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<p>In the rapidly growing Electric Vehicle (EV) sector, achieving superior battery performance, extended range, and optimized energy usage are central goals of modern Energy Management Systems (EMS). Reinforcement Learning (RL), a branch of machine learning, presents an ideal solution for dynamic and adaptive energy management. By factoring in user behavior, driving conditions, and energy consumption, RL enables real-time energy allocation in EVs. This research investigates how RL algorithms, such as Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), can optimize energy distribution among various EV components, including the drivetrain, auxiliary systems, and regenerative braking. By modeling energy management as a Markov Decision Process (MDP), RL-based EMS can dynamically adjust to road conditions, traffic patterns, and weather changes. Simulation results show that RL outperforms traditional rule-based systems in terms of energy efficiency, battery lifespan, and overall performance. The study also explores solutions to computational complexity and weak training frameworks using approaches like transfer learning. The method proposed shows an energy efficiency improvement of 97.89%, battery life extension of 98.52%, adaptability to varying conditions at 96.98%, regenerative braking efficiency of 98.25%, and a 32.47% reduction in computational complexity.</p> Graphical abstract <p></p>

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Smart energy optimization in electric vehicles using reinforcement learning algorithms

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • P. Aruna Jeyanthy,
  • S. Manoraja

摘要

In the rapidly growing Electric Vehicle (EV) sector, achieving superior battery performance, extended range, and optimized energy usage are central goals of modern Energy Management Systems (EMS). Reinforcement Learning (RL), a branch of machine learning, presents an ideal solution for dynamic and adaptive energy management. By factoring in user behavior, driving conditions, and energy consumption, RL enables real-time energy allocation in EVs. This research investigates how RL algorithms, such as Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), can optimize energy distribution among various EV components, including the drivetrain, auxiliary systems, and regenerative braking. By modeling energy management as a Markov Decision Process (MDP), RL-based EMS can dynamically adjust to road conditions, traffic patterns, and weather changes. Simulation results show that RL outperforms traditional rule-based systems in terms of energy efficiency, battery lifespan, and overall performance. The study also explores solutions to computational complexity and weak training frameworks using approaches like transfer learning. The method proposed shows an energy efficiency improvement of 97.89%, battery life extension of 98.52%, adaptability to varying conditions at 96.98%, regenerative braking efficiency of 98.25%, and a 32.47% reduction in computational complexity.

Graphical abstract