Research Article
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Year 2025, Volume: 9 Issue: 2, 276 - 283, 30.06.2025
https://doi.org/10.30939/ijastech..1691411

Abstract

References

  • [1] Ou S, Lin Z, Manente V, Bouchard J, He X, Lu Z, et al. Light-Duty Vehicle Transportation Policy and Implication on Greenhouse Gas Emissions. ACS Symposium Series 2022;1412:21–81. https://doi.org/10.1021/bk-2022-1412.ch002.
  • [2] Tu DT. A Novel Concept of Hybrid Electric Public Bus With Power Management System in Vietnam’s Condition. Journal of Advanced Manufacturing Technology 2023;17.
  • [3] Khadhraoui A, Selmi T, Cherif A. Energy Management of a Hybrid Electric Vehicle. Engineering, Technology and Applied Science Research 2022;12:8916–21. https://doi.org/10.48084/etasr.5058.
  • [4] Ma S, Lin M, Lin TE, Lan T, Liao X, Maréchal F, et al. Fuel cell-battery hybrid systems for mobility and off-grid applications: A review. Renewable and Sustainable Energy Reviews 2021;135. https://doi.org/10.1016/j.rser.2020.110119.
  • [5] Sorlei IS, Bizon N, Thounthong P, Varlam M, Carcadea E, Culcer M, et al. Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies. Energies 2021;14:1–29. https://doi.org/10.3390/en14010252.
  • [6] İnci M, Büyük M, Demir MH, İlbey G. A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects. Renewable and Sustainable Energy Reviews 2021;137. https://doi.org/10.1016/j.rser.2020.110648.
  • [7] Barhate SS, Mudhalwadkar R, Madhe S. Fault Detection Methods Suitable for Automotive Applications in Proton Exchange Fuel Cells. Engineering, Technology and Applied Science Research 2022;12:9607–13. https://doi.org/10.48084/etasr.5262.
  • [8] Al-Ani MAJ, Zdiri MA, Salem F Ben, Derbel N. Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems. Engineering, Technology and Applied Science Research 2024;14:13929–36. https://doi.org/10.48084/etasr.6936.
  • [9] Pramuanjaroenkij A, Kakaç S. The fuel cell electric vehicles: The highlight review. International Journal of Hydrogen Energy. 2023;48(25):9401-25. https://doi.org/10.1016/j.ijhydene.2022.11.103
  • [10] Tanç B, Arat HT, Conker Ç, Baltacioğlu E, Aydin K. Energy distribution analyses of an additional traction battery on hydrogen fuel cell hybrid electric vehicle. International Journal of Hydrogen Energy 2020;45:26344–56. https://doi.org/10.1016/j.ijhydene.2019.09.241.
  • [11] Zhou Y, Ravey A, Péra MC. Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor. Energy Conversion and Management 2020;213:112821. https://doi.org/10.1016/j.enconman.2020.112821.
  • [12] Wu X, Hu X, Yin X, Li L, Zeng Z, Pickert V. Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle. Journal of Power Sources 2019;423:358–66. https://doi.org/10.1016/j.jpowsour.2019.03.044.
  • [13] Xu S, Tian X, Wang C, Qin Y, Lin X, Zhu J, et al. A Novel Coordinated Control Strategy for Parallel Hybrid Electric Vehicles during Clutch Slipping Process. Applied Sciences (Switzerland) 2022;12. https://doi.org/10.3390/app12168317.
  • [14] Zhou W, Yang L, Cai Y, Ying T. Dynamic programming for New Energy Vehicles based on their work modes part I: Electric Vehicles and Hybrid Electric Vehicles. Journal of Power Sources 2018;406:151–66. https://doi.org/10.1016/j.jpowsour.2018.10.047.
  • [15] Zhou W, Yang L, Cai Y, Ying T. Dynamic programming for new energy vehicles based on their work modes Part II: Fuel cell electric vehicles. Journal of Power Sources 2018;407:92–104. https://doi.org/10.1016/j.jpowsour.2018.10.048.
  • [16] Zeng T, Zhang C, Zhang Y, Deng C, Hao D, Zhu Z, et al. Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle. Energy 2021;227:120305. https://doi.org/10.1016/j.energy.2021.120305.
  • [17] 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;15:1–25. https://doi.org/10.3390/en15124325.
  • [18] Zhou Y, Ravey, A, Péra MC. Predictive energy management for fuel cell hybrid electric vehicle. Intelligent Control and Smart Energy Management. 2022;181:1-44. https://doi.org/10.1007/978-3-030-84474-5_1
  • [19] Deng K, Peng H, Dirkes S, Gottschalk J, Ünlübayir C, Thul A, et al. An adaptive PMP-based model predictive energy management strategy for fuel cell hybrid railway vehicles. ETransportation 2021;7. https://doi.org/10.1016/j.etran.2020.100094.
  • [20] Naunin D. Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles. Energies Article 2022. https://doi.org/https://doi.org/10.3390/en15041318.
  • [21] Lin X, Wang Z, Zeng S, Huang W, Li X. Real-time optimization strategy by using sequence quadratic programming with multivariate nonlinear regression for a fuel cell electric vehicle. International Journal of Hydrogen Energy 2021;46:13240–51. https://doi.org/10.1016/j.ijhydene.2021.01.125.
  • [22] Li Q, Wang T, Li S, Chen W, Liu H, Breaz E, et al. Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation. Applied Energy 2021;285. https://doi.org/10.1016/j.apenergy.2021.116505.
  • [23] Han L, Yang K, Ma T, Yang N, Liu H, Guo L. Battery life constrained real-time energy management strategy for hybrid electric vehicles based on reinforcement learning. Energy 2022;259:124986. https://doi.org/10.1016/j.energy.2022.124986.
  • [24] Oladosu TL, Pasupuleti J, Kiong TS, Koh SPJ, Yusaf T. Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review. International Journal of Hydrogen Energy 2024;61:1380–404. https://doi.org/10.1016/j.ijhydene.2024.02.284.
  • [25] Lee S, Seon J, Hwang B, Kim S, Sun Y, Kim J. Recent Trends and Issues of Energy Management Systems Using Machine Learning. Energies 2024;17. https://doi.org/10.3390/en17030624.
  • [26] Rajesh, Vijayakumari A. Hybrid Energy Storage System for Electric Vehicle Using Battery and Ultracapacitor. In: Sengodan T, Murugappan M, Misra S, editors. Advances in Electrical and Computer Technologies, Singapore: Springer Singapore; 2020;1203–14. https://doi.org/10.1007/978-981-15-5558-9_102
  • [27] Silva LCA, Eckert JJ, Lourenço MAM, Silva FL, Corrêa FC, Dedini FG. Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2021;43:259. https://doi.org/10.1007/s40430-021-02975-w.
  • [28] Wangsupphaphol A, Phichaisawat S, Nik Idris NR, Jusoh A, Muhamad ND, Lengkayan R. A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability 2023;15:11200. https://doi.org/10.3390/su151411200.
  • [29] Camaraza-Medina Y, Sánchez Escalona AA, Miguel Cruz-Fonticiella O, García-Morales OF. Method for heat transfer calculation on fluid flow in single-phase inside rough pipes. Thermal Science and Engineering Progress 2019;14:100436. https://doi.org/10.1016/j.tsep.2019.100436.
  • [30] Shaari N, Kamarudin SK. Recent advances in additive-enhanced polymer electrolyte membrane properties in fuel cell applications: An overview. International Journal of Energy Research 2019;43:2756–94. https://doi.org/10.1002/er.4348.
  • [31] Olabi AG, Wilberforce T, Abdelkareem MA. Fuel cell application in the automotive industry and future perspective. Energy 2021;214:118955. https://doi.org/10.1016/j.energy.2020.118955.
  • [32] Wong CY, Wong WY, Ramya K, Khalid M, Loh KS, Daud WRW, et al. Additives in proton exchange membranes for low- and high-temperature fuel cell applications: A review. International Journal of Hydrogen Energy 2019;44:6116–35. https://doi.org/10.1016/j.ijhydene.2019.01.084.
  • [33] Solmaz H, Kocakulak T. Determination of Lithium Ion Battery Characteristics for Hybrid Vehicle Models. International Journal of Automotive Science and Technology 2020;4:264–71. https://doi.org/10.30939/ijastech..723043.
  • [34] Bellman R. The Theory of Dynamic Programming. Bulletin of the American Mathematical Society 1954;60:503–15. https://doi.org/10.1090/S0002-9904-1954-09848-8.

