Electric vehicle scheduling method based on multivariate data

A scheduling method and multi-data technology, applied in the field of electric vehicles, can solve problems such as the difficulty of solving large-scale problems of algorithms, and achieve the effect of improving accuracy and confidence.

Pending Publication Date: 2019-10-01
TONGJI UNIV
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Problems solved by technology

Based on the Markov decision model, Minkoff completed the modeling and solution of the vehicle dynamic scheduling problem based on the Markov decision process. The proposed algorithm can be satisfied in the solution of small and medium-scale (10 demands) vehicle dynamic scheduling problems. However, due to the limitations of the model, the algorithm is difficult to solve large-scale problems

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  • Electric vehicle scheduling method based on multivariate data
  • Electric vehicle scheduling method based on multivariate data
  • Electric vehicle scheduling method based on multivariate data

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Embodiment Construction

[0021] According to one or more embodiments, such as figure 1 shown. A Multivariate Data Based Electric Vehicle Scheduling Method. Based on travel data such as taxi floating car data, mobile phone signaling data, and time-sharing leases, and on the premise of determining constraints (constraints include company profits, transshipment costs, shortage losses, shortest paths, etc.), use machine learning to weight The reservoir sampling algorithm is used to obtain data, and an optimization model is established to obtain the optimal solution, that is, the optimal scheduling method, so as to meet the needs of passengers while avoiding resource waste. Specific steps are as follows:

[0022] (1) Under the Online Mode, multiple travel data such as taxi floating car data, mobile phone signaling data, and time-sharing leases are collected uniformly, and a travel database is established;

[0023] (2) Apply the weighted reservoir flow sampling algorithm to determine the weight, which ca...

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Abstract

An electric vehicle scheduling method based on multivariate data comprises the steps: receiving floating vehicle data, mobile phone signaling data and / or time-sharing vehicle leasing travel data, andestablishing a travel database; and on the premise that constraint conditions are determined, utilizing a weighted reservoir sampling algorithm in machine learning to obtain data, establishing an optimization model, and obtaining an optimal solution, namely the optimal scheduling method.

Description

technical field [0001] The invention belongs to the technical field of electric vehicles, in particular to an electric vehicle scheduling method based on multivariate data. Background technique [0002] As one of the main forces for the development of new energy vehicles, electric vehicles have relatively high operating costs due to the sudden and intensive flow of road vehicles. The currently promoted electric vehicles are developing in the direction of intelligence. The planning and decision-making module of the vehicle management system is equivalent to the brain of the vehicle. By comprehensively analyzing the information provided by the environmental perception system, the current vehicle behavior is planned, including speed planning, avoidance Obstacle local path planning, etc., and generate corresponding decisions, such as car following, lane changing, parking, etc. The planning and decision-making module also needs to consider the mechanical characteristics, dynamic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/04G06Q50/30
CPCG06Q10/06315G06Q10/04G06Q50/30Y02T10/40
Inventor 骆晓李晔
Owner TONGJI UNIV
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