[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122719-en":3,"doc-seo-122719-105":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":20,"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},122719,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",6,"Technology","Driving behavior-guided battery health monitoring for electric vehicles using machine learning","Accurate estimation of battery state of health (SOH) is essential for safe and reliable electric vehicle (EV) operation. Feature-based machine learning can monitor battery health quickly, yet performance may degrade when multiple health indicators (HIs) introduce feature redundancy. In addition, disregarding real-world driving behavior can make key features unavailable and reduce estimation accuracy. A feature-based learning pipeline is proposed by quantifying feature acquisition probability under practical driving scenarios. Features are screened using accuracy and correlation on public degradation datasets, followed by scenario-based feature fusion to balance performance and real-world practicality.","Driving behavior-guided battery health monitoring for electric vehicles using machine learning  \nNanhua Jiang a†, Jiawei Zhanga†, Weiran Jiangb, Yao Renb, Jing Linc, Edwin Khooc, and Ziyou Songa∗ aDepartment of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore.  \nbFarasis Energy USA, Inc., Hayward, CA 94545, USA.  \ncInstitute for Info COMM Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis,  \nSingapore 138632, Singapore.  \nAbstract  \nAn accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs) . Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms.  \n1. Introduction  \nWith the rising awareness of the reduction of carbon emissions, electric vehicles (EVs) are established as a leading candidate for the electrification of transportation because of their pollution-free characteristics [1] . Lithium-ion batteries (LIBs) are considered one of the most promising energy storage technologies for EVs, due to their high energy density, long cycle life, and reduction of manufacturing costs [2, 3] . However, the diverse and dynamic operating conditions may aggravate the irreversible side reactions in LIBs, which permanently deteriorate the performance of LIBs and cause capacity and power fade [4, 5], which must be accurately monitored for timely maintenance and replacement [6, 7] to ensure safe and reliable operations  \n†  \n∗  \nThese authors contributed equally to this work.  \nCorresponding authors. E-mail addresses (Ziyou Song): [ziyou@nus.edu.sg](ziyou@nus.edu.sg)  \nNomenclature  \nHealth indicators  \nACC  \nACCCC  \nACCCT  \nACCCV  \nACCDC  \nACCDT  \nACCDV  \nACT  \nACV  \nACVCC  \nACVCT  \nACVCV  \nADC  \nADT  \nADV  \nCCCT  \nCCDT  \nCDE-SOC  \nCDET  \nCVCT  \nDTA  \nDTP  \nDTPL  \nDTS  \nDVA  \nDVS  \nDVV  \nDVVL  \nECC  \nECCCC  \nECCCT  \nECCCV  \nECCDC  \nECCDT  \nECCDV  \nECT  \nECV  \nECVCC  \nECVCT  \nECVCV  \nEDC  \nEDT  \nEDV  \nHCCCT  \nHCT  \nHCVCT  \nHDT  \nHT  \nICA  \nICP  \nICP-SOC  \nICPL  \nICS  \nKT  \nLCCCT  \nLCT  \nLCVCT  \nLDT  \nLT  \nArea under charge current  \nArea under constant-current charge current  \nArea under constant-current charge temperature  \nArea under constant-current charge voltage  \nArea under constant-current discharge current  \nArea under constant-current discharge temperature  \nArea under constant-current discharge voltage Area under charge temperature  \nArea under charge voltage  \nArea under constant-voltage charge current  \nArea under constant-voltage charge temperature  \nArea under constant-voltage charge voltage Area under discharge current  \nArea under discharge temperature Area under discharge voltage Constant-cur","cbCaikEJn0tDyJ53","https://ap.wps.com/l/cbCaikEJn0tDyJ53","pdf",4376804,1,33,"English","en",105,"# Introduction\n## Battery degradation and the need for SOH monitoring\n## Limitations of existing feature-based approaches\n## Proposed driving-behavior-guided feature acquisition and fusion","[{\"question\":\"Why is battery state of health (SOH) estimation critical for electric vehicles?\",\"answer\":\"SOH reflects battery capacity and power fade caused by irreversible side reactions under diverse operating conditions, enabling timely maintenance and replacement for safe, reliable EV operation.\"},{\"question\":\"What challenges reduce the performance of feature-based battery health monitoring?\",\"answer\":\"Using multiple health indicators can cause feature redundancy, and ignoring real-world driving behaviors can lead to inaccurate results because some features are rarely accessible in practical scenarios.\"},{\"question\":\"How does the proposed method improve practicality of SOH monitoring?\",\"answer\":\"It evaluates feature acquisition probability under real-world driving conditions, screens features using estimation accuracy and correlation on public datasets, and applies scenario-based feature fusion to balance performance and practicality.\"}]","Driving behavior-guided battery health monitoring for electric vehicles using machine learning | PDF",1785812523,83,{"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},"driving-behavior-guided-battery-health-monitoring-for-electric-vehicles-using-machine-learning","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/driving-behavior-guided-battery-health-monitoring-for-electric-vehicles-using-machine-learning/122719/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is battery state of health (SOH) estimation critical for electric vehicles?","Question",{"text":75,"@type":76},"SOH reflects battery capacity and power fade caused by irreversible side reactions under diverse operating conditions, enabling timely maintenance and replacement for safe, reliable EV operation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges reduce the performance of feature-based battery health monitoring?",{"text":80,"@type":76},"Using multiple health indicators can cause feature redundancy, and ignoring real-world driving behaviors can lead to inaccurate results because some features are rarely accessible in practical scenarios.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve practicality of SOH monitoring?",{"text":84,"@type":76},"It evaluates feature acquisition probability under real-world driving conditions, screens features using estimation accuracy and correlation on public datasets, and applies scenario-based feature fusion to balance performance and practicality.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]