Spatiotemporal path planning for energy saving of logistics delivery electric vehicles under load-speed coupling

doi:10.3969/j.issn.1674-8484.2026.03.008

Abstract

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

CLC Number:

U461.91

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.

Figures/Tables 23

References 23

[1]	HAN Bing, YAN Yifan, CHI Tianze, et al. The electric vehicle routing problem with travel time and energy consumption uncertainty[J]. Transport Res Part E: Logist Transport Rev, 2025, 202: Paper No 104211.
[2]	孙超, 刘波, 孙逢春. 新能源汽车节能规划与控制技术研究综述[J]. 汽车安全与节能学报, 2022, 13(4): 593-616.
	SUN Chao, LIU Bo, SUN Fengchun. Review of energy-saving planning and control technology fornew energy vehicles[J]. J Autom Safe Energ, 2022, 13(4): 593-616. (in Chinese)
[3]	CHEN Xirong, WANG Jinrun, DENG Haowei, et al. Shortest path planning in multimodal metropolitan transportation networks under timetable and label constraints[J]. J Advan Transport, 2025, 2025(1): 3365603.
[4]	周鲜成, 周开军, 王莉, 等. 物流配送中的绿色车辆路径模型与求解算法研究综述[J]. 系统工程理论与实践, 2021, 41(1): 213-230. doi: 10.12011/SETP2020-2300
	ZHOU Xiancheng, ZHOU Kaijun, WANG Li, et al. Review of green vehicle routing model and its algorithm in logistics distribution[J]. Syst Engi:Theo Pract, 2021, 41(1): 213-230. (in Chinese)
[5]	姜广田, 纪皎月, 董佳伟. 绿色物流配送下的多车型动态车辆路径优化[J]. 系统工程理论与实践, 2024, 44(7): 2362-2380. doi: 10.12011/SETP2023-0524
	JIANG Guangtian, JI Jiaoyue, DONG Jiawei. Multi-vehicle dynamic vehicle routing optimization in green-logistics distribution[J]. Syst Engi-Theo Prac, 2024, 44(7): 2362-2380. (in Chinese)
[6]	陈晓峰, 王兰文, 马果, 等. 考虑能耗及稳定性的无人驾驶车辆越野环境路径规划[J]. 汽车安全与节能学报, 2025, 16(3): 496-503.
	CHEN Xiaofeng, WANG Lanwen, MA Guo, et al. Energy and stability aware path planning for autonomousvehicles in off road environments[J]. J Autom Safe Energ, 2025, 16(3): 496-503. (in Chinese)
[7]	Demir E, Bektaş T, Laporte G. An adaptive large neighbor-hood search heuristic for the pollution-routing problem[J]. Euro J Opera Res, 2012, 223(2): 346-359.
[8]	Kramer R, Subramanian A, Vidal T, et al. A matheuristic approach for the pollution-routing problem[J]. Europ J Opera Res, 2015, 243(2): 523-539.
[9]	Franceschetti A, Honhon D, Van Woensel T, et al. The time-dependent pollution-routing problem[J]. Transport Res Part B:Meth, 2013, 56: 265-293. doi: 10.1016/j.trb.2013.08.008 URL
[10]	Franceschetti A, Demir E, Honhon D, et al. A metaheuristic for the time-dependent pollution-routing problem[J]. Euro J Opera Res, 2017, 259(3): 972-991.
[11]	Ben A S, Rejeb L, Baati L. Route planning for electric vehicles[J]. IET Intel Transport Syst, 2022, 16(7): 875-889.
[12]	ZHANG Shuai, Gajpal Y, Appadoo S S, et al. Electric vehicle routing problem with recharging stations for minimizing energy consumption[J]. Int’l J Prod Econ, 2018, 203: 404-413.
[13]	XIAO Yiyong, ZUO Xiaorong, Kaku I, et al. Development of energy consumption optimization model for the electric vehicle routing problem with time windows[J]. J Clean Prod, 2019, 225: 647-663. doi: 10.1016/j.jclepro.2019.03.323
[14]	刘育良, 陈淮莉. 负载影响下纯电动汽车配送路径规划及充电策略[J]. 计算机应用, 2020, 40(10): 2831-2837. doi: 10.11772/j.issn.1001-9081.2020020157
	LIU Yuliang, CHEN Huaili. Distribution path planning and charging strategy for pure electric vehicles with load constraint[J]. J Comput Appl, 2020, 40(10): 2831-2837. (in Chinese) doi: 10.11772/j.issn.1001-9081.2020020157
[15]	陈婉茹, 徐光明, 张得志, 等. 碳交易机制下多中心混合车队配送路径和速度优化研究[J]. 系统工程理论与实践, 2023, 43(11): 3320-3335. doi: 10.12011/SETP2022-2971
	CHEN Wanru, XU Guangming, ZHANG Dezhi, et al. Multi-depot mixed fleet routing and speed optimization under acarbon trading mechanism[J]. Syst Engi-Theo Prac, 2023, 43(11): 3320-3335. (in Chinese)
[16]	Cieslik W, Antczak W. Research of load impact on energy consumption in an electric delivery vehicle based on real driving conditions: guidance for electrification of light-duty vehicle fleet[J]. Energies, 2023, 16(2): 775-794.
[17]	LIU Luying, Kotz A, Salapaka A, et al. Impact of time-varying passenger loading on conventional and electrified transit bus energy consumption[J]. Transport Res Rec, 2019, 2673(10): 632-640. doi: 10.1177/0361198119852337
[18]	TONG Zheming, GUAN Sheng, ZHANG Qinguo, et al. Data-driven energy efficient speed planning for battery electric industrial vehicles: forklift as a case study[J]. J Clean Prod, 2024, 443: 140923. doi: 10.1016/j.jclepro.2024.140923 URL
[19]	郑李萍, 王建强, 张玉召, 等. 多时空配送任务驱动的无人车队车辆数优化方法[J]. 计算机应用, 2021, 41(5): 1406-1411. doi: 10.11772/j.issn.1001-9081.2020081183
	ZHENG Liping, WANG Jianqiang, ZHANG Yuzhao, et al. Vehicle number optimization approach of autonomous vehicle fleet driven by multi-spatio-temporal distribution task[J]. J Comput Appl, 2021, 41(5): 1406-1411. (in Chinese) doi: 10.11772/j.issn.1001-9081.2020081183
[20]	Fiori C, Ahn K, Rkha H A. Power-based electric vehicle energy consumption model: model development and validation[J]. Appl Energ, 2016, 168: 257-268. doi: 10.1016/j.apenergy.2016.01.097 URL
[21]	BAI Xiangen, LIU Di, XU Xiaofeng. A review of improved methods for ant colony optimization in path planning[J]. J Ship Res, 2024, 68(02): 77-92.
[22]	GAO Yucen, LI Zhuoran, HE Jingyu, et al. Algorithms for shortest path tour problem[J]. Theo Comput Sci, 2025: 115428.
[23]	ZHU Yazhen, SONG Qing, LI Meng. Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm[J]. Int’l J Advan Robo Syst, 2025, 22(1): 1312784.

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)