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Buck converter topology for fuel cell hybrid vehicles

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Warianty tytułu
PL
Topologia przetwornicy buck dla pojazdów hybrydowych z ogniwami paliwowymi
Języki publikacji
EN
Abstrakty
EN
Fuel cell hybrid vehicles are electric vehicles with energy conversion technologies that combine fuel cells and batteries. The Energy Management System is crucial to the fuel cell hybrid system's operation since it lowers the system's hydrogen usage. This study looks into how to best manage the fuel cell hybrid vehicles' connected DC/DC converter topologies. The results of the simulations run in Matlab-Simulink show that the suggested buck converter is efficient and a better option for use in electrical vehicle applications. Due to its lower emissions and increased fuel efficiency, fuel cell-powered hybrid electric vehicles (HEV) are being developed by many automobile firms. Power electronics is the important technology for the development of fuel cells for propulsion. This document details various DC/DC converter topologies that are employed to link HEV motor controllers to fuel cells. The objective is to offer a straightforward and useful boost converter topology with a coordinated control that can simultaneously control both the output voltage and input current. The outcomes of simulations run under various dynamics are used to assess the buck converter's performance.
PL
Pojazdy hybrydowe z ogniwami paliwowymi to pojazdy elektryczne z technologiami konwersji energii, które łączą ogniwa paliwowe i akumulatory. System zarządzania energią ma kluczowe znaczenie dla działania hybrydowego systemu ogniw paliwowych, ponieważ zmniejsza zużycie wodoru przez system. W badaniu tym zbadano, jak najlepiej zarządzać topologiami połączonych przetwornic DC/DC pojazdów hybrydowych z ogniwami paliwowymi. Wyniki symulacji przeprowadzonych w programie Matlab-Simulink pokazują, że proponowana przetwornica jest wydajna i lepiej nadaje się do zastosowań w pojazdach elektrycznych. Ze względu na niższą emisję i zwiększoną oszczędność paliwa, wiele firm motoryzacyjnych opracowuje hybrydowe pojazdy elektryczne (HEV) napędzane ogniwami paliwowymi. Energoelektronika jest ważną technologią dla rozwoju ogniw paliwowych do napędu. W tym dokumencie wyszczególniono różne topologie przetwornic DC/DC, które są wykorzystywane do łączenia sterowników silników HEV z ogniwami paliwowymi. Celem jest zaoferowanie prostej i użytecznej topologii przetwornicy podwyższającej napięcie ze skoordynowanym sterowaniem, które może jednocześnie kontrolować zarówno napięcie wyjściowe, jak i prąd wejściowy. Wyniki symulacji przeprowadzonych przy różnej dynamice służą do oceny działania przetwornicy buck.
Rocznik
Strony
104--107
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
  • College of Computer Science and Engineering, University of Haʼil, Haʼil
  • SIME Laboratory, ENSIT, University of Tunis
Bibliografia
  • [1] Yu, P.; Li, M.; Wang, Y.; Chen, Z. Fuel Cell Hybrid Electric Vehicles: A Review of Topologies and Energy Management Strategies. World Electr. Veh. J. 2022, 13, 172.
  • [2] Wang, Y.; Wang, L.; Li, M.; Chen, Z. A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems. Etransportation 2020, 4, 100064.
  • [3] Badji, A.; Abdeslam, D.O.; Chabane, D.; Benamrouche, N. Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations. Energy 2022, 249, 123743.
  • [4] 37. Lu, X.Q.; Wu, Y.B.; Lian, J.; Zhang, Y.Y.; Chen, C.; Wang, P.S.; Meng, L.Z. Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Convers. Manag. 2020, 205, 112474.
  • [5] Anbarasu, A.; Dinh, T.Q.; Sengupta, S. Novel enhancement of energy management in fuel cell hybrid electric vehicle by an advanced dynamic model predictive control. Energy Convers. Manag. 2022, 267, 115883.
  • [6] Fu, J.; Zeng, L.; Lei, J.; Deng, Z.; Fu, X.; Li, X.; Wang, Y. A Real-Time Load Prediction Control for Fuel Cell Hybrid Vehicle. Energies 2022, 15, 3700.
  • [7] Zhou, Y.; Ravey, A.; Pera, M.C. Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor. Energy Convers. Manag. 2020, 213, 112821.
