science, Nanyang Technological University, Singapore.  \nE-mail","cbCaiit3pfAenhjy","https://ap.wps.com/l/cbCaiit3pfAenhjy","pdf",4042607,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does BatteryLake address in public battery aging datasets?\",\"answer\":\"It addresses limited practical usability caused by inconsistent file formats, unclear schemas, and metadata dispersed across repositories and papers, which makes results hard to reproduce.\"},{\"question\":\"How does BatteryLake ensure extracted metadata is reliable?\",\"answer\":\"It uses LLM agents that ground outputs in verbatim source evidence and explicitly abstain when evidence is unavailable. It further applies a human-in-the-loop selective prediction workflow for low-confidence fields.\"},{\"question\":\"What does the BatteryLake benchmark provide to researchers?\",\"answer\":\"It releases an open benchmark with 41 curated datasets from 25+ institutions, standardizing state of health (SOH) and remaining useful life (RUL) tasks across multiple chemistries, aging regimes, split protocols, and baseline model families.\"}]",1784205682,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":26},"batterylake-agentic-physics-grounded-curation-of-heterogeneous-battery-aging-data-and-benchmarking","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/batterylake-agentic-physics-grounded-curation-of-heterogeneous-battery-aging-data-and-benchmarking/85700/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does BatteryLake address in public battery aging datasets?","Question",{"text":74,"@type":75},"It addresses limited practical usability caused by inconsistent file formats, unclear schemas, and metadata dispersed across repositories and papers, which makes results hard to reproduce.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does BatteryLake ensure extracted metadata is reliable?",{"text":79,"@type":75},"It uses LLM agents that ground outputs in verbatim source evidence and explicitly abstain when evidence is unavailable. It further applies a human-in-the-loop selective prediction workflow for low-confidence fields.",{"name":81,"@type":72,"acceptedAnswer":82},"What does the BatteryLake benchmark provide to researchers?",{"text":83,"@type":75},"It releases an open benchmark with 41 curated datasets from 25+ institutions, standardizing state of health (SOH) and remaining useful life (RUL) tasks across multiple chemistries, aging regimes, split protocols, and baseline model families.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":44,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":45,"doc_module":4,"doc_module_name":44,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":44,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":44,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":44,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":44,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":44,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":44,"category_name":124,"show_sort_weight":27,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":27,"doc_module":4,"doc_module_name":44,"category_name":127,"show_sort_weight":27,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":44,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":44,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]