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Federated Learning-based Distributed Control for Heterogeneous Platoons of Connected Autonomous Electric Vehicles

Project description

Advancing sustainable vehicle platoon technology

The European Green Deal aims to reduce emissions from conventional vehicles, promoting sustainable development. Heterogeneous platoons of connected autonomous electric vehicles (HPCAEVs) are a promising solution, but their secure and energy-efficient operation in mixed traffic poses challenges. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the FEASIBLE project aims to address these challenges by providing scientific foundations for HPCAEV operation. It focuses on networked modelling, stability analysis, and control design. Federated learning models will incorporate HPCAEV characteristics and interactions. Stability analysis criteria will be derived using Lyapunov theory, and robust predictive control methods will be developed. Advanced realistic simulations will test the effectiveness of these methods, comparing them with current cutting-edge solutions.

Objective

To promote the EU's sustainable development of environment and economy, EU planned to reduce emissions from conventional vehicles in European Green Deal. Heterogeneous platoons of connected autonomous electric vehicles (HPCAEVs) are thought to be the most efficient, viable, and promising solution to reduce both energy consumption and pollutant emissions produced by conventional vehicles and improve road efficiency. However, since HPCAEVs are still in the evolving stage, the secure and energy-saving operation of HPCAEVs in mixed traffic flow is suffering significant challenges. The FEASIBLE project is proposed in the context of addressing these challenges by providing some scientific foundations that enable the secure and energy-saving operation of HPCAEVs in mixed traffic flow, with particular focus on networked modeling, stability analysis, and control design. First, network-level federated learning (FL) models will be set up by incorporating intrinsic characteristics of HPCAEVs, macroscopic traffic flow, surrounding vehicle flow, and their interactions. Second, novel stability analysis criteria for HPCAEVs will be derived with expected dynamic performance by designing a FL-based Lyapunov function with sufficiently small and bounded modeling error. Third, FL-based distributed robust model predictive control methods for HPCAEVs with intervehicle distance constraints will be developed under surrounding road conditions. Finally, advanced simulations and tests of HPCAEVs will be conducted to demonstrate the effectiveness and practicality of the proposed modeling and control methods, and the control performance will be compared with cutting-edge methods.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSITA DEGLI STUDI DI PAVIA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 172 750,08
Address
STRADA NUOVA 65
27100 Pavia
Italy

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Region
Nord-Ovest Lombardia Pavia
Activity type
Higher or Secondary Education Establishments
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Total cost

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