Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles

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Year : 2026 | Volume : 04 | 02 | Page :
By

Divyanshu Nitnaware,

Ravikant Keshav Nanwatkar,

Somya Dubey,

  1. UG Student, School of Computer Science Engineering and Applications, D. Y. Patil International University, Akurdi, Pune, Maharashtra, India
  2. Assistant Professor, School of Computer Science Engineering and Applications, D. Y. Patil International University, Akurdi, Pune, Maharashtra, India
  3. Assistant Professor, School of Computer Science Engineering and Applications, D. Y. Patil International University, Akurdi, Pune, Maharashtra, India

Abstract

As the number of EVs increases, smart solutions for energy management are needed that will optimize energy use, prolong battery life and boost vehicle performance. The application of conventional rule based and optimization-based Energy Management Strategies (EMS) for Battery–Supercapacitor Hybrid Energy Storage Systems (HESS) often leads to sub-optimal power management, supercapacitor mismatch and battery degradation when subjected to varying driving conditions. This study aims to design an intelligent energy management approach for Battery–Supercapacitor HESS based on Deep Reinforcement Learning (DRL) technique in an EV. In this proposed work the novelty is in the integration of a DRL agent continuously learning the optimal power-sharing policy based on real-time vehicle operating conditions, without needing to know the control rules or accurate models of the system. The approach includes the formulation of a mathematical model of the battery–supercapacitor HESS, followed by the design of a DRL-based controller using algorithms like Deep Q- Network (DQN) or Proximal Policy Optimization (PPO), and testing the efficiency of the controller under typical driving cycles through MATLAB/Simulink and Python- reinforcement learning environments. The performance of the proposed strategy will be compared to the conventional rule based and fuzzy logic controllers by considering various performance parameters such as battery State of Charge (SOC), battery degradation, energy efficiency, peak power demand and computational response time. The desired result is an adaptive, robust and real-time EMS that optimises battery life, minimises energy losses and optimises regenerative braking efficiency. The proposed framework can be used for electric vehicles, hybrid electric vehicles, autonomous transportation systems, and smart mobility systems that need intelligent and sustainable energy management.

Keywords: Battery, Supercapacitor, Deep Reinforcement Learning, Deep Q-Network, Proximal Policy Optimization, Hybrid Energy Storage Systems.

How to cite this article: Divyanshu Nitnaware, Ravikant Keshav Nanwatkar, Somya Dubey. Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles. International Journal of Advanced Control and System Engineering. 2026; 04(02):-.
How to cite this URL: Divyanshu Nitnaware, Ravikant Keshav Nanwatkar, Somya Dubey. Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles. International Journal of Advanced Control and System Engineering. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijacse/article=2026/view=252781

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Ahead of Print Subscription Review Article
Volume 04
02
Received 23/07/2026
Accepted 29/07/2026
Published 17/08/2026
Publication Time 25 Days

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