Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method

Document Type : Research paper

Authors

1 Head of the Accounting Department, Associate professor of International School of Finance Technology and Science, Tashkent, Uzbekistan;

2 Associate professor of International School of Finance Technology and Science, Tashkent, Uzbekistan;

3 department of sciences/ Al-Manara College For Medical Sciences/ (Maysan)/Iraq

4 Al-Nisour University College, Nisour Seq. Karkh, Baghdad, Iraq

5 College of Technical Engineering, National University of Science and Technology, Dhi Qar, 64001, Iraq

6 Al-Hadi University College, Baghdad,10011, Iraq

7 Al-Zahrawi University College, Karbala, Iraq

8 Mazaya University College/ Iraq

9 PhD, Associate Professor, Mamun University, Khiva City, 220900, Uzbekistan

10 Tashkent State University of Economics 49, Islam Karimov str., 100066, Tashkent, Uzbekistan

10.22098/joape.2024.17112.2334

Abstract

Improving fuel efficiency and enhancing the dynamic performance of hybrid electric vehicles are critical challenges in modern powertrain control design. This paper proposes a novel optimized fuzzy logic-based energy management strategy specifically developed for a Class B HEV. The main objective is to reduce fuel consumption and emissions while ensuring effective power distribution among key drivetrain components. The study introduces a two-stage methodology: first, an optimal sizing of the powertrain components—internal combustion engine, electric motor, and battery—is achieved using a genetic algorithm, ensuring the most efficient configuration for vehicle performance. Second, three different energy management strategies are implemented and compared: a conventional rule-based control, a standard fuzzy logic controller, and the proposed optimized fuzzy controller. Simulation results demonstrate that the optimized fuzzy strategy significantly improves fuel economy and emission performance compared to the other methods. Specifically, it achieves up to 20% better fuel efficiency than the rule-based controller while maintaining smooth power transitions. The study also highlights the impact of component sizing on control effectiveness, reinforcing the advantage of co-optimization of both sizing and control logic. The findings suggest that integrating intelligent optimization techniques such as GA with fuzzy control logic provides a superior approach to energy management in HEVs. This makes the proposed method a promising solution for next-generation hybrid vehicle applications aiming for both environmental sustainability and high performance.

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Main Subjects

Journal of Operation and Automation in Power Engineering
Volume 12, Special Issue (Open)
Advanced Technologies for Resilient and Efficient Microgrid Management: Innovations in Energy Optimization, Security, and Integration
2024

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History

  • Receive Date: 04 April 2025
  • Revise Date: 03 July 2025
  • Accept Date: 10 July 2025
  • First Publish Date: 10 July 2025

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