[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123607-en":3,"doc-seo-123607-105":30,"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":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},123607,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Review of Machine Learning Method for Safety Management of Lithium-Ion Battery Energy Storage","Electrochemical energy storage is widely deployed, making safe operation and maintenance of battery energy storage power plants an increasingly critical problem. Conventional battery management systems mainly collect cell voltage, current, and temperature data, but hardware limits, transmission bandwidth, and latency hinder effective health and safety monitoring for large-scale systems. Applying machine learning to predict lithium-ion operating conditions offers a route to strengthen safety management. The paper introduces abuse and thermal-runaway risk mechanisms, discusses BMS architecture and use characteristics, and reviews machine-learning methods for health and safety analysis, aiming at safety assessment for energy-storage stations.","Review of machine learning method for safety management of lithium-ion battery energy storage  \nZhehua Du  \nWuhan Second Ship Design and Research Institute, Wuhan, Hubei, 430205, China  \nAbstract. With the broad implementation of electrochemical energy storage technology, the noteworthy issue of  \nensuring safe operation and maintenance of battery energy storage power plants has become more and more  \nprominent. The conventional battery management system solely acquires data on the voltage, current, and  \ntemperature of individual battery cells. Constrained by hardware processing capabilities, limitations in data  \ntransmission bandwidth, and latency issues, effectively monitoring the health and safety of large-scale battery  \nenergy storage systems has become a critical technological challenge. The implementation of machine learning  \ntechniques in predicting the operating conditions of lithium-ion batteries has provided opportunities for enhancing  \nthe safety management of energy storage systems. To address the safety management requirements of lithium-ion  \nbatteries, this paper firstly introduces research related to the risk mechanism of abusive use and thermal runaway of  \nsuch batteries. Next, the architecture and application characteristics of the lithium-ion battery management system  \nwill be discussed. The implementation of machine learning techniques for analyzing the health and safety status of  \nlithium-ion batteries is extensively discussed. Finally, a safety assessment of lithium-ion batteries for energy storage  \npower stations is anticipated.  \n1. Introduction  \nAmong all types of electrochemical energy storage, lithium-ion battery account for 90% of the market share. However, Li-ion battery system safety accidents characterized by thermal runaway often occur, which seriously threaten the safety of life and property. Therefore, the high safety of energy storage batteries under the condition of high energy density is the primary guarantee for the commercialization and application. The technical solution of the existing power storage battery management system is obviously not suitable for the safe operation and maintenance requirements of large-scale power storage power stations. There are problems such as large monitoring data, complex information types and urgent safety assessment. At the same time, due to thermal runaway characteristics, it is difficult to control lithiumion batteries in case of accidents, which may evolve into major safety accidents such as combustion and explosion of energy storage system.  \n2. Thermal runaway warning and safety management  \n2.1 Thermal runaway mechanism of battery  \nIn practical applications, if battery abuse occurs, battery material will be damaged and abnormal heating will occur. Heat accumulation intensifies the internal exothermic chemical reaction process, forming positive feedback, and eventually causing thermal runaway. Mechanical abuse, electrical abuse and thermal abuse are the main causes of reversible or irreversible damage to lithium-ion batteries. Literature [1] studies have shown that the decomposition of battery SEI film is the main source of exothermic reaction. Continued increase in temperature will lead to the reaction of negative metal lithium with electrolyte decomposition (about 120°C), membrane melting (130°C- 140°C), positive electrode decomposition (150°C-211°C), and cause runaway overcharge heat.  \n2.2 Characteristic parameters and early warning  \nLithium-ion batteries release a lot of heat and flammable gases. When the concentration of flammable gas reaches its explosion limit, an explosion will occur under the action of external high temperature. This will seriously affect energy storage power stations [2] . For electric energy storage systems, thermal runaway is usually caused by electrical abuse. Such as inappropriate overcharge and overdischarge conditions can cause a variety of side effects inside the battery. And that triggers thermal runaw","cbCaiiCtjGrGEcvQ","https://ap.wps.com/l/cbCaiiCtjGrGEcvQ","pdf",699886,1,4,"English","en",105,"# Introduction\n# Thermal Runaway Warning and Safety Management\n## Thermal runaway mechanism of battery\n## Characteristic parameters and early warning\n## Battery management system\n# Machine Learning Approach in Battery Safety Management\n## Machine learning methods for battery state estimation","[{\"question\":\"Why is safety management critical for lithium-ion battery energy storage systems?\",\"answer\":\"Lithium-ion systems account for most market share, but thermal runaway accidents are common and threaten life and property. High energy density demands reliable safety assurance for commercialization and large-scale operation.\"},{\"question\":\"What mechanisms lead to thermal runaway in lithium-ion batteries?\",\"answer\":\"Battery abuse can damage materials and cause abnormal heating. Heat accumulation intensifies internal exothermic reactions through positive feedback, eventually triggering thermal runaway under mechanical, electrical, or thermal abuse pathways.\"},{\"question\":\"What role does the battery management system (BMS) play in safety management?\",\"answer\":\"BMS monitors, estimates, and predicts battery state parameters to control charging and discharging. It integrates monitoring, prediction, control, and communication, allowing safe operation and mitigation of abnormal conditions by isolating or disconnecting cells.\"}]","Review of Machine Learning Method for Safety Management of Lithium-Ion Battery Energy Storage | PDF",1785817609,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"review-of-machine-learning-method-for-safety-management-of-lithium-ion-battery-energy-storage","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/review-of-machine-learning-method-for-safety-management-of-lithium-ion-battery-energy-storage/123607/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is safety management critical for lithium-ion battery energy storage systems?","Question",{"text":74,"@type":75},"Lithium-ion systems account for most market share, but thermal runaway accidents are common and threaten life and property. High energy density demands reliable safety assurance for commercialization and large-scale operation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What mechanisms lead to thermal runaway in lithium-ion batteries?",{"text":79,"@type":75},"Battery abuse can damage materials and cause abnormal heating. Heat accumulation intensifies internal exothermic reactions through positive feedback, eventually triggering thermal runaway under mechanical, electrical, or thermal abuse pathways.",{"name":81,"@type":72,"acceptedAnswer":82},"What role does the battery management system (BMS) play in safety management?",{"text":83,"@type":75},"BMS monitors, estimates, and predicts battery state parameters to control charging and discharging. It integrates monitoring, prediction, control, and communication, allowing safe operation and mitigation of abnormal conditions by isolating or disconnecting cells.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]