Global Optimization of Hysteresis Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles

Year 2025, Volume: 9 Issue: 2, 276 - 283, 30.06.2025
https://doi.org/10.30939/ijastech..1691411

Abstract

Fuel Cell Hybrid Electric Vehicles (FCHEVs) represent a new generation of environmentally friendly transportation technologies and have garnered significant global attention due to their potential to reduce emissions and reliance on fossil fuels. One of the critical challenges in FCHEV development lies in the design and optimization of the energy management strategy (EMS), which plays a pivotal role in determining how energy is distributed among the various power sources to maximize vehicle performance, minimize fuel consumption, and prolong system longevity, all while adhering to operational constraints. This study focuses on evaluating and optimizing EMS configurations within two distinct powertrain architectures. The first configuration, referred to as FCB, consists of a Fuel Cell System (FCS) coupled with a high-capacity battery. The second, more advanced configuration—termed FCBUC—integrates an ultracapacitor alongside the FCS and battery to enhance responsiveness and energy efficiency. Both systems were modeled and simulated using a hysteresis-based EMS, which governs the switching logic between power sources based on state-of-charge (SOC) thresholds and power demand fluctuations. To further enhance performance, a global optimization technique was employed to fine-tune key control parameters, ensuring that the system operated near optimal efficiency throughout a realistic urban driving cycle, specifically modeled after conditions in Vietnam. The results demonstrate that the proposed EMSs significantly improve system behavior by efficiently managing power flow and reducing hydrogen fuel consumption. Notably, the FCBUC configuration exhibited superior energy distribution capability and fuel economy by 11.7% reduction in hydrogen consumption and improved efficiency (59.07% avg. for FCBUC) compared to the FCB model. This study highlights the importance of advanced EMS design and powertrain configuration in realizing the full potential of FCHEV technologies in real-world urban environments.

