载荷-车速耦合下物流配送电动车节能时空路径规划

doi:10.3969/j.issn.1674-8484.2026.03.008

摘要/Abstract

摘要：

为优化物流配送电动车能耗问题，提出一种质量载荷—速度耦合驱动的节能时空路径规划方法。构建了载荷-空间-时间状态网络，来描述车辆配送时空路径；融合“综合功率能耗模型（CPEM）”建立弧段能耗，通过解析能耗与速度的函数关系，动态确定不同载荷下的最优经济车速。提出一种“蚁群搜索+ Dijkstra寻优”的 2 阶段分解路径搜索策略，解决复杂配送阶段的候选路径搜索与返程阶段的精确路径能耗寻优问题。在 10 节点及Sioux Falls 24 节点网络上展开仿真实验。结果表明：在等距条件下，相较于其他路径，本方法得出的路径能耗降低约 7%；与传统最短路径策略相比，整体配送能耗降幅达 13%~16%。

关键词: 电动车, 物流配送, 路径规划, 车辆能耗, 装载量-空间-时间状态网络, 交通仿真

Abstract:

A load (mass) -speed-coupling driven-energy-efficient spatiotemporal path-planning method was proposed to optimize the energy consumption of electric vehicles in logistics distribution. A load-space-time state network was constructed to characterize vehicle distribution paths in the spatiotemporal domain. The energy consumption of each arc was formulated by integrating a comprehensive power-based energy consumption model (CPEM). Functional relationship between energy consumption and speed was derived to determine the optimal economic speed under varying load conditions. A two-stage decomposed path search strategy was developed combining the Ant Colony Optimization and the Dijkstra’s algorithm to address candidate path exploration in complex delivery stages and precise energy-optimal routing in the return stage. Simulation experiments were conducted on a 10-node network and the Sioux Falls 24-node network. The results show that the proposed method reduces energy consumption by approximately 7% compared with the alternative paths under equal-distance conditions. The overall energy consumption is reduced by 13%~16% compared with the conventional shortest-path strategy.

Key words: electric vehicles, logistics distribution, route planning, vehicle energy consumption, load-space-time state network, traffic simulation

中图分类号:

U461.91

王建强, 操建中, 童婷, 王锋. 载荷-车速耦合下物流配送电动车节能时空路径规划[J]. 汽车安全与节能学报, 2026, 17(3): 359-368.

WANG Jianqiang, CAO Jianzhong, TONG Ting, WANG Feng. Spatiotemporal path planning for energy saving of logistics delivery electric vehicles under load-speed coupling[J]. Journal of Automotive Safety and Energy, 2026, 17(3): 359-368.

图/表 23

参考文献 23

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车辆净质量，m₀	1.8 t
空气阻力因数，C_D	0.28
受风横截面积，A_cross	2.332 m²
空气密度，ρ_a	1.2 kg·m^-3
滑动阻力因数，μ	0.01
车况阻力因数，c₁	0.0328
轮胎阻力因数，c₂	4.575
电池效率，η_b	92%
最大迭代次数，iter_max	100
蚂蚁数量，w	50
挥发因数，ψ	0.3
信息素因子，α	1
启发函数因子，β	7
信息素强度，Q	1

车辆净质量，m₀	1.8 t
空气阻力因数，C_D	0.28
受风横截面积，A_cross	2.332 m²
空气密度，ρ_a	1.2 kg·m^-3
滑动阻力因数，μ	0.01
车况阻力因数，c₁	0.0328
轮胎阻力因数，c₂	4.575
电池效率，η_b	92%
最大迭代次数，iter_max	100
蚂蚁数量，w	50
挥发因数，ψ	0.3
信息素因子，α	1
启发函数因子，β	7
信息素强度，Q	1

实验组	权重参数a_i取值(a₁-a₁₇)	最低能耗路径(C0-C9)
1	9-3-6-10-3-7-9-8-7-5-6-12-5-4-8-9-7	C0-C3-C4-C5-C2-C1-C6-C7-C8-C9-C4-C3-C0
2	9-10-5-4-5-7-5-4-8-10-6-8-5-4-9-6-2	C0-C3-C4-C9-C8-C5-C7-C6-C1-C2-C0
3	5-13-14-14-14-6-13-7-14-14-15-8-6-5-10-9-11	C0-C1-C2-C3-C4-C9-C8-C7-C5-C6-C4-C0
4	20-18-18-17-20-12-16-9-10-20-10-10-13-7-8-18-10	C0-C3-C4-C9-C8-C5-C7-C6-C1-C2-C0
5	19-10-16-8-14-10-11-18-13-10-11-9-15-12-13-16-12	C0-C3-C2-C1-C6-C7-C8-C9-C4-C5-C2-C0

实验组	权重参数a_i取值(a₁-a₁₇)	最低能耗路径(C0-C9)
1	9-3-6-10-3-7-9-8-7-5-6-12-5-4-8-9-7	C0-C3-C4-C5-C2-C1-C6-C7-C8-C9-C4-C3-C0
2	9-10-5-4-5-7-5-4-8-10-6-8-5-4-9-6-2	C0-C3-C4-C9-C8-C5-C7-C6-C1-C2-C0
3	5-13-14-14-14-6-13-7-14-14-15-8-6-5-10-9-11	C0-C1-C2-C3-C4-C9-C8-C7-C5-C6-C4-C0
4	20-18-18-17-20-12-16-9-10-20-10-10-13-7-8-18-10	C0-C3-C4-C9-C8-C5-C7-C6-C1-C2-C0
5	19-10-16-8-14-10-11-18-13-10-11-9-15-12-13-16-12	C0-C3-C2-C1-C6-C7-C8-C9-C4-C5-C2-C0

场景编号	配送量分布特征	分布参数
1	显著差距分布	前5个高值节点超过300 kg，后5个低值节点小于100 kg
2	相对均匀分布	所有节点均为100~300 kg之间(序号相邻差值≤30 kg)
3	随机分布	100~400 kg之间随机分布(无规律，如表4)