网联环境中新能源货车的粒子群调度方法  

Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment

作　　者：赵传超 郑睿[1,2] 龚莉 马小陆 Zhao Chuanchao;Zheng Rui;Gong Li;Ma Xiaou(School of Physics and Electronic Information,Anhui Normal University,Wuhu 241002,China;Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot(Anhui Normal University),Wuhu 241002,China;School of Electrical and Information Engineering,Anhui University of Technology,Maanshan 243000,China)

机构地区：[1]安徽师范大学物理与电子信息学院,安徽芜湖241002 [2]安徽省智能机器人信息融合与控制工程实验室(安徽师范大学),安徽芜湖241002 [3]安徽工业大学电气与信息工程学院,安徽马鞍山243000

出　　处：《系统仿真学报》2023年第6期1337-1350,共14页Journal of System Simulation

基　　金：安徽省重点研究与开发计划(202004a0502001);安徽省科技重大专项(202003a05020028);安徽省自然科学基金(1908085MF216);安徽省高校优秀青年支持计划(gxyq202002)。

摘　　要：在V2X(vehicle to everything)智能网联环境中,新能源货车的调度系统需获取实时的动态信息。利用传统的粒子群调度方法,系统存在易陷入局部最优和求解效率不高的问题。在研究新能源货车多目标优化问题的基础上,提出改进的新能源货车粒子群调度方法。改进惯性权重更新方式使惯性权重呈非线性递减,降低系统陷入局部最优的风险;设计“先验性”路径编码的方式,优化路径编码,提高算法的求解效率,减少新能源货车的能源消耗。仿真结果表明:总路径长度、路径平滑度和算法的收敛速度方面均有改善,在静态和动态环境中,改进的方法均能实现新能源货车的合理调度,对于构建新能源货车的智能网联系统有重要意义。In V2X intelligent network environment,the dispatching system of new energy trucks needs real-time dynamic information.The system under traditional particle swarm scheduling method is prone to fall into local optimum and low solution efficienty.An improved particle swarm scheduling method for new energy trucks is proposed on the basis of multi-objective optiminaztion research.The inertia weight update method is improved so that the inertia weight decreases non-linearly,and the risk of the system falling into local optimum is reduced.A priori path encoding method is designed and optimized,the solution efficiency of the algorithm is improved,and the energy consumption of new energy trucks is reduced.The simulation results show that the total path length,path smoothness,and algorithm convergence speed are improved.In both static and dynamic environments,the reasonable scheduling of new energy trucks is achieved by the improved method.

关 键 词：V2X(vehicle to everything) 新能源货车 惯性权重 路径编码 粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]