آمار

تعداد نشریات31
تعداد شماره‌ها453
تعداد مقالات4,019
تعداد مشاهده مقاله6,763,773
تعداد دریافت فایل اصل مقاله4,524,634
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. (1403). Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method. سامانه مدیریت نشریات علمی, 12(Special Issue), 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, 1403, 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. (1403). 'Designing an Energy Management Control System in Hybrid Vehicles Using an Optimized Fuzzy Method', سامانه مدیریت نشریات علمی, 12(Special Issue), 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, 1403; 12(Special Issue): 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، 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
مراجع
  1. S. Ahmadi and S. M. T. Bathaee, “Multi-objective genetic optimization of the fuel cell hybrid vehicle supervisory system: Fuzzy logic and operating mode control strategies,” Int. J. Hydrog. Energy, vol. 40, no. 36, pp. 12512–12521, 2015.
  2. A. A. Issa, M. A. Hussein, J. K. Abbas, L. Hussein, and M. Altaee, “Investigating power transmission structures in hybrid electric vehicle to improve fuel consumption and emissions,” Procedia Environ. Sci. Eng. Manage., vol. 11, no. 2, pp. 249–258, 2024.
  3. S. Meng, Z. Xin-Jian, C. Hong-Fei, and S. Hai-Feng, “An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system,” Energy, vol. 67, pp. 268–275, 2014.
  4. S. R. Moroa, P. A. Cauchick-Miguel, T. T. de Sousa-Zomer, and G. H. de Sousa Mendes, “Design of a sustainable electric vehicle sharing business model in the brazilian context,” Int. J. Ind. Eng. Manage., vol. 14, no. 2, pp. 147–161, 2023.
  5. M.    Mahmoodi-k,    M.    Montazeri,    and    V.    Madanipour, “Simultaneous multi-objective optimization of a phev power management system and component sizing in real world traffic condition,” Energy, vol. 233, p. 121111, 2021.
  6. H. Shayeghi and A. Younesi, “Enhancement of voltage/frequency stability in an autonomous micro energy grid with penetration of wind energy using a parallel fuzzy mechanism,” Iran. J. Electr. Electron. Eng., vol. 16, no. 4, 2020.
  7. K. Chang, P. M. S. Choi, and S. H. Huang, “How are new-energy vehicle sales affected by government support?
    u.s. geospatial evidence,” Econ. Ann.-XXI, vol. 160, no. 7-8, pp. 14–23, 2016.
  8. H. Maghfiroh, O. Wahyunggoro, and A. I. Cahyadi, “Energy management in hybrid electric and hybrid energy storage system vehicles: A fuzzy logic controller review,” IEEE Access, 2024.
  9. A. Bes¸kardes¸, Y. Hames¸, and K. Kaya, “A comprehensive review on fuzzy logic control systems for all, hybrid, and fuel cell electric vehicles,” Soft Comput., vol. 28, no. 13, pp. 8183–8221, 2024.
  10. S. Huo, J. Bai, S. Gao, and Y. Liu, “Sampling strategy selection for emc simulation surrogate model in uncertainty analysis and electromagnetic optimization design,” Prog. Electromagn. Res.-C, vol. 145, 2024.
  11. T. L. Oladosu, J. Pasupuleti, T. S. Kiong, S. P. J. Koh, and T. Yusaf, “Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review,” Int. J. Hydrog. Energy, vol. 61, pp. 1380–1404, 2024.
  12. A. Urooj and A. Nasir, “Review of intelligent energy management techniques for hybrid electric vehicles,” J. Energy Storage, vol. 92, p. 112132, 2024.
  13. B. Lin, C. Wei, F. Feng, and T. Liu, “A predictive energy management strategy for heavy hybrid electric vehicles based on adaptive network-based fuzzy inference system-optimized time horizon,” Energies, vol. 17, no. 10, p. 2288, 2024.
  14. J. Jiang, C. Wang, Q. Zhang, Y. Zhang, and Q. Yu, “Wavelet transform–based real–time energy management strategy of hybrid energy storage system for electric vehicle,” Int. J. Circuit Theory Appl., vol. 52, no. 3, pp. 1136–1157, 2024.
  15. M. B. F. Ahsan, S. Mekhilef, T. K. Soon, M. B. Mubin, P. Shrivastava, and M. Seyedmahmoudian, “Lithium-ion battery and supercapacitor-based hybrid energy storage system for electric vehicle applications: A review,” Int. J. Energy Res., vol. 46, no. 14, pp. 19826–19854, 2022.
