Energy storage battery fault monitoring management method and system based on big data

By using sliding windows and long short-term memory networks in the energy storage battery management system to identify inconsistent energy output periods of battery cells and power compensation behaviors of adjacent cells, and combining the Bayesian estimation method to predict damage probability, the problem of difficulty in identifying potential faults and assessing damage risks in existing technologies is solved, and efficient fault warning and maintenance are achieved.

CN120703576AActive Publication Date: 2025-09-26GANZHOU KANGJIN ENERGY STORAGE TECHNOLOGY CO LTD
7 Cites 4 Cited by

Patent Information

Application Number
CN202510778118.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-11
Publication Date
2025-09-26
Estimated Expiration
2045-06-11

AI Technical Summary

Technical Problem

Existing energy storage battery management systems have difficulty accurately identifying potential faults in battery cells, especially when the fault signal is masked by the compensation behavior of adjacent cells. In addition, there is a lack of accurate assessment and prediction of the risk of battery cell damage, resulting in the failure to detect faults in a timely manner.

Method used

The operating parameters of the battery cells in the battery string are obtained through the battery management system, and the sliding window method is used to calculate the rate of change. Combined with the long-short-term memory network and Bayesian estimation method, the periods of inconsistent energy output and the power compensation behavior of adjacent cells are identified. A damage probability model is constructed, a fault probability curve is generated, and fault warning and maintenance strategies are formulated.

Benefits of technology

It realizes precise monitoring and intelligent management of energy storage battery packs, improves the accuracy and timeliness of fault warnings, and ensures the stability and safety of battery packs.

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

Abstract

The invention discloses an energy storage battery fault monitoring management method and system based on big data, and the method comprises the steps: calculating the change rate of a battery monomer between each window and an adjacent window, and recognizing an energy output inconsistent time period; identifying adjacent monomer power compensation behaviors in the energy output inconsistent time period; the load allocation rate, the deviation ratio and the heat consumption change amplitude of the compensated single battery in the compensation process are obtained; predicting the damage probability of the single battery in a plurality of periods in the future, and generating a fault probability curve; constructing a masked fault identification model, and identifying the fault state of the single battery; evaluating the fault level of the single battery, and constructing a fault level index table; and evaluating the validity of the fault early warning and maintenance strategy, and updating the fault early warning strategy and the health maintenance strategy. Through the technical method provided by the invention, accurate monitoring and intelligent management of the energy storage battery pack can be realized, the accuracy and timeliness of battery fault early warning are remarkably improved, and efficient and safe operation of an energy storage system is ensured.
Need to check novelty before this filing date? Find Prior Art