Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (6): 1984-1993.doi: 10.13229/j.cnki.jdxbgxb.20231277

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Scheduling algorithm for battery electric vehicle in closed scenic area

Sheng-yu YAN(),Ming-jie CHENG,Hong-ce TIAN,Hong-yu WANG,Yong-heng ZHOU,Bo-hao MA   

  1. School of Automobile,Chang'an University,Xi'an 710018,China
  • Received:2023-11-20 Online:2025-06-01 Published:2025-07-23

Abstract:

To meet the scheduling needs of battery electric vehicle (BEV) in closed scenic areas, a multi-objective scheduling model was proposed. With the goal of optimizing the operating costs of BEV procurement, frequency of departure, stopping time, and charging price difference, a departure schedule solving algorithm was designed based on UI rules. Heuristic algorithms were used to solve the train number chain set, and a BEV performance testing plan was designed. By limiting the driving speed of the test sample vehicle, the single round trip time was obtained, and a maximum round trip calculation method combining CRUISE simulation and real vehicle testing was proposed. Taking the south line of Mount Wutai scenic spot as an example, the feasibility of BEV scheduling model and solution algorithm was verified. The results indicate that the UI rule-based time slot BEV scheduling algorithm can achieve minute level BEV departure schedules. The deviation rate between the calculated number of BEV cars purchased on the example route and the ideal minimum number of cars purchased is 2.99%, with a solution time of 0.89 seconds. When simulating a scheduling plan with a daily passenger flow from 3 000 to 30 000, the maximum deviation rate of actual transportation capacity supply and demand is 1.00%. The research results can be applied to the BEV dynamic scheduling algorithm and vehicle scale calculation model for enclosed scenic spots.

Key words: engineering of communication and transportation system, closed scenic area, battery electric vehicle(BEV), multi-objective scheduling, optimal cost model, heuristic algorithm

CLC Number: 

  • U492.2

Fig.1

Algorithm for solving departure timetable"

Fig.2

Flow chart of heuristic algorithm"

Fig.3

Parameter optimization process"

Table 1

Driving speed requirement of BEV"

弯道分类坡度

上坡车速/

(km·h-1

下坡车速/

(km·h-1

多弯道

路段

3%以内[30,70][40,70]
3%~8%20,60][30,50]
8%以上20,50]20,40]

少弯道

路段

3%以内[40,80][40,80]
3%~8%[30,60][40,60]
8%以上20,40][30,50]

Fig.4

Model of electricity consumption on CRUISE"

Table 2

Basic parameters of the tested BEV"

试验样车型号与技术参数
车型ZK6127BEV
整备质量/kg10 600
长×宽×高/(mm×mm×mm)11 970×2 550×3 635
动力电池额定功率/(kW·h)350
座位数/座49+1
驱动电机型号TZ400XSYTB49
开启空调的续驶里程/km305

Table 3

Basic parameters of the south line and charging pile"

参数数值
Q/(人·日-124 000
Wmax/次3
t1/min6
t2/min20
t3/min70
320 kW直流单枪快充电流/A500
320 kW直流双枪充电电流/A250
320 kW直流单枪快充功率/kW320
320 kW直流双枪充电功率/kW160

Fig.5

Balance of transportation capacity supply and demand"

Fig.6

Gantt chart of scheduling"

Fig.7

Deviation rate of actual vehicle purchases under different of passenger flow"

Fig.8

Algorithm stability verification chart"

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