Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Zhang, Peiyu; Zhou, Jianshan; Duan, Xuting; Sheng, Zhengguo; Zhao, Dezong; Cao, Dongpu

doi:10.1109/tiv.2023.3265866

Cited by 20 publications

(2 citation statements)

References 39 publications

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“…The roadside infrastructures communicate with vehicles include traffic lights, road signs, and centralized traffic management systems. Vehicle platooning concept have remarkable significance to achieve road safety and high efficiency [137]. The application of V2I technologies include safety and mobility applications, including collision avoidance, collision detection, traffic light detection, toll and fine collection, and smart parking [138]- [141].…”

Section: B Vehicle-to-x Technologiesmentioning

confidence: 99%

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Yavuz,

Kivanç

2024

IEEE Access

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The concept of Prosumer has enabled consumers to actively participate in Peer-to-Peer (P2P) energy trading, particularly as Renewable Energy Source (RES)s and Electric Vehicle (EV)s have become more accessible and cost-effective. In addition to the P2P energy trading, prosumers benefit from the relatively high energy capacity of EVs through the integration of Vehicle-to-X (V2X) technologies, such as Vehicle-to-Home (V2H), Vehicle-to-Load (V2L), and Vehicle-to-Grid (V2G). Optimization of an Energy Management System (EMS) is required to allocate the required energy efficiently within the cluster, due to the complex pricing and energy exchange mechanism of P2P energy trading and multiple EVs with V2X technologies. In this paper, Deep Reinforcement Learning (DRL) based EMS optimization method is proposed to optimize the pricing and energy exchanging mechanisms of the P2P energy trading without affecting the comfort of prosumers. The proposed EMS is applied to a small-scale cluster-based environment, including multiple (6) prosumers, P2P energy trading with novel hybrid pricing and energy exchanging mechanisms, and V2X technologies (V2H, V2L, and V2G) to reduce the overall energy costs and increase the Self-Sufficiency Ratio (SSR)s. Multi Double Deep Q-Network (DDQN) agents based DRL algorithm is implemented and the environment is formulated as a Markov Decision Process (MDP) to optimize the decision-making process. Numerical results show that the proposed EMS reduces the overall energy costs by 19.18%, increases the SSRs by 9.39%, and achieves an overall 65.87% SSR. Additionally, numerical results indicates that model-free DRL, such as DDQN agent based Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, promise to eliminate the energy management complexities with multiple uncertainties.INDEX TERMS Energy management system, peer-to-peer energy trading, vehicle-to-home, multi-agent reinforcement learning, deep reinforcement learning, smart grids.

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Section: B Vehicle-to-x Technologiesmentioning

confidence: 99%

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Yavuz,

Kivanç

2024

IEEE Access

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“…However, satellite navigation is susceptible to harsh environments, such as tunnels and urban canyons [2][3][4]. Cooperative vehicle infrastructure systems (CVIS) are widely applied to make up for the shortcomings of satellite positioning [5]. This technology connects all kinds of transportation elements including vehicle clusters, roadside units and wireless network links [6].…”

Section: Introductionmentioning

confidence: 99%

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Cao,

Li,

Tang

et al. 2023

Sensors

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A vehicle’s position can be estimated with array receiving signal data without the help of satellite navigation. However, traditional array self-position determination methods are faced with the risk of failure under multipath environments. To deal with this problem, an array signal subspace fitting method is proposed for suppressing the multipath effect. Firstly, all signal incidence angles are estimated with enhanced spatial smoothing and root multiple signal classification (Root-MUSIC). Then, non-line-of-sight (NLOS) components are distinguished from multipath signals using a K-means clustering algorithm. Finally, the signal subspace fitting (SSF) function with a P matrix is established to reduce the NLOS components in multipath signals. Meanwhile, based on the initial clustering estimation, the search area can be significantly reduced, which can lead to less computational complexity. Compared with the C-matrix, oblique projection, initial signal fitting (ISF), multiple signal classification (MUSIC) and signal subspace fitting (SSF), the simulated experiments indicate that the proposed method has better NLOS component suppression performance, less computational complexity and more accurate positioning precision. A numerical analysis shows that the complexity of the proposed method has been reduced by at least 7.64dB. A cumulative distribution function (CDF) analysis demonstrates that the estimation accuracy of the proposed method is increased by 3.10dB compared with the clustering algorithm and 11.77dB compared with MUSIC, ISF and SSF under multipath environments.

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Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Sun,

Zhang,

et al. 2024

Energy

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Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Cited by 20 publications

References 39 publications

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

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