Regulation and control system and method based on heat energy of energy storage cabinet

By constructing a temperature distribution matrix and a thermal gradient vector, overheating risk areas are identified, airflow paths are dynamically generated, and guide vanes and fans are adjusted in a coordinated manner. This solves the problem of uneven heat distribution in the energy storage cabinet and achieves temperature balance and energy efficiency optimization.

CN121300527APending Publication Date: 2026-01-09JIANGSU SIBEIER ARMOR STRUCTURAL PARTS CO LTD
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Patent Information

Application Number
CN202511465549.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-14
Publication Date
2026-01-09

AI Technical Summary

Technical Problem

Under high heat load operation, energy storage cabinets are prone to triggering battery thermal runaway. Traditional heat dissipation designs have delayed response and uncertain heat accumulation locations, leading to decreased system efficiency and safety hazards. In particular, uneven heat distribution during outdoor deployment or operation in extreme environments affects stability.

Method used

By collecting temperature data from the energy storage cabinet, a temperature distribution matrix and thermal gradient vector are constructed to identify overheating risk areas. Combined with a thermal power load mapping model, an optimized airflow path diagram is dynamically generated, and the angle of the guide vanes and the fan speed are adjusted in a coordinated manner to achieve adaptive thermal energy regulation.

Benefits of technology

It achieves balanced and rapid suppression of internal temperature distribution in the energy storage cabinet, improves system stability and energy efficiency, adapts to various environmental conditions, and extends service life.

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Abstract

The invention discloses a regulation and control system and method based on heat energy of an energy storage cabinet, and belongs to the technical field of intelligent heat management. Temperature data in the energy storage cabinet are collected, a temperature distribution matrix is constructed, and a thermal gradient vector and an overheating risk area are extracted based on a difference algorithm; further establishing a thermal power load mapping model, and dynamically generating an air circulation path optimization diagram; the opening angle of a flow deflector and the rotating speed of a fan are adjusted in combination with an optimized path, and self-adaptive regulation and control of a thermal field in the cabinet body are achieved; meanwhile, a machine learning model is trained by utilizing historical operation data, an optimal regulation and control strategy is output, and an execution layer is driven to realize collaborative response of a fan and a flow deflector; the method has the advantages of high regulation and control precision, high response speed, low energy consumption and the like, and is suitable for heat management requirements of various types of energy storage cabinets.
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