Artificial intelligence-driven energy storage system health state monitoring and early warning mechanism

Through the health status monitoring and early warning mechanism of the energy storage system driven by artificial intelligence, sensor networks and machine learning algorithms are used for intelligent analysis and early warning, solving the problems of single monitoring means and lagging early warning mechanism in the existing technology, and achieving comprehensive real-time monitoring and efficient early warning of the energy storage system.

CN119906157AInactive Publication Date: 2025-04-29GUANGDONG ZHONGYUNMEDIA TECH CO LTD
5 Cites 8 Cited by

Patent Information

Application Number
CN202510360789.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-26
Publication Date
2025-04-29
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing energy storage system monitoring methods rely on simple threshold judgment and manual experience, and there are problems such as single monitoring methods, lack of intelligent analysis capabilities, lagging early warning mechanisms, and insufficient decision-making support.

Method used

The health status monitoring and early warning mechanism of energy storage systems driven by artificial intelligence is adopted, including the data acquisition layer, the data processing layer, the artificial intelligence analysis layer, the decision support layer and the user interaction layer. Data is collected in real time through sensor networks, intelligent analysis is performed using machine learning and deep learning algorithms, optimization suggestions and early warnings are generated, and system reliability is improved through multi-level alarm mechanisms.

Benefits of technology

It realizes comprehensive real-time monitoring of the energy storage system, improves the accuracy and timeliness of abnormal detection, and implements fault prediction and early warning based on historical data and AI models, improving operational efficiency and system reliability.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides an artificial intelligence-driven energy storage system health state monitoring and early warning mechanism. The mechanism comprises five levels of data acquisition, processing, artificial intelligence analysis, decision support and user interaction. System operation data is collected in real time through a sensor network, and after cleaning and standardization, machine learning and deep learning algorithms are used for analysis. Based on the analysis results, the system provides optimization suggestions, fault diagnosis and predictive maintenance plans, and displays system status and sends alarms through a user interface. According to the invention, intelligent power grid optimization, battery health state monitoring and fault diagnosis early warning are realized, and through multi-source data fusion, real-time artificial intelligence analysis and adaptive learning, the efficiency and reliability of the energy storage system are improved, and the service life of the energy storage system is prolonged. The system can predict the energy demand, evaluate the health state of the battery and identify an abnormal mode, thereby actively making a maintenance plan, reducing accidental shutdown, and providing a comprehensive solution for intelligent management of the energy storage system.
Need to check novelty before this filing date? Find Prior Art