Driving-Behavior-Aware Optimal Energy Management Strategy for Multi-Source Fuel Cell Hybrid Electric Vehicles Based on Adaptive Soft Deep-Reinforcement Learning

Tao, Fazhan; Fu, Zhumu; Gao, Aiyun; Sun, Haochen

doi:10.1109/tits.2022.3233564

Cited by 23 publications

(10 citation statements)

References 50 publications

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“…In addition, some papers propose combined EMMs to further improve fuel economy while ensuring FC durability, such as DP-ECMS [107], the rule-based fuzzy control method [108], adaptive neuro-fuzzy inference system-ECMS (ANFIS-ECMS) [109], and MPC-PMP [110]. As a new research hotspot in the field of artificial intelligence (AI) and internet of vehicles (IOV), learning-based and cycle information-based EMMs have been applied to achieve the optimal fuel economy of FCVs in real time [63,111]. Progress, challenges, and potential solutions of learning-based EMMs for FCVs have been reviewed in detail in [112][113][114] and will not be further elaborated here.…”

Section: Overview Of Energy Management Methods For Fcvsmentioning

confidence: 99%

“…Progress, challenges, and potential solutions of learning-based EMMs for FCVs have been reviewed in detail in [112][113][114] and will not be further elaborated here. More importantly, considering the importance of driving cycle information in the design and development of EMMs for FCVs, the following summarizes the existing papers from two perspectives: driving pattern As a new research hotspot in the field of artificial intelligence (AI) and internet of vehicles (IOV), learning-based and cycle information-based EMMs have been applied to achieve the optimal fuel economy of FCVs in real time [63,111]. Progress, challenges, and potential solutions of learning-based EMMs for FCVs have been reviewed in detail in [112][113][114] and will not be further elaborated here.…”

Section: Overview Of Energy Management Methods For Fcvsmentioning

confidence: 99%

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Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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Section: Overview Of Energy Management Methods For Fcvsmentioning

confidence: 99%

Section: Overview Of Energy Management Methods For Fcvsmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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“…Examples of Reinforcement learning are proposed by [28] and [37]. RL has been applied to EV in literature also to integrate driving behavior in the Energy Management System (EMS) as in [44] and to manage the forecasted charging load in vehicle-based mobility-on-demand systems [45] or [46]. Prophet-BiLSTM is employed in the day-ahead forecast of EV charging load in [47], it performed better than transformer and DNN.…”

Section: Related Workmentioning

confidence: 99%

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Matrone,

Ogliari,

Nespoli

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Transports is one of the sectors that produce the highest emissions of CO 2 ; in the last ten years, there has been a process of decarbonization which has led to a considerable increase in Electric Vehicles (EVs). However, the sudden introduction of a large number of Electric vehicle supply equipment (EVSE) supplying electrical energy to EVs could cause problems in the management of the electric grid which must cope with the consequent increase in the electrical load demand. In this context, the 24 hour ahead forecast of the power curve associated with the recharge of EVs becomes of vital importance to ensure the reliability of the electric grid. In this paper, different Machine Learning models based on Recurrent Neural Networks (LSTM, GRU) and with different architectures, are compared based on their capability to accurately predict the power curve of an EV charging station one day in advance. A Sequence to Sequence model has been implemented and a thorough analysis of an Attention layer has been detailed. The models are tested on a real world open dataset.

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“…Deng [ 9 ] proposed an online estimation model for fuel cell aging based on operation in four distinct modes, and developed an EMS through advanced deep reinforcement learning. Sun [ 10 ] introduced the influence of driving behavior into the EMS and proposed an adaptive deep reinforcement learning system. Although highly adaptable, learning‐based strategies rely on large databases, require significant resources during the learning process, and may result in locally optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

Qin,

He,

Liu

et al. 2024

Energy Tech

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The high operating costs limit the popularity and development of fuel cell buses (FCBs). To enhance the lifecycle operating economy of FCBs, this article analyzes the impact of power sources degradation on operating costs and proposes an online operating costs minimization strategy, which integrates online state of health (SOH) estimation, online multiobjective optimization, and online adaptive adjustment into the control strategy framework. Specifically, the SOH of the power sources is estimated by cycle life models in real time. The hydrogen consumption, battery energy consumption, and power sources degradation are incorporated into the objective function and adjusted in real time according to the SOH. Under different SOH, the simulation results indicate that compared with the equivalent consumption minimization strategy, the proposed strategy can reduce fuel cell and battery degradation by 13.8% and 5%, and decrease operating costs by 5.3%. Moreover, the adaptability of the control strategy is enhanced throughout the full lifecycle.

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Driving-Behavior-Aware Optimal Energy Management Strategy for Multi-Source Fuel Cell Hybrid Electric Vehicles Based on Adaptive Soft Deep-Reinforcement Learning

Cited by 23 publications

References 50 publications

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

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