  16. H. Maghfiroh, O. Wahyunggoro, and A. I. Cahyadi, “Energy management in hybrid electric and hybrid energy storage system vehicles: A fuzzy logic controller review,” IEEE Access, 2024.
  17. A. Mazouzi, N. Hadroug, W. Alayed, A. Hafaifa, A. Iratni, and A. Kouzou, “Comprehensive optimization of fuzzy logic-based energy management system for fuel-cell hybrid electric vehicle using genetic algorithm,” Int. J. Hydrog. Energy, vol. 81, pp. 889–905, 2024.
  18. J. J. Eckert, L. C. de Alkmin Silva, F. G. Dedini, and F. C. Corrêa, “Electric vehicle powertrain and fuzzy control multi-objective optimization, considering dual hybrid energy storage systems,” IEEE Trans. Veh. Technol., vol. 69, no. 4, pp. 3773–3782, 2020.
  19. Bureau d’Economie Théorique et Appliquée, Institut Français de l’Énergie, Institut d’Économie et de Politique de l’Énergie, Assoc. Française de l’Hydrogène, Avadikyan, A., Larrue, P., “The partnership for a new generation of vehicles and the us doe transportation fuel cells programme,” tech. rep., The Economic Dynamics of Fuel Cell Technologies, 2003.
  20. S. M. R. Rostami and Z. Al-Shibaany, “Intelligent energy management for full-active hybrid energy storage systems in electric vehicles using teaching–learning-based optimisation in fuzzy logic algorithms,” IEEE Access, 2024.
  21. D. Zhong, B. Liu, L. Liu, W. Fan, and J. Tang, “Artificial intelligence algorithms for hybrid electric powertrain system control: A review,” Energies, vol. 18, no. 8, p. 2018, 2025.
  22. J. Omakor, M. Alzayed, and H. Chaoui, “Particle swarm–optimized fuzzy logic energy management of hybrid energy storage in electric vehicles,” Energies, vol. 17, no. 9, p. 2163, 2024.
  23. S.    Terenchuk,    B.    Yeremenko,    S.    Kartavykh,    and O. Nasikovskyi, “Application of fuzzy mathematics methods to processing geometric parameters of degradation of building structures,” Eureka: Phys. Eng., no. 1, pp. 56–62, 2018.
  24. G. D. Uribe-Guerra, D. A. Múnera-Ramírez, and J. D. Arias-Londoño, “Feed formulation using multi-objective bayesian optimization,” Comput. Electron. Agric., vol. 224, p. 109173, 2024.
  25. J. I. Huertas, O. Serrano-Guevara, J. Díaz-Ramírez, D. Prato, and L. Tabares, “Real vehicle fuel consumption in logistic corridors,” Appl. Energy, vol. 314, p. 118921, 2022.
  26. Y. Huang, N. C. Surawski, B. Organ, J. L. Zhou, O. H. H. Tang, and E. F. C. Chan, “Fuel consumption and emissions performance under real driving: Comparison between hybrid and conventional vehicles,” Sci. Total Environ., vol. 659, pp. 275–282, 2019.
  27. G. Li, J. Zhang, and H. He, “Battery soc constraint comparison for predictive energy management of plug-in hybrid electric bus,” Appl. Energy, vol. 194, pp. 578–587, 2017.
  28. P. E. Bailey, A. Marathe, D. K. Lowenthal, B. Rountree, and M. Schulz, “Finding the limits of power-constrained application performance,” in Proc. Int. Conf. High Perform. Comput., Netw., Storage Anal., pp. 1–12, 2015.
  29. E. Kamal, L. Adouane, R. Abdrakhmanov, and N. Ouddah, “Hierarchical and adaptive neuro-fuzzy control for intelligent energy management in hybrid electric vehicles,” IFAC-Pap. OnLine, vol. 50, no. 1, pp. 3014–3021, 2017.
  30. J. J. Eckert, F. M. Santiciolli, E. Bertoti, E. dos Santos Costa, F. C. Corrêa, L. C. A. da Silva, and F. G. Dedini, “Gear shifting multi-objective optimization to improve vehicle performance, fuel consumption, and engine emissions,” Mech. Based Des. Struct. Mach., vol. 46, no. 2, pp. 238–253, 2018.
آمار
تعداد مشاهده مقاله: 478
تعداد دریافت فایل اصل مقاله: 286


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب