Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy

Hou, Zhuoran; Guo, Jianhua; Li, Jihao; Hu, Jincheng; Sun, Wen; Zhang, Yuanjian

doi:10.1016/j.energy.2023.127081

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…As is shown in Table II across the various predictive lengths compared to the contemporary state-of-the-art models. Despite the slower inference speed of the DL-STFEE model compared to the machine learning method, its inference speed, which achieves more than 50Hz inference frequency, can still meet the requirement for online implementation such as vehicleenvironment cooperation in the onboard vehicle control unit [40] or mobile edge computing devices [41]. The relevant studies demonstrate that the real-time application of vehicleenvironment cooperation requires a control frequency larger than 50Hz [42,43].…”

Section: Experiments 2: Comparison With Baselinesmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.

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Section: Experiments 2: Comparison With Baselinesmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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“…To comprehensively estimate the economic performance of the studied vehicle, the total equivalent fuel consumption [32], comprising both the actual fuel consumption and the equivalent fuel consumption derived from electric consumption using an equivalence factor  , is adopted. The equivalent factor  is a constant parameter in each control step that is determined by the method raised in our previous research [5,43,53]. The equivalent fuel consumption is written as:…”

Section: B Ltrmpc Energy Management Strategy In Phevmentioning

confidence: 99%

“…Additionally, we aim to exploit the GRU-based state observer to describe the longitudinal dynamic response with proven accuracy, the impact of the vehicle dynamic model on the optimization results could be neglected. The power balance equation [53]…”

Section: Constraints Of Ltrmpc Energy Management Strategymentioning

confidence: 99%

A Learning-and-Tube-Based Robust Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

Hou,

Chu,

Guo

et al. 2024

IEEE Trans. Intell. Veh.

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“…Hou et al mainly focused on the cooperation mechanism and basic framework under the Internet of vehicles. In addition, researchers also put forward an energy management method based on the Internet of vehicles [26]. At the same time, researchers also proposed that the disorderly charging of EVs will affect the energy management of smart buildings with renewable energy, and thus proposed a method to limit the disorderly charging behavior of EVs.…”

Section: Introductionmentioning

confidence: 99%

Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation

Jia,

Ma,

et al. 2023

Energies

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Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper’s goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome.

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Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy

Cited by 7 publications

References 20 publications

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

A Learning-and-Tube-Based Robust Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation

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