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Khalilov, Sherzod, Abdiganiyeva, Sitorabonu, Abed Hussein, Abdullah, K. Abbas, Jamal, Ashoor Issa, Ali, abdali abdulridui, Hussam, Khalid Abozibid, Hassan, Naama Henedy, Sadiq, Ganiyev, Sardor, Khamdamov, Shoh-Jakhon, Nargiza Rasulovna, Samadova. (1404). Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method. سامانه مدیریت نشریات علمی, 12(Special Issue (Open)), 53-63. doi: 10.22098/joape.2025.17112.2334
Sherzod Khalilov; Sitorabonu Abdiganiyeva; Abdullah Abed Hussein; Jamal K. Abbas; Ali Ashoor Issa; Hussam abdali abdulridui; Hassan Khalid Abozibid; Sadiq Naama Henedy; Sardor Ganiyev; Shoh-Jakhon Khamdamov; Samadova Nargiza Rasulovna. "Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method". سامانه مدیریت نشریات علمی, 12, Special Issue (Open), 1404, 53-63. doi: 10.22098/joape.2025.17112.2334
Khalilov, Sherzod, Abdiganiyeva, Sitorabonu, Abed Hussein, Abdullah, K. Abbas, Jamal, Ashoor Issa, Ali, abdali abdulridui, Hussam, Khalid Abozibid, Hassan, Naama Henedy, Sadiq, Ganiyev, Sardor, Khamdamov, Shoh-Jakhon, Nargiza Rasulovna, Samadova. (1404). 'Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method', سامانه مدیریت نشریات علمی, 12(Special Issue (Open)), pp. 53-63. doi: 10.22098/joape.2025.17112.2334
Khalilov, Sherzod, Abdiganiyeva, Sitorabonu, Abed Hussein, Abdullah, K. Abbas, Jamal, Ashoor Issa, Ali, abdali abdulridui, Hussam, Khalid Abozibid, Hassan, Naama Henedy, Sadiq, Ganiyev, Sardor, Khamdamov, Shoh-Jakhon, Nargiza Rasulovna, Samadova. Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method. سامانه مدیریت نشریات علمی, 1404; 12(Special Issue (Open)): 53-63. doi: 10.22098/joape.2025.17112.2334

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

Journal of Operation and Automation in Power Engineering
مقاله 7، دوره 12، Special Issue (Open)، 2024، صفحه 53-63    اصل مقاله (3.94 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.17112.2334
نویسندگان
Sherzod Khalilov* 1؛ Sitorabonu Abdiganiyeva1؛ Abdullah Abed Hussein2؛ Jamal K. Abbas3؛ Ali Ashoor Issa4؛ Hussam abdali abdulridui5؛ Hassan Khalid Abozibid6؛ Sadiq Naama Henedy7؛ Sardor Ganiyev1؛ Shoh-Jakhon Khamdamov8؛ Samadova Nargiza Rasulovna9
1International School of Finance Technology and Science, Tashkent, Uzbekistan.
2Department of Sciences, Al-Manara College for Medical Sciences, Maysan, Iraq.
3Al-Nisour University College, Nisour Seq. Karkh, Baghdad, Iraq.
4College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq.
5Al-Hadi University College, Baghdad, 10011, Iraq.
6Al-Zahrawi University College, Karbala, Iraq.
7Mazaya University College, Nasiriyah, Iraq.
8Mamun University, Khiva City, 220900, Uzbekistan.
9Tashkent State University of Economics, Islam Karimov, Tashkent, Uzbekistan.
چکیده
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.
کلیدواژه‌ها
Hybrid electric vehicle؛ energy management strategy؛ fuzzy logic controller؛ genetic algorithm؛ power train optimization؛ fuel efficiency؛ emissions reduction
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