Hybrid Electric Vehicles: Energy Management Strategy Using Deep Reinforcement Incentive Learning

Strategic Insights -

A new study on mathematics has yielded significant findings on energy management strategies for hybrid electric vehicles (HEVs). Researchers from the Department of Mechanical Engineering proposed a deep reinforcement incentive learning (DRIL) algorithm to minimize fuel consumption and maintain battery charge sustainability in HEVs. The DRIL algorithm is designed to optimize power distribution and balance exploration and exploitation for power allocation. The study's results demonstrate a reduction in fuel costs by 3.47%-3.04% compared to existing DRL algorithms.

Key Takeaways:

  • The study aimed to develop an effective energy management strategy (EMS) for hybrid electric vehicles (HEVs) to achieve fuel-efficient and environmentally friendly mobility.
  • The proposed deep reinforcement incentive learning (DRIL) algorithm is designed to minimize fuel consumption and maintain battery charge sustainability in HEVs.
  • The DRIL algorithm optimizes power distribution and balances exploration and exploitation for power allocation.
  • The study's results demonstrate a reduction in fuel costs by 3.47%-3.04% compared to existing DRL algorithms.
  • The research was funded by the Scientific and Technological Research Council of Turkey, Ministry of Education, Universities and Research (MIUR), and the European Union (EU).
  • The study's findings have significant implications for the development of efficient and adaptive control strategies in HEVs.
  • The proposed DRIL algorithm can be applied to various driving conditions, including pre-training and human-in-the-loop (HIL) test-driving cycles.

Statistics:

  • The proposed DRIL algorithm reduces fuel costs by 3.47% compared to existing DRL algorithms under the pre-training driving cycle.
  • The DRIL algorithm reduces fuel costs by 3.04% compared to existing DRL algorithms under the HIL-obtained driving cycles.
  • The study's results demonstrate the effectiveness of the proposed DRIL algorithm in minimizing fuel consumption and maintaining battery charge sustainability.
  • The DRIL algorithm optimizes power distribution and balances exploration and exploitation for power allocation in HEVs.

Sources:

  • A Novel Energy Management Strategy for Hybrid Electric Vehicles Using Deep Reinforcement Incentive Learning. Energy, 2025; 334.
  • NewsRx. New Mathematics Findings from Department of Mechanical Engineering Reported (A Novel Energy Management Strategy for Hybrid Electric Vehicles Using Deep Reinforcement Incentive Learning). Journal of Engineering. October 20, 2025; p 2077.