Reinforcement Learning-Driven Energy Management for Battery-Supercapacitor Hybrid Storage in Electric Vehicles

Title

Reinforcement Learning-Driven Energy Management for Battery-Supercapacitor Hybrid Storage in Electric Vehicles

Creator

Suresh, K.

Description

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.

Source

Proceedings of 4th International Conference on Electronics and Renewable Systems, ICEARS 2026;pp.941-945

Date

01-01-2026

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Affordable and Renewable Energy; Battery - Supercapacitor; Electric Vehicles; Energy Management Strategy; Hybrid Energy Storage System; Power Split Control; Reinforcement Learning; Resilience

Coverage

Suresh K., Christ Deemed to Be University, Department of Electrical and Electronics Engineering, Karnataka, Bangalore, 560074, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833154881-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICEARS67481.2026.11416618

https://www.scopus.com/pages/publications/105035742630?origin=resultslist