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Energy Management in Hybrid Energy Storage Systems for Electric Vehicles: A Reinforcement Learning Approach with Python-Simulink Integration
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| Author(s) |
Parisa RANJBARAN, Alireza BAKHSHAI, Praveen JAIN |
| Abstract |
A Reinforcement Learning (RL) algorithm is employed for the energy management of a Hybrid Energy Storage System (HESS) in All Electric Vehicles (AEVs), focusing on enhancing battery life and vehicle mileage. Simulink\_gym is used as the interface between Python and Simulink environment to enable seamless communication for efficient simulation and training. The results indicate that the RL-based Energy Management Strategy (EMS) significantly outperforms conventional rule-based strategies by achieving superior power sharing in HESS, leading to enhanced energy efficiency and more balanced utilization of storage components. |
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| Filename: | 0342-epe2025-full-04434848.pdf |
| Filesize: | 532.2 KB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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