[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118310-en":3,"doc-seo-118310-105":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},118310,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine-Learning-Based Efficient Parameter Space Exploration for Energy Storage Systems - ECS Meeting Abstracts","Energy storage systems must estimate remaining energy across their lifetime, yet this depends on many interacting operating parameters, creating a large, high-dimensional parameter space. Exhaustive testing on dense grids is prohibitively costly, further amplified by cell-to-cell performance variability. This work develops a Gaussian-process framework using domain knowledge to enable Bayesian optimization, selecting experiments that maximize information gain while reducing uncertainty. Results demonstrate accurate remaining-energy prediction with far fewer experiments. To address laboratory-to-field mismatch, an approach is proposed to predict performance under real-world cycling conditions.","Lawrence Berkeley National Laboratory LBL Publications  \nTitle  \nMachine-Learning-Based Efficient Parameterspace Exploration for Energy Storage Systems  \nPermalink  \n[https://escholarship.org/uc/item/62b4h7vb](https://escholarship.org/uc/item/62b4h7vb)  \nJournal  \nECS Meeting Abstracts, MA2024-02(3)  \nISSN  \n2151-2043  \nAuthors  \nHarris, Stephen J  \nAlghalayini, Maher Noack, Marcus et al.  \nPublication Date  \n2024-11-22  \nDOI  \n10.1149/ma2024-023394mtgabs  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine-Learning-Based Efficient Parameter Space Exploration for Energy Storage Systems  \nMaher B. Alghalayini 1,2*, Daniel Collins-Wildman 1 , Kenneth Higa 1 , Armina Guevara 1 , Vincent Battaglia 1 ,  \nMarcus M. Noack2*, Stephen J. Harris 1  \n1 Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, 94720, California,  \nUSA.  \n2 Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, 94720,  \nCalifornia, USA.  \n*Corresponding author(s). E-mail(s): [MAlghalayini@lbl.gov](MAlghalayini@lbl.gov) ;  \n[MarcusNoack@lbl.gov](MarcusNoack@lbl.gov) ;  \nContributing authors: [dcollinswildman@lbl.gov](dcollinswildman@lbl.gov) ; [khiga@lbl.gov](khiga@lbl.gov) ;  \n[arminamguevara@berkeley.edu](arminamguevara@berkeley.edu) ; [vsbattaglia@lbl.gov](vsbattaglia@lbl.gov) ; [SJHarris@lbl.gov](SJHarris@lbl.gov) ;  \nAbstract  \nShifting towards sustainable energy sources requires developing new storage systems and estimating their remaining energy over their lifetime. The remaining energy of these systems depends on many operating parameters, resulting ina large high-dimensional parameter space to explore. Testing cells exhaustivelyon a dense grid in the parameter space is prohibitively expensive. This is especially true with considerable cell-to-cell variability in performance, even under the same cycling conditions. Here, we develop a framework based on Gaussian processes, equipped with domain knowledge, to implement Bayesian optimization to explore the parameter space efficiently and quantify remaining energy using failure distributions. Bayesian optimization identifies future experiments that maximize information gain and minimize uncertainty. Experimental results show accurate remaining energy predictions with significantly fewer experiments. However, laboratory cycling conditions, including those in literature, may not represent real-world cycling. We propose an approach based on laboratory results to predict remaining energy under real-world cycling conditions.  \n001  \n002  \n003  \n004  \n005  \n006  \n007  \n008  \n009  \n010  \n011  \n012  \n013  \n014  \n015  \n016  \n017  \n018  \n019  \n020  \n021  \n022  \n023  \n024  \n025  \n026  \n027  \n028  \n029  \n030  \n031  \n032  \n033  \n034  \n035  \n036  \n037  \n038  \n039  \n040  \n041  \n042  \n043  \n044  \n045  \n046  \n047  \n048  \n049  \n050  \n051  \n052  \n053  \n054  \n055  \n056  \n057  \n058  \n059  \n060  \n061  \n062  \n063  \n064  \n065  \n066  \n067  \n068  \n069  \n070  \n071  \n072  \n073  \n074  \n075  \n076  \n077  \n078  \n079  \n080  \n081  \n082  \n083  \n084  \n085  \n086  \n087  \n088  \n089  \n090  \n091  \n092  \n093  \n094  \n095  \n096  \n097  \n098  \n099  \n100  \nKeywords: Machine Learning, Bayesian Optimization, Gaussian Process, Stochastic Modeling, Parameter Space, Efficient Exploration, Energy Storage, Battery Failure  \nModeling, Complex Cycling Conditions  \n1 INTRODUCTION  \nIn the fight against global warming, the demand for new energy storage technologies has increased dramatically. Although solar and wind have great potential to fight global warming, these intermittent sources hinder grid integration. Energy storage systems have risen as a popular solution [1] . Within the grid, energy storage systems store excess energy during peak generation periods and release energy when needed during low-energy generation periods. While commercially availa","cbCaipHl8RXkeiLW","https://ap.wps.com/l/cbCaipHl8RXkeiLW","pdf",6939130,1,28,"English","en",105,"# Abstract\n## Introduction\n## Remaining Energy Estimation and Parameter Space Challenge\n## Parameter-Dependent Degradation Modeling\n## Physics-Based and Machine-Learning Approaches","[{\"question\":\"Why is exploring the energy storage parameter space difficult?\",\"answer\":\"Remaining energy depends on many operating and cell parameters, and all combinations form a very high-dimensional space. Exhaustive grid testing is prohibitively expensive, especially under cell-to-cell variability.\"},{\"question\":\"What method does the document use to explore parameters efficiently?\",\"answer\":\"It uses a Gaussian-process framework combined with Bayesian optimization, enhanced with domain knowledge. The approach selects future experiments to maximize information gain and minimize uncertainty.\"},{\"question\":\"How is remaining energy quantified in the proposed framework?\",\"answer\":\"The framework quantifies remaining energy using failure distributions. Bayesian optimization is then used to efficiently explore parameters that influence those distributions.\"}]","Machine-Learning-Based Efficient Parameter Space Exploration for Energy Storage Systems - ECS Meeting Abstracts | PDF",1785682966,71,{"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},"machine-learning-based-efficient-parameter-space-exploration-for-energy-storage-systems-ecs-meeting-abstracts","",{"@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/machine-learning-based-efficient-parameter-space-exploration-for-energy-storage-systems-ecs-meeting-abstracts/118310/",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},"Why is exploring the energy storage parameter space difficult?","Question",{"text":75,"@type":76},"Remaining energy depends on many operating and cell parameters, and all combinations form a very high-dimensional space. Exhaustive grid testing is prohibitively expensive, especially under cell-to-cell variability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method does the document use to explore parameters efficiently?",{"text":80,"@type":76},"It uses a Gaussian-process framework combined with Bayesian optimization, enhanced with domain knowledge. The approach selects future experiments to maximize information gain and minimize uncertainty.",{"name":82,"@type":73,"acceptedAnswer":83},"How is remaining energy quantified in the proposed framework?",{"text":84,"@type":76},"The framework quantifies remaining energy using failure distributions. Bayesian optimization is then used to efficiently explore parameters that influence those distributions.","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"]