In this paper, we present a high-level optimization-based control strategy for the coordination of electric automated vehicles (AVs) in confined sites. A centralized controller optimizes the state and input trajectories of all vehicles in the site such that collisions are avoided in cross-intersections, narrow roads, merge crossings, and charging stations, while also considering the charging process. Specifically, the controller consists of two optimization-based components. The first component is tasked with solving the combinatorial part of the problem, which corresponds to the order in which the vehicles pass the crossings, by solving a Mixed Integer Quadratic Problem (MIQP). The found combinatorial solution is then utilized for calculating the optimal state and input trajectories that are obtained by solving a Nonlinear Program (NLP). The control algorithm is compared with respect to alternative optimization-based approaches in simulation scenarios. For the presented scenario, our method achieves improved energy efficiency by up to 7.6% while slightly improving the average mission end time, and furthermore, it is capable of avoiding deadlocks.


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    Title :

    A Two-Stage MIQP-Based Optimization Approach for Coordinating Automated Electric Vehicles in Confined Sites


    Contributors:


    Publication date :

    2024-02-01


    Size :

    2340049 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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