  • [8] Yuan, J.; Yang, L.; Chen, Q. Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles. Int. J. Hydrogen Energy 2018, 43, 8063–8078.
  • [9] He, H.; Wang, X.; Chen, J.; Wang, Y.-X. Regenerative Fuel Cell-Battery-Supercapacitor Hybrid Power System Modeling and Improved Rule-Based Energy Management for Vehicle Application. J. Energy Eng. 2020, 146, 04020060.
  • [10] Du, C.; Huang, S.; Jiang, Y.; Wu, D.; Li, Y. Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming. Energies 2022, 15, 4325.
  • [11] Wang, Y.J.; Sun, Z.D.; Li, X.Y.; Yang, X.Y.; Chen, Z.H. A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems. Energy 2019, 189, 116142.
  • [12] Wang, Y.J.; Sun, Z.D.; Chen, Z.H. Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine. Appl. Energy 2019, 254, 113707.
  • [13] Zhang, H.T.; Li, X.G.; Liu, X.Z.; Yan, J.Y. Enhancing fuel cell durability for fuel cell plug-in hybrid electric vehicles through strategic power management. Appl. Energy 2019, 241, 483– 490.
  • [14] Alcazar-Garcia, D.; Martinez, J.L.R. Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles. Energy 2022, 254, 123719.
  • [15] He, H.; Jia, C.; Li, J. A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery healthaware control. Int. J. Hydrogen Energy 2022, 47, 22147– 22164.
  • [16] Farhadi Gharibeh, H.; Farrokhifar, M. Online Multi-Level Energy Management Strategy Based on Rule-Based and OptimizationBased Approaches for Fuel Cell Hybrid Electric Vehicles. Appl. Sci. 2021, 11, 3849.
  • [17] Liu, Y.; Liu, J.; Qin, D.; Li, G.; Chen, Z.; Zhang, Y. Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning. J. Clean. Prod. 2020, 260, 121017.
  • [18] Chuanlong, J.; Liang, Q.; Zhiqiang, Z.; Yan, Z. Research on Energy Management Strategy of Vehicle Fuel Cell-Battery Hybrid Energy System Based on GT-SUIT/Simulink. J. Phys. Conf. Ser. 2021, 1885, 042067.
  • [19] Pisal, P.S.; Vidyarthi, D.A. An optimal control for power management in super capacitors/battery of electric vehicles using Deep Neural Network. J. Power Sources 2022, 542, 231696.
  • [20] Li, S.Q.; He, H.W.; Zhao, P.F. Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective. Energy 2021, 230, 120890.
  • [21] Yue, M.; Jemei, S.; Zerhouni, N. Health-Conscious Energy Management for Fuel Cell Hybrid Electric Vehicles Based on PrognosticsEnabled Decision-Making. IEEE Trans. Veh. Technol. 2019, 68, 11483–11491.
  • [22] Huangfu, Y.; Li, P.; Pang, S.; Tian, C.; Quan, S.; Zhang, Y.; Wei, J. An Improved Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Pontryagin’s Minimum Principle. IEEE Trans. Ind. Appl. 2022, 58, 4086–4097.
  • [23] Mounica, V.; Obulesu, Y.P. Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application. Energies 2022, 15, 4185.
  • [24] Yue, M.L.; Al Masry, Z.; Jemei, S.; Zerhouni, N. An online prognostics-based health management strategy for fuel cell hybrid electric vehicles. Int. J. Hydrogen Energy 2021, 46, 13206–13218.
  • [25] Zhou, Y.; Ravey, A.; Pera, M.C. Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer. Appl. Energy 2020, 258, 114057.
  • [26] Iqbal, M.; Laurent, J.; Benmouna, A.; Becherif, M.; Ramadan, H.S.; Claude, F. Ageing-aware load following control for compositecost optimal energy management of fuel cell hybrid electric vehicle. Energy 2022, 254, 124233.
  • [27] Zhang, Y.J.; Huang, Y.J.; Chen, Z.; Li, G.; Liu, Y.G. A Novel Learning-Based Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle. IEEE Trans. Transp. Electrif. 2022, 8, 23–35.
  • [28] Ghaderi, R.; Kandidayeni, M.; Soleymani, M.; Boulon, L.; Trovao, J.P.F. Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory. IEEE Trans. Veh. Technol. 2022, 71, 5704– 5714.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4af39b9-f040-4ac2-b68b-db64beedc3d0
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