网联环境中新能源货车的粒子群调度方法

doi:10.16182/j.issn1004731x.joss.22-0116

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (6): 1337-1350.doi: 10.16182/j.issn1004731x.joss.22-0116

网联环境中新能源货车的粒子群调度方法

赵传超¹^,²(), 郑睿¹^,²(), 龚莉¹^,², 马小陆³

^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

收稿日期:2022-02-21 修回日期:2022-03-04 出版日期:2023-06-29 发布日期:2023-06-20
通讯作者: 郑睿 E-mail:zhao_chuanchao@163.com;zrwx0609@ahnu.edu.cn
作者简介:赵传超(1994-)，男，硕士生，研究方向为多智能体优化控制。E-mail：zhao_chuanchao@163.com
基金资助:
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)

Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment

Chuanchao Zhao¹^,²(), Rui Zheng¹^,²(), Li Gong¹^,², Xiaolu Ma³

^1.School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China
^2.Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
^3.School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243000, China

Received:2022-02-21 Revised:2022-03-04 Online:2023-06-29 Published:2023-06-20
Contact: Rui Zheng E-mail:zhao_chuanchao@163.com;zrwx0609@ahnu.edu.cn

摘要/Abstract

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。

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

Abstract:

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.

Key words: V2X(vehicle to everything), new energy truck, inertia weight, path encoding, particle swarm algorithm

中图分类号:

TP18

赵传超, 郑睿, 龚莉, 马小陆. 网联环境中新能源货车的粒子群调度方法[J]. 系统仿真学报, 2023, 35(6): 1337-1350.

Chuanchao Zhao, Rui Zheng, Li Gong, Xiaolu Ma. Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment[J]. Journal of System Simulation, 2023, 35(6): 1337-1350.

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测试函数	PF特征	决策变量	目标空间	种群规模
ZDT1	凸，连续	30	2	200
ZDT2	凹，连续	30	2	200
ZDT3	非连续	30	2	200
ZDT4	凸，连续	30	2	200
ZDT6	凹，连续	30	2	200

测试函数	改进的		传统的
测试函数	Mean	Std.	Mean	Std.
ZDT1	1.058E-02	1.428E-03	3.593E-01	7.041E-02
ZDT2	3.444E-01	3.630E-01	4.034E-01	4.052E-01
ZDT3	2.426E-02	3.118E-02	4.897E-02	4.893E-02
ZDT4	1.357E+01	3.979E+00	1.533E+01	1.362E+01
ZDT6	2.284E+00	2.108E+00	2.489E+00	2.105E+00

测试函数	改进的		传统的
测试函数	Mean	Std.	Mean	Std.
ZDT1	1.222E-02	1.635E-03	1.281E-02		1.777E-03
ZDT2	1.741E-03	3.173E-03	1.208E-02		4.352E-03
ZDT3	4.595E-02	2.426E+00	4.852E-02		1.218E-01
ZDT4	2.507E-02	1.252E+00	2.742E-02		1.138E-02
ZDT6	1.135E-02	2.108E+00	1.469E-02		3.100E-02

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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

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