Study on Distribution Path Planning of Electric Logistics Vehicles Under Partial Charging Strategy

Jinfeng Lu

doi:10.54097/hd3cwb22

Authors

Jinfeng Lu

DOI:

https://doi.org/10.54097/hd3cwb22

Keywords:

Distribution path planning; partial charging strategy; electric logistics vehicle; soft time window; urban distribution.

Abstract

The current situation of energy consumption and environmental pollution is becoming more and more serious, electric vehicles have gradually become an important distribution tool for logistics companies due to their advantages of low energy consumption and low pollution, however, the problems of limited battery capacity and long charging time are still the main obstacles to their effective promotion in the field of logistics and distribution. In order to improve the utilization rate of electric logistics vehicles, the charging method of the vehicle is studied in detail, and it is proposed to use partial charging strategy to optimize the distribution path scheme of electric logistics vehicles with soft time window, and to establish an optimization model with the goal of minimizing the total cost of distribution, and at the same time, consider the constraints of the vehicle's loading, the customer's soft time window, and the battery endurance; the genetic algorithm is improved by using the idea of destruction and repair in the large neighborhood search algorithm, and the problem is solving, and verify the effectiveness of the model and algorithm using the Solomon arithmetic set; and conduct a sensitivity analysis of the distribution cost by adjusting 2 parameters, the battery capacity and charging price of the electric vehicle. The results show that the partial charging strategy can effectively reduce the cost compared with the full charging strategy in the distribution activities of electric logistics vehicles. In the example, the total cost is reduced by 7.69%, the electricity cost is reduced by 8.85%, and the total distance is shortened by 7.4%. In this case, the total cost, electricity cost and time window cost increased by 6.22%, 19.83% and 6.83% respectively. At the same time, some charging strategies can also significantly improve the power utilization rate of the vehicle battery, and reduce the idle and waste of vehicle resources and power resources. This paper proposes a distribution route optimization model of electric logistics vehicles under the partial charging strategy, which can provide beneficial enlightenment for logistics enterprises in the scheme scheduling and distribution model selection in urban distribution, and is of great significance for reducing the distribution cost of logistics enterprises.

Downloads

Download data is not yet available.

References

Zhuang Helin, Xia Xiaoyun, Li Kangshun et al. Research progress of electric vehicle path planning model and algorithm[J]. Journal of System Simulation, 2024, 36(02):320-337.

LI Ying, ZHANG Pengwei, WU Yifan. A vehicle allocation and path optimization model for hybrid electric vehicle/conventional vehicle fleet [J]. Journal of System Management, 2020, 29(03):522-531.

JIN Dongyao, LIU Min, ZHU Yeona et al. Solving electric vehicle path problems with load constraints based on hybrid genetic search[J/OL]. Journal of System Simulation,1-13.(2023-10-25). [2024-02-20]. https://doi.org/10.16182/j.issn1004731x.joss.23-0863.

KESKIN M, CATAY B, LAPORTE G. A simulation-based heuristic for the electric vehicle routing problem with time windows and stochastic waiting times at recharging stations[J]. COMPUTERS & OPERATIONS RESEARCH, 2021, 8(03): 125-143.

WU Fuliang, DONG Ming. Research on optimal path planning problem for electric vehicle distribution considering energy consumption [J]. Industrial Engineering and Management, 2023, 28(03):9-16.

Zhao Peng. Research on electric vehicle charging and switching station siting planning method [D]. Dalian University of Technology, 2020.

Ma Yanfang, Xue Jinzhao, Li Baoyu et al. Mobile charging pile site-path optimization and genetic-RSI two-stage algorithm [J/OL]. Computer Engineering and Applications. (2023-06-28). [2024-02-20]. http://kns.cnki.net/kcms/detail/11.2127.tp.20230626.1903.022.html.

Ma Qiao. Research on charging station layout optimization and orderly charging/discharging strategy based on electric vehicle charging load spatio-temporal distribution prediction[D]. Xi'an University of Technology,2023.

LAM E, DESAULNIERS G, STUCKEY P J. Branch-and-cut-and-price for the Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations[J]. COMPUTERS & OPERATIONS RESEARCH, 2022,9(03):145-157.

MAO H T, SHI J M, ZHOU Y Z, et al. The Electric Vehicle Routing Problem With Time Windows and Multiple Recharging Options[J]. IEEE ACCESS, 2020,8(03): 114864-114875.

Huang JH, Liu FX. Charging strategy and path optimization problem for electric vehicles under dynamic load[J]. Computer Integrated Manufacturing Systems,2023,29(11):3909-3921.

Ge Xianlong, Li Zuwei, Ge Xiaobo. Optimization study of logistics and distribution path with time window considering flexible charging strategy[J]. Control Theory and Applications, 2020, 37(06):1293-1301

Feng Shasha. Research on delivery path optimization of electric logistics vehicles based on partial charging strategy [D]. Beijing Jiaotong University,2019.

Li Shuxuan. Research on logistics and distribution path optimization of electric vehicles considering charging strategy[D]. Central South Forestry University of Science and Technology,2021.

CHEN Mingxing, ZHOU Zhifeng, ZHAO Tianqi et al. Research on carbon emission calculation and reduction strategy of the whole life cycle of semi-steel radial tires[J]. Rubber Industry,2023,70(05):393-399.

KANG Xiaoping, NIE Huihui, GAO Min et al. Life cycle carbon emissions of electric vehicles[J]. Energy Storage Science and Technology,2023,12(03):976-984.

Li Zuwei. Research on urban logistics and distribution path optimization based on electric vehicle charging selection [D]. Chongqing Jiaotong University,2019.