References

  • [1] Ou S, Lin Z, Manente V, Bouchard J, He X, Lu Z, et al. Light-Duty Vehicle Transportation Policy and Implication on Greenhouse Gas Emissions. ACS Symposium Series 2022;1412:21–81. https://doi.org/10.1021/bk-2022-1412.ch002.
  • [2] Tu DT. A Novel Concept of Hybrid Electric Public Bus With Power Management System in Vietnam’s Condition. Journal of Advanced Manufacturing Technology 2023;17.
  • [3] Khadhraoui A, Selmi T, Cherif A. Energy Management of a Hybrid Electric Vehicle. Engineering, Technology and Applied Science Research 2022;12:8916–21. https://doi.org/10.48084/etasr.5058.
  • [4] Ma S, Lin M, Lin TE, Lan T, Liao X, Maréchal F, et al. Fuel cell-battery hybrid systems for mobility and off-grid applications: A review. Renewable and Sustainable Energy Reviews 2021;135. https://doi.org/10.1016/j.rser.2020.110119.
  • [5] Sorlei IS, Bizon N, Thounthong P, Varlam M, Carcadea E, Culcer M, et al. Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies. Energies 2021;14:1–29. https://doi.org/10.3390/en14010252.
  • [6] İnci M, Büyük M, Demir MH, İlbey G. A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects. Renewable and Sustainable Energy Reviews 2021;137. https://doi.org/10.1016/j.rser.2020.110648.
  • [7] Barhate SS, Mudhalwadkar R, Madhe S. Fault Detection Methods Suitable for Automotive Applications in Proton Exchange Fuel Cells. Engineering, Technology and Applied Science Research 2022;12:9607–13. https://doi.org/10.48084/etasr.5262.
  • [8] Al-Ani MAJ, Zdiri MA, Salem F Ben, Derbel N. Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems. Engineering, Technology and Applied Science Research 2024;14:13929–36. https://doi.org/10.48084/etasr.6936.
  • [9] Pramuanjaroenkij A, Kakaç S. The fuel cell electric vehicles: The highlight review. International Journal of Hydrogen Energy. 2023;48(25):9401-25. https://doi.org/10.1016/j.ijhydene.2022.11.103
  • [10] Tanç B, Arat HT, Conker Ç, Baltacioğlu E, Aydin K. Energy distribution analyses of an additional traction battery on hydrogen fuel cell hybrid electric vehicle. International Journal of Hydrogen Energy 2020;45:26344–56. https://doi.org/10.1016/j.ijhydene.2019.09.241.
  • [11] Zhou Y, Ravey A, Péra MC. Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor. Energy Conversion and Management 2020;213:112821. https://doi.org/10.1016/j.enconman.2020.112821.
  • [12] Wu X, Hu X, Yin X, Li L, Zeng Z, Pickert V. Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle. Journal of Power Sources 2019;423:358–66. https://doi.org/10.1016/j.jpowsour.2019.03.044.
  • [13] Xu S, Tian X, Wang C, Qin Y, Lin X, Zhu J, et al. A Novel Coordinated Control Strategy for Parallel Hybrid Electric Vehicles during Clutch Slipping Process. Applied Sciences (Switzerland) 2022;12. https://doi.org/10.3390/app12168317.
  • [14] Zhou W, Yang L, Cai Y, Ying T. Dynamic programming for New Energy Vehicles based on their work modes part I: Electric Vehicles and Hybrid Electric Vehicles. Journal of Power Sources 2018;406:151–66. https://doi.org/10.1016/j.jpowsour.2018.10.047.
  • [15] Zhou W, Yang L, Cai Y, Ying T. Dynamic programming for new energy vehicles based on their work modes Part II: Fuel cell electric vehicles. Journal of Power Sources 2018;407:92–104. https://doi.org/10.1016/j.jpowsour.2018.10.048.
  • [16] Zeng T, Zhang C, Zhang Y, Deng C, Hao D, Zhu Z, et al. Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle. Energy 2021;227:120305. https://doi.org/10.1016/j.energy.2021.120305.
  • [17] 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;15:1–25. https://doi.org/10.3390/en15124325.
  • [18] Zhou Y, Ravey, A, Péra MC. Predictive energy management for fuel cell hybrid electric vehicle. Intelligent Control and Smart Energy Management. 2022;181:1-44. https://doi.org/10.1007/978-3-030-84474-5_1
  • [19] Deng K, Peng H, Dirkes S, Gottschalk J, Ünlübayir C, Thul A, et al. An adaptive PMP-based model predictive energy management strategy for fuel cell hybrid railway vehicles. ETransportation 2021;7. https://doi.org/10.1016/j.etran.2020.100094.
