Energy management strategy for hybrid electric vehicles based on deep reinforcement learning with consideration of electric drive system thermal characteristics

Energy Conversion and Management, Год журнала: 2025, Номер 332, С. 119697 - 119697

Опубликована: Март 6, 2025

Язык: Английский

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Aging-aware real-time multi-layer co-optimization approach for hybrid vehicles: across configuration, parameters, and control

Energy Conversion and Management, Год журнала: 2025, Номер 332, С. 119748 - 119748

Опубликована: Март 25, 2025

Язык: Английский

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Collaborative optimization of velocity planning and energy management for fuel cell hybrid buses at multiple intersections and bus stations

Energy, Год журнала: 2025, Номер unknown, С. 135912 - 135912

Опубликована: Апрель 1, 2025

Язык: Английский

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Multi-perspective evaluation of a novel powertrain integrating series-parallel and power-split modes: An ultra-rapid hierarchical control approach

Mechanism and Machine Theory, Год журнала: 2025, Номер 210, С. 106034 - 106034

Опубликована: Апрель 14, 2025

Язык: Английский

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Energy management strategy for fuel cell hybrid ships based on deep reinforcement learning with multi-optimization objectives

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 93, С. 1258 - 1267

Опубликована: Ноя. 11, 2024

Язык: Английский

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A Fuzzy Logic Control-Based Approach for Real-Time Energy Management of the Fuel Cell Electrical Bus Considering the Durability of the Fuel Cell System

World Electric Vehicle Journal, Год журнала: 2024, Номер 15(3), С. 92 - 92

Опубликована: Март 1, 2024

The present study proposes a fuzzy logical control-based real-time energy management strategy (EMS) for fuel cell electrical bus (FCEB), taking into account the durability of system (FCS), in order to enhance both vehicle’s economic performance and FCS’s service life. At first, model FCEB is established whilst power-following also formulated as benchmark evaluation proposed strategy. Subsequently, controller designed improve work efficiency FCS, which battery state-of-charge (SOC) desired power are considered inputs, FCS output. Then, limitation method integrated restrict change rate strengthen last, accessed based on China city driving cycle (CCBC). results indicate that can satisfy dynamic well. Importantly, it has remarkable effectiveness terms promoting FCEB’s economy. Despite slight reduction contrast control, improvements still acceptable. be confined ±10 kW. Meanwhile, promotion reach up 8.43%, 7.69%, 6.53% consideration under different SOCs. This will significantly benefit saving durability.

Язык: Английский

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Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Energy, Год журнала: 2024, Номер 304, С. 132144 - 132144

Опубликована: Июнь 19, 2024

Язык: Английский

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A Comprehensive Review on Energy Management Strategies for Fuel‐Cell‐Based Electric Vehicles

Energy Technology, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

The rapid growth of the transportation sector in past few decades has contributed significantly to global warming issues, leading extensive research on vehicles having nearly zero or total tailpipe carbon emissions. automobiles within this classification belong hybrid electrical (HEVs), plug‐in HEVs, battery–electric (BEVs), fuel‐cell (FC) EVs (FCEVs), and FC HEVs. FCHEVs are powered by a combination systems, rechargeable batteries, ultracapacitors, and/or mechanical flywheels. technology appears hold potential terms extended driving distances quicker refueling times for that emit no exhaust fumes. A significant number studies have examined various types energy‐storage devices as vehicle power supply, their interfacing with drive mechanism using converters energy management strategies (EMS). In article, EMS FC‐based discussed. Classifications FCEVs, BEVs, EMSs developed researchers. review report, it is indicated existing capable performing well, yet further required better reliability intelligence toward achieving greater fuel efficiency lifetime upcoming FCHEVs.

Язык: Английский

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An Adaptive Power Management Using a Single Point Of Preview Predictive Model of Reference SOC for Plug-In Hybrid Electric Buses in Consideration of the Full Life Circle of the Battery

Опубликована: Янв. 1, 2024

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Язык: Английский

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A Comparative Study of Energy Management Strategies for Fuel Cell Hybrid Vehicles Based on Deep Reinforcement Learning

Опубликована: Янв. 1, 2024

The energy management strategy (EMS) is the top priority to ensure safe and efficient operation of fuel cell hybrid vehicles. Nowadays, EMS based on deep reinforcement learning (DRL) has become a research hotspot. However, there lack unified comparison benchmark for DRL-based EMSs. Most EMSs not discussed impact algorithm hyperparameters, provided comprehensive evaluation indicators including cost, aging efficiency. In this paper, five different DRL methods are designed, multi-objective reward function that integrates equivalent hydrogen consumption, degradation, battery state-of-charge fluctuation its working range designed. First, hyperparameters determined convergence performance in training process. weight coefficients average consumption. Then above-mentioned compared horizontally. Finally, six driving conditions used as test sets explore adaptability results show can be effectively applied real-time environments, algorithms applications, which provide valid guidance researchers use EMS.

Язык: Английский

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