[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120019-en":3,"doc-seo-120019-105":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},120019,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids","Rising demand for sustainable transportation is accelerating electric vehicle adoption, while range limitations and frequent recharging needs continue to restrict performance. Machine learning-based energy optimization offers a data-driven path to analyze driving history, road, weather, and traffic conditions to predict and reduce energy waste, extending achievable range. Renewable microgrids further strengthen grid security and reliability through diverse renewable sources and battery storage, and support greenhouse-gas reduction. This review examines ML-driven energy management in such microgrids, including reconfigurable structures and charging-demand estimation for hybrid electric vehicles, improving operational cost and prediction accuracy.","Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids  \nDr. Sharon Sophia. J 1, Dr D David Winster Praveenraj 2, Kassem AL-Attabi3, Sheela Bijlwan4, Mayank Nagar5, and Sharayu Ikhar6  \n1Assistant Professor,School of Business and Management, CHRIST (Deemed to be University) Bangalore Yeshwantpur Campus, India.  \n2Assistant Professor ,School of Business and Management , CHRIST (Deemed to be University ) Bangalore Yeshwantpur Campus, India.  \n3The Islamic university, Najaf, Iraq.  \n4Department of Computing Sciences  \nUttaranchal School of Computing Sciences, Uttaranchal University, Dehradun-248007, India.  \n5 Department of Computer Science & Engineering, IES College of Technology,IES University, Bhopal, Madhya Pradesh 462044 India.  \n6Researcher, Yashika Journal Publications Pvt Ltd, Wardha, Maharashtra, India Email: [sharyu.ikhar@gmail.com](sharyu.ikhar@gmail.com).  \nAbstract. The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs) . Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions.  \nThis paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data.  \nBy understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector  \n1Corresponding Authour :[sharon.sophia@christuniversity.in](sharon.sophia@christuniversity.in)  \n[2](2david.winster@christuniversity.in)[david.winster@christuniversity.in](2david.winster@christuniversity.in)  \n[3](3kassem.alattabi@iunajaf.edu.iq)[kassem.alattabi@iunajaf.edu.iq](3kassem.alattabi@iunajaf.edu.iq)  \n[4](4sheela.bijlwan@gmail.com)[sheela.bijlwan@gmail.com](4sheela.bijlwan@gmail.com)  \n[5](5research@iesbpl.ac.in)[research@iesbpl.ac.in](5research@iesbpl.ac.in)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nmachines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs) . Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand  \nprediction accuracy.  \n1 Introduction  \nThe transition towards sustainable energy solutions has become a focal point in recent years, with renewable microgrids emerging as a cleaner and more competitive generation of power systems. These microgrids, harnessing energy from diverse renewable sources such as wind, solar, hydro, and biomass units, have demonstrated their potential in reducing power losses, costs, and carbon emissions. Furthermore, they play a pivotal role in enhancing power quality and operational efficiency. Integral to these renewable microgrids are battery storage units, which address the volatile and non-dispatchable nature of renewable energy sources. These storage units not only mitigate operational costs but also bolster the growth trajectory of renewable energy sources, emphasizing the indispensable nature of research in this domain.  \nConcurrently, the rise of electric vehicles (EVs) as a sustainab","cbCaifUHqA9vA3Cv","https://ap.wps.com/l/cbCaifUHqA9vA3Cv","pdf",1517744,1,9,"English","en",105,"# Introduction\n# Review and discussion","[{\"question\":\"What problems do electric vehicles face that motivate ML-driven energy management?\",\"answer\":\"Electric vehicles are limited by driving range constraints and the need for frequent recharging. ML-driven approaches aim to optimize energy use to reduce waste and extend driving range.\"},{\"question\":\"How do machine learning systems optimize EV energy consumption in renewable microgrids?\",\"answer\":\"They analyze historical driving data and external factors such as road conditions, weather, and traffic to predict energy consumption patterns, then optimize charging and usage decisions.\"},{\"question\":\"How does the integration of EVs with renewable microgrids improve overall energy management?\",\"answer\":\"The review highlights that EVs can address charging demand and can also function as mobile energy storage via vehicle-to-grid (V2G), supporting the smart grid during peak load hours.\"}]","Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids | PDF",1785727762,23,{"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-driven-energy-management-for-electric-vehicles-in-renewable-microgrids","",{"@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-driven-energy-management-for-electric-vehicles-in-renewable-microgrids/120019/",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","2026-08-03",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},"What problems do electric vehicles face that motivate ML-driven energy management?","Question",{"text":75,"@type":76},"Electric vehicles are limited by driving range constraints and the need for frequent recharging. ML-driven approaches aim to optimize energy use to reduce waste and extend driving range.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do machine learning systems optimize EV energy consumption in renewable microgrids?",{"text":80,"@type":76},"They analyze historical driving data and external factors such as road conditions, weather, and traffic to predict energy consumption patterns, then optimize charging and usage decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the integration of EVs with renewable microgrids improve overall energy management?",{"text":84,"@type":76},"The review highlights that EVs can address charging demand and can also function as mobile energy storage via vehicle-to-grid (V2G), supporting the smart grid during peak load hours.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]