Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Suresh, K. Title A name given to the resource Reinforcement Learning-Driven Energy Management for Battery-Supercapacitor Hybrid Storage in Electric Vehicles Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of 4th International Conference on Electronics and Renewable Systems, ICEARS 2026;pp.941-945 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEARS67481.2026.11416618" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEARS67481.2026.11416618</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035742630?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035742630?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Suresh K., Christ Deemed to Be University, Department of Electrical and Electronics Engineering, Karnataka, Bangalore, 560074, India Description An account of the resource The fast growth of the electric vehicles (EVs) market has increased the requirements towards high power transients, efficiency, and reliability on automotive onboard energy management systems by extending battery lifetime. Pure battery storage systems are similarly subject to frequent peak power demands during rapid acceleration and regenerative braking, and thus suffer from rapid aging. Aiming at this issue, in this paper, an AI-based EMS for a battery-supercapacitor HESS in EVs is developed. Dynamic driving conditions are handled by an RL-based power splitting control strategy which dynamically divides power between lithium-ion battery and supercapacitor in this context. The battery stress is to be minimized with the stabilization of the DC-link voltage and traction power demand. System modeling and validation is carried out in MATLAB/Simulink with the use of typical urban drive cycles. Simulation results show that, compared with a rule-based control of the EMS, our proposed AI-enabled EMS can decrease battery peak current by 38.6%, enhance energy efficiency by 11.2%, and increase cycle life by around 27%. The deviation of the DC-link voltage is limited within 1.8% and such control can be used to reduce total system response time in rapid load transition by 22%. Comparison results reveal that the optimal management framework has better adaptability and stability when compared to the corresponding one under different loads and driving conditions, which are promising for next generation EVs energy management issues. 2026 IEEE. Subject The topic of the resource Affordable and Renewable Energy; Battery - Supercapacitor; Electric Vehicles; Energy Management Strategy; Hybrid Energy Storage System; Power Split Control; Reinforcement Learning; Resilience Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154881-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online