Hierarchical Communication Network for Electric Vehicles Aggregation and Control

The hierarchical aggregation and control of electric vehicles (EVs) is an effective way to promote the consumption of clean energy and ensure the balance of power demand-supply. In the case of increasingly insufficient wireless resources of cellular networks, one challenging problem is how to guarantee the real-time and accurate service interaction requirements of massive services in EV aggregation response. In this paper, in order to support the communication process of EV hierarchical aggregation and control, a hierarchical communication network architecture of “Central cloud- Regional cloud- Edge unit- EVs” is proposed. Considering the differences of service processing functions and communication demands of each layer, the utility function models are constructed to describe the different demands on the timeliness and accuracy. And based on the utility functions, a network communication resource allocation model is established to allocate power and channel resources on demand. Finally, to reduce the amount of computation, this paper proposes the joint allocation algorithm of power and channel resources based on the improved water-filling algorithm, adaptive particle swarm optimization algorithm and two-sided matching theory. The simulation experiment results illustrate that the total utility of the proposed algorithm is increased by 0.43 times on average compared with the channel average allocation (H-A), and 0.08 times compared with the layer-by-layer optimization (H-NJO).

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-08-26
DOI
https://doi.org/10.1109/tvt.2024.3395476
Citations
1
Primary Topic
Network Time Synchronization Technologies
Type
article
Field-Weighted Citation Impact
0.35
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article

Hierarchical Communication Network for Electric Vehicles Aggregation and Control

Xin Wu, Lin Liu, Xiang Li, Yawen Yu et al.
1 citations
IEEE Transactions on Vehicular Technology
Network Time Synchronization Technologies
0.35
article

Hierarchical Communication Network for Electric Vehicles Aggregation and Control

Xin Wu, Lin Liu, Xiang Li, Yawen Yu, Yi Liu
article en
1 citations

Abstract

The hierarchical aggregation and control of electric vehicles (EVs) is an effective way to promote the consumption of clean energy and ensure the balance of power demand-supply. In the case of increasingly insufficient wireless resources of cellular networks, one challenging problem is how to guarantee the real-time and accurate service interaction requirements of massive services in EV aggregation response. In this paper, in order to support the communication process of EV hierarchical aggregation and control, a hierarchical communication network architecture of “Central cloud- Regional cloud- Edge unit- EVs” is proposed. Considering the differences of service processing functions and communication demands of each layer, the utility function models are constructed to describe the different demands on the timeliness and accuracy. And based on the utility functions, a network communication resource allocation model is established to allocate power and channel resources on demand. Finally, to reduce the amount of computation, this paper proposes the joint allocation algorithm of power and channel resources based on the improved water-filling algorithm, adaptive particle swarm optimization algorithm and two-sided matching theory. The simulation experiment results illustrate that the total utility of the proposed algorithm is increased by 0.43 times on average compared with the channel average allocation (H-A), and 0.08 times compared with the layer-by-layer optimization (H-NJO).

IEEE Transactions on Vehicular TechnologyVol. 73(11)
North China Electric Power University (CN), China Southern Power Grid (China) (CN)
Openalex Percentile: Top 40%
Network Time Synchronization Technologies
0.35
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Hierarchical Communication Network for Electric Vehicles Aggregation and Control — Xin Wu, Lin Liu, et al. · IEEE Transactions on Vehicular Technology (2024) | TGRS Research Map | TGRS