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AMoD system charging scheduling and vehicle rebalancing method considering riding pricing

A rebalancing and vehicle technology, applied in the direction of instruments, data processing applications, resources, etc., can solve the problems of not fully describing the dynamic change process of the vehicle, not considering the impact, etc., to improve the poor dynamic performance, wide application range, good dynamic Effects of changing traffic conditions

Inactive Publication Date: 2021-08-24
东北大学秦皇岛分校
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The above research only focuses on certain aspects of pricing, charging scheduling, and vehicle rebalancing operations to optimize the operating efficiency of the AMoD system, without considering all aspects, and cannot fully describe the dynamic process of vehicles in the system
At the same time, the above research does not consider the impact of future customer dynamic changes on the system, and timely prediction of future customer dynamic changes plays an important role in improving system operating efficiency

Method used

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  • AMoD system charging scheduling and vehicle rebalancing method considering riding pricing
  • AMoD system charging scheduling and vehicle rebalancing method considering riding pricing
  • AMoD system charging scheduling and vehicle rebalancing method considering riding pricing

Examples

Experimental program
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Effect test

Embodiment 1

[0306] The parameters in Embodiment 1 are set as follows: Assume that the entire system has 8 customer sites, 4 large charging sites, and each customer site has a small charging station. Customer according to parameter Λ i The Poisson distribution process randomly arrives at each station, and the arrival rate of each station is uniformly randomly selected in the interval [0.5, 0.95], and each time step reaches one unit. The destination distribution of each customer is derived from the distribution p ij obtained by sampling, and p ij Also uniformly randomly generated in the interval [0,1]. The travel time between each station is given by the Euclidean distance, with each vehicle moving 0.2 per unit step. Furthermore, assuming that each PS i The generation rate of low-battery vehicles is directly proportional to the customer arrival rate, that is, Each charging station CS k Has 3 charging piles, each CS k The charging rate is 0.1, 0.1, 0.2, 0.2. CSP per charging stati...

Embodiment 2

[0309]Example 2: Through actual scene analysis, in this case study, the effectiveness of the proposed adaptive method is tested based on the large-scale simulation software AMoDeus and the actual operating data of a rental company. The data set mainly includes the taxi driving trajectory data of a certain city in November 2016, from which we arbitrarily extracted one day's data for scene analysis. The basic road data of the simulation mainly comes from part of the map data of the city center, which mainly includes 46012 roads and 20376 nodes. AMoDeus can use the k-means clustering algorithm to generate stations based on the data set, so there are 50 customer stations and 10 large charging stations in the setup system. In addition, the number of customers in the system is set to 10494, the total number of vehicles in the system is 500, and the system is rebalanced and optimized every 30 minutes. Figure 7 , 8 Shows how customer wait times change over the course of a day. Fro...

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Abstract

The invention provides an AMoD system charging scheduling and vehicle rebalancing method considering riding pricing, and the method comprises the steps: designing a customer riding pricing mechanism according to the riding willingness of a customer, designing a low-electric-quantity vehicle charging mechanism and a fully-charged vehicle scheduling mechanism, establishing a fluid model according to the operation mechanism of an autonomous on-demand travel system, establishing a vehicle migration kinetic equation, building a vehicle charging queuing model, deducing a queue stability condition, calculating charging delay, analyzing a system static equilibrium state and a system balance condition, giving a minimum vehicle queue scale required by stable system operation, designing a real-time rebalance strategy, and periodically adjusting system rebalance. Charging scheduling of the autonomous on-demand travel system and dynamic balance control of vehicle supply and demand are realized; according to the method, the problem of combination among a riding pricing mechanism, electric vehicle charging scheduling and vehicle rebalance is comprehensively considered, and the defect that the dynamic performance of a static strategy is poor can be effectively overcome.

Description

technical field [0001] The invention belongs to the control field of an autonomous on-demand travel system, and in particular relates to a charging scheduling and vehicle rebalancing method of an AMoD system considering ride pricing. Background technique [0002] Due to the spatio-temporal characteristics of urban traffic, the starting and ending points of customer travel are unevenly distributed. After serving customers, vehicles tend to gather at some stations and run out at other stations. Autonomous Mobility-on-Demand (AMoD) The system will inevitably become unbalanced, reducing the overall quality of service. Autonomous vehicles that use electricity as their energy source need to go to charging stations for recharging on a regular basis. Improper management of charging infrastructure in high-demand areas may cause large queuing delays and affect system operation efficiency. Therefore, reasonable scheduling of available idle vehicles (i.e., vehicle rebalancing) and char...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/30
CPCG06Q10/06312G06Q10/067G06Q50/40
Inventor 郭戈康明高振宇
Owner 东北大学秦皇岛分校
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