Comprehensive optimization of fuzzy logic-based energy management system for fuel-cell hybrid electric vehicle using genetic algorithm

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 81, С. 889 - 905

Опубликована: Июль 26, 2024

Язык: Английский

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Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control

Energy Conversion and Management, Год журнала: 2024, Номер 321, С. 119032 - 119032

Опубликована: Сен. 11, 2024

Язык: Английский

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Energy management strategy with mutation protection for fuel cell electric vehicles

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 63, С. 48 - 58

Опубликована: Март 18, 2024

Язык: Английский

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Remaining useful life prediction for vehicle-oriented PEMFCs based on organic gray neural network considering the influence of dual energy source synergy

Energy, Год журнала: 2025, Номер unknown, С. 135578 - 135578

Опубликована: Март 1, 2025

Язык: Английский

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A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management

Control Engineering Practice, Год журнала: 2024, Номер 153, С. 106104 - 106104

Опубликована: Сен. 25, 2024

Язык: Английский

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Hierarchical energy management for extended-range electric vehicles considering range extender dynamic coordination

Journal of Power Sources, Год журнала: 2024, Номер 622, С. 235349 - 235349

Опубликована: Сен. 5, 2024

Язык: Английский

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Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

Energy Conversion and Management, Год журнала: 2024, Номер 321, С. 119039 - 119039

Опубликована: Сен. 11, 2024

Язык: Английский

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Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Energy and AI, Год журнала: 2024, Номер 17, С. 100392 - 100392

Опубликована: Июль 14, 2024

Trucks consume a lot of energy. Hybrid technology maintains long range while realizing energy savings. is therefore an effective energy-saving for trucks. Recovery engine waste heat through the organic Rankine cycle further enhances efficiency and provides thermal management. However, powertrain greatly increases complexity management system. In order to design system with high robustness, this study proposes deep reinforcement learning embedded rule-based This method optimises key parameters by inserting into it. Therefore, scheme combines good optimization effect excellent robustness rule. verify feasibility scheme, builds dynamic model carries out simulation study. Subsequently, hybrid semi physical experimental bench was constructed rapid control prototype carried out. The results show that can reduce consumption 4.31 % compared under C-WTVC driving cycle. addition, saving safe operation also be achieved other unfamiliar untrained cycles. shows has agreement in experiment simulation, which demonstrates potential real vehicle engineering applications promotes application learning.

Язык: Английский

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Comparison of deep reinforcement learning-based energy management strategies for fuel cell vehicles considering economics, durability and adaptability

Energy, Год журнала: 2024, Номер 307, С. 132771 - 132771

Опубликована: Авг. 10, 2024

Язык: Английский

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Thermal management of fuel cell-battery electric vehicles: Challenges and solutions

Applied Energy, Год журнала: 2025, Номер 387, С. 125635 - 125635

Опубликована: Март 5, 2025

Язык: Английский

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