Cloud edge collaborative intelligent management method and device for energy storage battery

By integrating cloud-edge collaborative architecture with multiple algorithms, real-time and accurate estimation of energy storage battery status and fault diagnosis are achieved. This solves the problem of balancing real-time performance and optimization in existing technologies, improves the reliability and adaptability of management, and ensures the safety and economy of battery operation.

CN121749429APending Publication Date: 2026-03-27TRANSCEND COMM BEIJING
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Patent Information

Application Number
CN202511971431.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing energy storage battery management methods struggle to balance real-time performance, accuracy, and global optimization, failing to meet the demands for real-time battery status monitoring and rapid fault response. Furthermore, the lack of efficient algorithms supporting edge and cloud collaboration results in poor versatility in the data preprocessing stage, making it unsuitable for battery characteristics under different temperatures and aging conditions.

Method used

Adopting a cloud-edge collaborative architecture and combining multiple algorithm fusion designs, it combines real-time processing at the edge with global optimization in the cloud. It utilizes a battery state estimation algorithm cascaded with neural networks and Kalman filters and a twin structure feature encoding model for fault diagnosis. Combined with temperature and aging adaptive correction, it achieves real-time and accurate estimation of battery state and fault diagnosis.

Benefits of technology

It improves the overall performance of energy storage battery management, achieves a balance between real-time response and global optimization, enhances the accuracy and adaptability of state estimation and fault diagnosis, and ensures the safety and economy of battery operation.

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Abstract

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.
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