  • [20] Naunin D. Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles. Energies Article 2022. https://doi.org/https://doi.org/10.3390/en15041318.
  • [21] Lin X, Wang Z, Zeng S, Huang W, Li X. Real-time optimization strategy by using sequence quadratic programming with multivariate nonlinear regression for a fuel cell electric vehicle. International Journal of Hydrogen Energy 2021;46:13240–51. https://doi.org/10.1016/j.ijhydene.2021.01.125.
  • [22] Li Q, Wang T, Li S, Chen W, Liu H, Breaz E, et al. Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation. Applied Energy 2021;285. https://doi.org/10.1016/j.apenergy.2021.116505.
  • [23] Han L, Yang K, Ma T, Yang N, Liu H, Guo L. Battery life constrained real-time energy management strategy for hybrid electric vehicles based on reinforcement learning. Energy 2022;259:124986. https://doi.org/10.1016/j.energy.2022.124986.
  • [24] Oladosu TL, Pasupuleti J, Kiong TS, Koh SPJ, Yusaf T. Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review. International Journal of Hydrogen Energy 2024;61:1380–404. https://doi.org/10.1016/j.ijhydene.2024.02.284.
  • [25] Lee S, Seon J, Hwang B, Kim S, Sun Y, Kim J. Recent Trends and Issues of Energy Management Systems Using Machine Learning. Energies 2024;17. https://doi.org/10.3390/en17030624.
  • [26] Rajesh, Vijayakumari A. Hybrid Energy Storage System for Electric Vehicle Using Battery and Ultracapacitor. In: Sengodan T, Murugappan M, Misra S, editors. Advances in Electrical and Computer Technologies, Singapore: Springer Singapore; 2020;1203–14. https://doi.org/10.1007/978-981-15-5558-9_102
  • [27] Silva LCA, Eckert JJ, Lourenço MAM, Silva FL, Corrêa FC, Dedini FG. Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2021;43:259. https://doi.org/10.1007/s40430-021-02975-w.
  • [28] Wangsupphaphol A, Phichaisawat S, Nik Idris NR, Jusoh A, Muhamad ND, Lengkayan R. A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability 2023;15:11200. https://doi.org/10.3390/su151411200.
  • [29] Camaraza-Medina Y, Sánchez Escalona AA, Miguel Cruz-Fonticiella O, García-Morales OF. Method for heat transfer calculation on fluid flow in single-phase inside rough pipes. Thermal Science and Engineering Progress 2019;14:100436. https://doi.org/10.1016/j.tsep.2019.100436.
  • [30] Shaari N, Kamarudin SK. Recent advances in additive-enhanced polymer electrolyte membrane properties in fuel cell applications: An overview. International Journal of Energy Research 2019;43:2756–94. https://doi.org/10.1002/er.4348.
  • [31] Olabi AG, Wilberforce T, Abdelkareem MA. Fuel cell application in the automotive industry and future perspective. Energy 2021;214:118955. https://doi.org/10.1016/j.energy.2020.118955.
  • [32] Wong CY, Wong WY, Ramya K, Khalid M, Loh KS, Daud WRW, et al. Additives in proton exchange membranes for low- and high-temperature fuel cell applications: A review. International Journal of Hydrogen Energy 2019;44:6116–35. https://doi.org/10.1016/j.ijhydene.2019.01.084.
  • [33] Solmaz H, Kocakulak T. Determination of Lithium Ion Battery Characteristics for Hybrid Vehicle Models. International Journal of Automotive Science and Technology 2020;4:264–71. https://doi.org/10.30939/ijastech..723043.
  • [34] Bellman R. The Theory of Dynamic Programming. Bulletin of the American Mathematical Society 1954;60:503–15. https://doi.org/10.1090/S0002-9904-1954-09848-8.
There are 34 citations in total.

Details

Primary Language English
Subjects Hybrid and Electric Vehicles and Powertrains, Automotive Engineering (Other)
Journal Section Research Article
Authors

Trong Tu Do 0000-0003-2071-0168

Submission Date May 4, 2025
Acceptance Date June 20, 2025
Publication Date June 30, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

Vancouver Do TT. Global Optimization of Hysteresis Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles. IJASTECH. 2025;9(2):276-83.


International Journal of Automotive Science and Technology (IJASTECH) is published by Society of Automotive Engineers Turkey

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