AI-based charging process safety monitoring method and system

By using AI-based neural network encoders and trajectory matching technology, the problems of missed and false alarms during charging have been solved, enabling precise monitoring and adaptive control of the charging process and improving the safety and stability of charging equipment.

CN122203513BActive Publication Date: 2026-09-11BEIJING XINKAIRUI TECH DEV CO LTD
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Patent Information

Application Number
CN202610662005.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-14
Publication Date
2026-09-11
Estimated Expiration
2046-05-14

AI Technical Summary

Technical Problem

Existing charging process safety monitoring technologies suffer from issues of missed or false alarms, and are unable to cope with parameter drift under different charging equipment, ambient temperatures, or battery aging conditions, resulting in insufficient reliability and robustness of safety monitoring.

Method used

An AI-based approach is adopted to extract the latent feature vector of the charging state data through a neural network encoder. Combined with the local curvature deviation and spatial distance deviation of the state evolution trajectory, abnormal deviation sections are identified. The compensation direction vector and current compensation amount are calculated through cross-parameter transmission relationship to generate control commands.

Benefits of technology

It enables precise monitoring of the charging process, early identification of minor anomalies, dynamic adaptation to different scenarios, reduction of false alarm rate, optimization of control strategy response speed, and improvement of charging equipment lifespan and system stability.

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Abstract

The application provides an AI-based charging process safety monitoring method and system, relating to the technical field of charging safety monitoring, comprising obtaining charging state data by collecting current, voltage and temperature data of a charging device, extracting an implicit feature vector using a neural network encoder and projecting it to a feature space to obtain a state representation point, forming a state evolution trajectory from the representation points at consecutive time points and performing shape matching with a reference trajectory to identify an abnormal deviation section, extracting current and temperature data sequences from the charging state data according to the abnormal deviation identifier, performing sliding window sampling to calculate a correlation peak value to determine a conduction time delay amount and a conduction coefficient, and then determining a prediction time window according to the conduction time delay amount and performing phase space reconstruction to calculate a compensation direction vector and a current compensation amount, and generating a control instruction and sending it to the charging device. The application realizes high-precision identification and active compensation control of charging process abnormalities, significantly improving charging safety and equipment operation stability.
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