Charging pile intelligent control management platform

By designing the intelligent control and management platform for charging piles, and using the data acquisition and load prediction module to dynamically adjust the power output of charging piles, the problem of lack of intelligent adjustment capabilities in the existing technology is solved, and more efficient and stable power grid resource management is achieved.

CN119726679AInactive Publication Date: 2025-03-28北京新技发科技有限公司
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Patent Information

Application Number
CN202411808563.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing charging pile management methods lack dynamic prediction and intelligent regulation capabilities for grid load and user needs, resulting in overloading of charging piles during peak periods, increasing user waiting time, unbalanced grid load, increasing power loss and equipment failure risks.

Method used

Design an intelligent control and management platform for charging piles, including data acquisition, data preprocessing, load prediction and demand analysis, charging pile scheduling and power adjustment, real-time monitoring and feedback, report generation and decision support, etc., through real-time data acquisition and analysis, use regression models to predict load changes, dynamically adjust charging pile power output, and optimize grid resource allocation.

Benefits of technology

It realizes accurate prediction and intelligent adjustment of grid load and user needs, avoids charging pile overload and grid imbalance, shortens user waiting time, improves grid operation stability and resource utilization efficiency, and extends the service life of charging piles.

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Abstract

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.
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