On-ramps are typical bottleneck roads that can easily result in additional energy consumption for electric vehicles. Energy consumption can be effectively reduced by optimizing the merging trajectory, but powertrain efficiency, constraint, and regenerative braking need also be considered. This article proposes an energy-efficient hierarchical on-ramp merging control strategy (EHOMCS) for autonomous electric vehicles. At the tactical layer, an optimal merging sequence decision method is presented, which optimizes the merging sequence of ramp vehicles by forward simulation of mainline vehicles. At the operational layer, an energy-efficient long-short-term optimal merging control method is proposed. In the long-term horizon, vehicle motion and powertrain operation are co-optimized with consideration of powertrain efficiency and regenerative braking. In the short-term horizon, the powertrain and safe distance constraints are taken into account to solve the real-time optimal output torque of the powertrain. The effectiveness of the proposed algorithm is verified in various merging scenarios. The results indicate that the proposed algorithm significantly reduces the energy consumption of ramp vehicles as compared to a Baseline method and the existing method.


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    Title :

    Energy-Efficient Hierarchical On-Ramp Merging Control for Autonomous Electric Vehicles


    Contributors:


    Publication date :

    2025-02-01


    Size :

    1942246 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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