Fuel-Cost Reduction and Energy-Efficient Control of Plug-in Hybrid Electric Vehicles Using Fuzzy Cognitive Maps by Optimization of Control Strategy in Real Traffic Conditions

Document Type : Research paper

Authors

1 Digital Marketing Department, Faculty of Administrative and Financial Sciences, University of Petra, Jordan

2 Department of Business Administration, Business School, Al al-Bayt University, Mafraq 25113, Jordan; Research follower, INTI International University, 71800 Negeri Sembilan, Malaysia

3 Faculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia. Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160 Thailand;

4 4PhD researcher (Education), Termiz University of Economics and Service, Farovon street 4-b, Termez, Surxondaryo, Uzbekistan

5 6PhD, Associate Professor, Head of the department "World and regional economy", Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan

6 Candidate of Economic Sciences, Associate Professor, Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan

7 DSc, Acting Professor, Department of World and Regional Economics, Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan

8 Associate Professor, Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan

9 Teacher, Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan

10.22098/joape.2025.18936.2475

Abstract

This study introduces a novel supervisory control framework based on Fuzzy Cognitive Maps (FCM) for optimal energy management in plug-in hybrid electric vehicles (PHEVs). The proposed supervisory controller is structured to simultaneously satisfy the driver’s demanded power, maintain the battery state of charge (SOC) within an acceptable operating range, and reduce fuel consumption. Owing to the fact that the presented method does not require an accurate system model, the computational burden associated with deriving the control policy is significantly reduced, and the overall implementation becomes less complex compared with classical control approaches. The target PHEV considered in this research features a series–parallel powertrain architecture. To evaluate the effectiveness of the proposed control strategy, simulations are conducted using three standard driving cycles along with an urban driving cycle representative of metropolitans. The results demonstrate that the proposed FCM-based supervisory controller not only fulfills the demanded traction power but also lowers fuel consumption relative to conventional fuzzy controllers, while maintaining the SOC within an appropriate operational window.

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Main Subjects

Journal of Operation and Automation in Power Engineering
Volume 13, Special Issue
Intelligent and Sustainable Power Systems (ISPS): AI-Driven Innovations for Renewable Integration and Smart Grid Resilience
2025

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History

  • Receive Date: 30 November 2025
  • Revise Date: 23 December 2025
  • Accept Date: 25 December 2025
  • First Publish Date: 25 December 2025

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