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<p>Stringent emission norms and the increasing demand for fuel efficiency have accelerated the development of vehicles with alternative power plants, such as batteries, ultracapacitors, and fuel cells. While battery electric vehicles (EVs) reduce crude oil dependency, they suffer from range anxiety. Hybrid electric vehicles (HEVs) offer a feasible solution, improving fuel economy and reducing emissions. This research focuses on designing optimal energy management strategies (EMS) for a full-parallel HEV using heuristic rule-based and advanced dynamic programming approaches to manage power distribution between the internal combustion engine and the electric motor. An efficient forward-facing dynamic model of the hybrid powertrain was developed using MATLAB Simulink, incorporating a real-time legislative driving cycle. Integrating actual testing data enhances the real-time effectiveness of the developed EMS. Results indicate these strategies can significantly reduce fuel consumption, maintain battery state of charge (SoC) levels, and enhance overall vehicle performance by up to 15–60% compared to conventional diesel engine-based vehicles. The simulated results are in good agreement with the experimental results guaranteeing the controller’s peak performance in real-world scenarios. Moreover, the modified dynamic programming control strategy performs better than the rule-based and dynamic programming strategies in terms of battery charge sustenance and vehicle efficiency. This research provides crucial insights, advancing hybrid vehicle technology and promoting environmental sustainability.</p>

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Enhanced Energy Management in Full-Parallel Hybrid Electric Vehicles: An Integrated Testing and Mathematical Modeling Approach

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Rakesh V. Mulik,
  • Ekambaram Porpatham,
  • Senthil Kumar Arumugam

摘要

Stringent emission norms and the increasing demand for fuel efficiency have accelerated the development of vehicles with alternative power plants, such as batteries, ultracapacitors, and fuel cells. While battery electric vehicles (EVs) reduce crude oil dependency, they suffer from range anxiety. Hybrid electric vehicles (HEVs) offer a feasible solution, improving fuel economy and reducing emissions. This research focuses on designing optimal energy management strategies (EMS) for a full-parallel HEV using heuristic rule-based and advanced dynamic programming approaches to manage power distribution between the internal combustion engine and the electric motor. An efficient forward-facing dynamic model of the hybrid powertrain was developed using MATLAB Simulink, incorporating a real-time legislative driving cycle. Integrating actual testing data enhances the real-time effectiveness of the developed EMS. Results indicate these strategies can significantly reduce fuel consumption, maintain battery state of charge (SoC) levels, and enhance overall vehicle performance by up to 15–60% compared to conventional diesel engine-based vehicles. The simulated results are in good agreement with the experimental results guaranteeing the controller’s peak performance in real-world scenarios. Moreover, the modified dynamic programming control strategy performs better than the rule-based and dynamic programming strategies in terms of battery charge sustenance and vehicle efficiency. This research provides crucial insights, advancing hybrid vehicle technology and promoting environmental sustainability.