[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118990-en":3,"doc-seo-118990-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118990,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Innovations in Electric Vehicle Energy Management Systems - Integrating Machine Learning and Artificial Intelligence","Electric vehicle energy management is enhanced through machine learning and artificial intelligence across batteries, charging-infrastructure utilization, and predictive maintenance. The approach improves energy efficiency by determining effective routing, integrating renewable sources into the charging ecosystem, and measuring consumed energy accurately. Despite strong potential to elevate user experience, efficiency, and overall performance, adoption is constrained by data-privacy concerns, charging-infrastructure challenges, model compatibility, and the need for standardized protocols. Collaboration among governments, corporations, and authorities is emphasized to accelerate EV adoption.","Innovations in Electric Vehicle Energy Management Systems by Integrating Machine Learning and Artificial Intelligence  \nShanmukha Naga Raju Vonteddu  \n*Research Scholar, Department of Electrical and Electronics Engineering University of Petroleum and Energy Studies  \nDehradun,India.  \nAsst. Professor(C), Dept. of Electrical and Electronics Engineering, University College of Engineering Kakinada,  \nJNTU Kakinada, India  \n[raj.shanmukha@gmail.com](raj.shanmukha@gmail.com)  \nRavindra Kollu  \nProfessor, Dept. of Electrical and Electronics Engineering,  \nUniversity College of Engineering Kakinada, JNTU Kakinada, India.  \n[ravikollu@jntucek.ac.in](ravikollu@jntucek.ac.in)  \nPrasanthi Kumari Nunna  \nAssociate Professor, Dept. of Electrical and Electronic Engineering,  \nUniversity of Petroleum and Energy Studies, Dehradun, India.  \n[prasanti@ddn.upes.ac.in](prasanti@ddn.upes.ac.in)  \nAbstract—There are numerous aspects of managing electric vehicles that are being improved by machine learning and artificial intelligence. These aspects include the management of batteries, the most efficient use of charging infrastructure, the prediction of repair needs, and the conservation of energy. By determining the most effective routes for vehicles, including renewable energy sources into the charging ecosystem, and accurately measuring the amount of energy that is being consumed, machine learning and artificial intelligence in electric vehicle management systems make these systems more energy efficient. The widespread implementation of machine learning and artificial intelligence in this research in electric car management systems is still being hampered by a number of factors, including concerns around data privacy, issues with charging infrastructure and model compatibility, and the requirement for standardized protocols and standards. The application of machine learning and artificial intelligence in the management systems of electric cars has the potential to enhance the user experience, make the systems more efficient, and increase their overall performance. It is of the utmost importance that governments, corporations , and authorities continue to collaborate in order to determine the most effective ways to utilize this technology and encourage a greater number of people to purchase electric vehicles.  \nKeywords- Predictive Maintenance, Energy Optimization, Autonomous Charging, Range Prediction, Vehicle-to-Grid (V2G) Integration, Adaptive Cruise Control, AI, ML  \nI. INTRODUCTION  \nRecently, there has been a big change in the auto industry's view of electric vehicles (EVs) . These vehicles are now widely seen as better for the environment and more environmentally friendly than traditional gasoline-powered cars. Electric vehicles are becoming more and more common, which has led to the creation of cutting-edge technology solutions that make them more efficient, better perform, and easier to use. The growth of electric vehicle management systems (EVMSs), which use machine learning and artificial intelligence, is a very interesting subject to research[1] . These technologies are very important for making electric cars work better, especially when it comes to managing batteries, figuring out how far an EV can go, saving energy, and making use of charging stations  \nThis research will look at the newest changes in EVMS technology, with a focus on how machine learning (ML) and artificial intelligence (AI) algorithms are being combined.  \nIntelligent systems can use the huge amounts of data that electric cars (EVs) and their surroundings produce to make EVs more efficient and effective by giving them real-time information and the ability to make decisions. The main features, functions, and benefits of EVMS (Earned Value Management Systems) powered by Machine Learning (ML) and Artificial Intelligence (AI) will be looked into in this research by looking at current research, and new trends. Also, an evaluation will be done to find out the pros and cons","cbCailqZroE1o5UP","https://ap.wps.com/l/cbCailqZroE1o5UP","pdf",405424,1,5,"English","en",105,"# Introduction\n## Motivation and background\n## Scope and objectives\n# Related Work\n## AI and machine learning for EV efficiency\n## Progress and breakthroughs","[{\"question\":\"Which EV energy management aspects are targeted by machine learning and AI?\",\"answer\":\"They are applied to battery management, charging infrastructure utilization, repair/maintenance prediction, energy conservation, routing decisions, and accurate energy-consumption measurement.\"},{\"question\":\"What challenges hinder widespread adoption of ML/AI in EV management systems?\",\"answer\":\"Data privacy concerns, charging infrastructure issues, model compatibility problems, and the requirement for standardized protocols and standards.\"},{\"question\":\"How can integrating ML/AI improve electric vehicle performance and user experience?\",\"answer\":\"By enabling real-time, data-driven decisions that make charging, driving, and overall EV operation more efficient, sustainable, and effective, while improving system performance and the user experience.\"}]","Innovations in Electric Vehicle Energy Management Systems - Integrating Machine Learning and Artificial Intelligence | PDF",1785721479,13,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"innovations-in-electric-vehicle-energy-management-systems-integrating-machine-learning-and-artificial-intelligence","",{"@graph":36,"@context":86},[37,54,69],{"@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/innovations-in-electric-vehicle-energy-management-systems-integrating-machine-learning-and-artificial-intelligence/118990/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which EV energy management aspects are targeted by machine learning and AI?","Question",{"text":76,"@type":77},"They are applied to battery management, charging infrastructure utilization, repair/maintenance prediction, energy conservation, routing decisions, and accurate energy-consumption measurement.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What challenges hinder widespread adoption of ML/AI in EV management systems?",{"text":81,"@type":77},"Data privacy concerns, charging infrastructure issues, model compatibility problems, and the requirement for standardized protocols and standards.",{"name":83,"@type":74,"acceptedAnswer":84},"How can integrating ML/AI improve electric vehicle performance and user experience?",{"text":85,"@type":77},"By enabling real-time, data-driven decisions that make charging, driving, and overall EV operation more efficient, sustainable, and effective, while improving system performance and the user experience.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"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":21,"slug":138},19,"General","general"]