[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126307-en":3,"doc-seo-126307-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126307,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Quantum Machine Learning for Battery Health and Thermal Risk Prediction","The rapid growth of connected Electric Vehicles (EV) in Intelligent Transport Systems (ITS) drives the need for real-time Lithium-ion battery health and thermal risk management. Lithium-ion batteries degrade over time and can undergo thermal runaway, creating safety and operational challenges. A Quantum Machine Learning (QML) and Agent-Based Model (ABM) framework is presented to simulate and predict EV behavior under multiple degradation conditions using a VQNN trained on NASA battery datasets. The model classifies EVs into four states (healthy, degraded fixed/mobile charging, and thermal runaway) achieving 96% accuracy, surpassing an LSTM baseline (91%) and delivering lower RMSE (0.1562 vs 0.6455).","Technological University Dublin  \nARROW@TU Dublin  \n\n| SAML-25 Workshop on Statistical and Machine Learning | Research Institutes/Centres/Groups |\n| --- | --- |\n| 2025-06-06\u003Cbr>Quantum Machine Learning for Battery Health and Thermal Risk Prediction\u003Cbr>Alexander Mutiso Mutua\u003Cbr>Technological University Dublin, [d22124754@mytudublin.ie](d22124754@mytudublin.ie)\u003Cbr>Ruairí de Fréin\u003Cbr>Technological University Dublin, [ruairi.defrein@tudublin.ie](ruairi.defrein@tudublin.ie)\u003Cbr>Follow this and additional works at: [https://arrow.tudublin.ie/saml](https://arrow.tudublin.ie/saml)\u003Cbr> Part of the Statistics and Probability Commons |  |\n\nRecommended Citation  \nMutiso Mutua, Alexander and de Fréin, Ruairí, \"Quantum Machine Learning for Battery Health and Thermal Risk Prediction\" (2025) . SAML-25 Workshop on Statistical and Machine Learning. 26.  \n[https://arrow.tudublin.ie/saml/26](https://arrow.tudublin.ie/saml/26)  \nThis Conference Paper is brought to you by the EUt+ Academic Press a free to read and publish press of the European University of Technology.  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.  \nQuantum Machine Learning for Battery Health and Thermal Risk  \nPrediction  \nAlexander Mutiso Mutua  \n[d22124754@mytudublin.ie](d22124754@mytudublin.ie)[ ](d22124754@mytudublin.ie)Technological University Dublin Dublin, Ireland  \nRuairí de Fréin  \n[ruairi.defrein@tudublin.ie](ruairi.defrein@tudublin.ie)[ ](ruairi.defrein@tudublin.ie)Technological University Dublin Dublin, Ireland  \nAbstract  \nThe rapid growth of connected Electric Vehicles (EV) as part of modern Intelligent Transport Systems (ITS) motivates the need for real-time management of Lithium-ion (Li-ion) battery health and thermal risks. Li-ion batteries, although widely used, are prone to degradation and thermal runaway, posing significant challenges for safe and efficient EV operation. We present a Quantum Machine Learning (QML) and Agent-Based Model (ABM) that simulatesand predicts EV behaviour under various battery degradation conditions. We use a Variational Quantum Neural Network (VQNN) trained on NASA battery datasets to classify EVs into four categories: healthy, degraded for fixed chargers, degraded for mobile chargers and thermal runaway based on their State of Health (SoH) and thermal risk. We simulate a fleet of 50 EVs and demonstrate that the VQNN classifies healthy and batteries at risk and achievesan accuracy of 96%, outperforming an LSTM baseline of 91% . The VQNN outperformed the LSTM on the NASA dataset, achieving a significantly lower RMSE of 0.1562 compared to 0.6455. Our results show that the QML-ABM framework improves battery health and thermal risk prediction, enhances safety, Mobile Charging as a Service (MCaaS), and supports real-time decision-making in ITS.  \nKeywords  \nState of Health, Lithium-ion, Quantum Machine Learning, Intelligent Battery Systems, Variational Quantum Neural Networks, Mobile Charging Points  \n1 Introduction  \nThe growing demand for sustainable urban mobility has accelerated the development of Intelligent Transportation Systems (ITS), particularly the deployment of Connected and Autonomous Vehicles (CAV) and Electric Vehicles (EV). One key barrier to adopting EVs and CAVs is battery reliability and safety. Lithium-ion (Li-ion) batteries are the dominant energy storage technology in EVs and can be integrated with renewable energy from microgrids [4, 5] . Li-ion batteries are prone to degradation over time. They can experience thermal runaway, which is an uncontrollable increase in temperature that may lead to battery failure, vehicle breakdowns, or even accidents [13] . This can lead to range anxiety for both users and operators, which is not only caused by insufficient Charging Points (CP) but also battery health [10] .  \nIn our previous work [10, 11], we proposed GEECharge, a datadriven framework for optimal CP deployment in Dublin based on population density and t","cbCaipRAmZ083Lm7","https://ap.wps.com/l/cbCaipRAmZ083Lm7","pdf",3130010,9,1,4,"English","en",105,"# 1 Introduction\n## 2 Quantum Machine Learning and Agent-Based Modelling Framework\n## 3 Experimental Setup and Results\n## 4 Discussion and Real-Time Decision-Making Implications\n## 5 Conclusion","[{\"question\":\"Why is real-time lithium-ion battery health and thermal risk prediction important for EVs?\",\"answer\":\"Battery degradation and thermal runaway can threaten safe and efficient EV operation. Real-time prediction supports proactive risk management and safer ITS operation.\"},{\"question\":\"What is the proposed modeling approach in this paper?\",\"answer\":\"The work combines Quantum Machine Learning (QML) with an Agent-Based Model (ABM). A Variational Quantum Neural Network (VQNN) predicts health and thermal-risk categories while ABM simulates interactions among EVs, fixed chargers, and mobile charging units.\"},{\"question\":\"How does the VQNN perform compared with the LSTM baseline?\",\"answer\":\"The VQNN achieves 96% accuracy versus 91% for the LSTM baseline. It also yields a significantly lower RMSE on the NASA dataset (0.1562 vs 0.6455).\"}]","Quantum Machine Learning for Battery Health and Thermal Risk Prediction | PDF",1785904366,10,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"quantum-machine-learning-for-battery-health-and-thermal-risk-prediction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":22},"https://docshare.wps.com/document/quantum-machine-learning-for-battery-health-and-thermal-risk-prediction/126307/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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},"Why is real-time lithium-ion battery health and thermal risk prediction important for EVs?","Question",{"text":76,"@type":77},"Battery degradation and thermal runaway can threaten safe and efficient EV operation. Real-time prediction supports proactive risk management and safer ITS operation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the proposed modeling approach in this paper?",{"text":81,"@type":77},"The work combines Quantum Machine Learning (QML) with an Agent-Based Model (ABM). A Variational Quantum Neural Network (VQNN) predicts health and thermal-risk categories while ABM simulates interactions among EVs, fixed chargers, and mobile charging units.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the VQNN perform compared with the LSTM baseline?",{"text":85,"@type":77},"The VQNN achieves 96% accuracy versus 91% for the LSTM baseline. 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