TCPN-based electric vehicle charging path planning method and system

By using a TCPN-based electric vehicle charging path planning method, and combining traffic, charging station, and power grid data, a six-tuple time-constrained Petri net model is established to dynamically constrain the charging path. This solves the problem of planning results deviating from actual needs in existing technologies and achieves efficient and dynamic charging path optimization.

CN122311577APending Publication Date: 2026-06-30ZHENGZHOU UNIV
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Patent Information

Application Number
CN202610404208.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-30
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing path planning methods cannot meet the dynamic balance of traffic, charging stations, power grid and user demand during electric vehicle charging, resulting in planning results that deviate from actual needs and low solution efficiency, failing to meet real-time response requirements.

Method used

A TCPN-based electric vehicle charging path planning method is adopted. By acquiring traffic, charging station, power grid and user data, a six-tuple time-constrained Petri net model is established. Combined with a structural controller for dynamic constraints, the charging path is optimized to minimize time, cost and carbon emissions.

Benefits of technology

It enables refined planning of charging routes, meets users' real-time response needs, achieves dynamic balance between traffic network status, grid load and user demand, shortens planning solution time, and improves charging efficiency and grid stability.

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Abstract

The invention belongs to the technical field of path planning, and particularly relates to an electric vehicle charging path planning method and system based on TCPN. According to the method, the traffic time and the maximum traffic capacity of each road section in a traffic network, the number of idle charging piles in a charging station, the charging power, the queuing waiting time and the total charging service cost are calculated; mapping data such as power grid load rate, charging carbon emission and electricity price fluctuation coefficient in the power grid into a place, transition and time constraint in the time constraint Petri network, and adding a structure controller to control the model; all feasible charging paths of the model are solved to obtain the optimal charging path from the goal of comprehensively minimizing the three aspects of time, cost and carbon emission, the charging path planning solving time is shortened, the established electric vehicle charging path model is more refined, the planned path better meets the real-time response requirement of a user, and the user experience is improved. And collaborative optimization of the charging efficiency and the power grid stability is realized.
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