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A Realtime Intelligent Energy Management Strategy for Hybrid Electric Vehicle Using Reinforcement Learning

  • Unique Paper ID: 167164
  • Volume: 11
  • Issue: 3
  • PageNo: 748-754
  • Abstract:
  • Designing energy management strategy for hybrid electric vehicles (HEVs) using reinforcement learning (RL) provides the best way to increase energy efficiency and improve vehicle performance. The concept uses renewable energy sources such as photovoltaic (PV) arrays and wind turbines, as well as removable batteries to power the vehicle. Key components include photovoltaic arrays and wind turbines to collect renewable energy, converters to connect electrical equipment to electric vehicles, removable batteries to store more energy, and the engine for driving. The proposed strategy uses reinforcement learning techniques to continuously learn and adjust power management decisions based on real-time and driving data. RL allows vehicles to decide when to charge the battery, when to use energy storage, and when to buy electricity directly from renewable sources. The system is designed to optimize energy efficiency, reduce fuel consumption and reduce environmental impact by dynamically adjusting the energy flow of components. This energy management concept represents a breakthrough in hybrid electric vehicles and provides the potential for more sustainable and efficient transportation.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 748-754

A Realtime Intelligent Energy Management Strategy for Hybrid Electric Vehicle Using Reinforcement Learning

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