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Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

机译：基于粒子群算法的混合动力电动汽车排队速度优化策略研究

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摘要

Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.
机译：交通信号灯十字路口在城市中很常见，并且会影响车辆的能耗，因此在城市道路条件下优化车辆的速度具有重要意义。本文提出了一种新的混合动力汽车队列优化速度优化策略，该策略有助于减少燃油消耗并提高交通效率，其中使用现实世界中的交通信号信息来构建研究场景。基于信号相位和时序（SPAT）获得目标速度的初始值。然后采用粒子群算法（PSO）求解非线性约束问题，并基于车对车通信（V2V）和车对基础设施通信（V2I）获得最优目标速度。下部控制器采用基于规则的控制策略，旨在将发动机和两个电动机的功率分配到功率分配的混合动力汽车中，由于其在燃油经济性方面的优势，这是非常有前途的。仿真结果证明了所提出策略在降低混合动力汽车队列燃油消耗和改善交通通畅性方面的卓越性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|6483145.1-6483145.14|共14页
  • 作者

    Wang Shaohua; Yu Chengquan; Shi Dehua; Sun Xiaoqiang;

  • 作者单位

    Jiangsu Univ, Sch Automobile & Traff Engn, Zhenjiang 212013, Peoples R China;

    Jiangsu Univ, Sch Automobile & Traff Engn, Zhenjiang 212013, Peoples R China;

    Jiangsu Univ, Sch Automobile & Traff Engn, Zhenjiang 212013, Peoples R China|Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Peoples R China;

    Jiangsu Univ, Sch Automobile & Traff Engn, Zhenjiang 212013, Peoples R China|Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Peoples R China;

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