Probabilistic thermal runaway early warning method and system based on multi-modal three-dimensional thermal field

The three-dimensional thermal field probabilistic early warning method, which integrates multimodal data fusion and hierarchical response strategies, solves the problems of insufficient information and delayed response in lithium battery thermal runaway monitoring, and achieves high-precision early identification and rapid intervention, thereby improving the safety of energy storage systems.

CN121328280APending Publication Date: 2026-01-13SHAANXI WINDRIDERPOWER CO LTD +2
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Patent Information

Application Number
CN202511367226.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-24
Publication Date
2026-01-13

AI Technical Summary

Technical Problem

Existing lithium battery thermal runaway monitoring technologies suffer from insufficient information acquisition by single-mode sensors, inadequate fusion of multi-mode data, and a lack of adaptability in early warning methods, resulting in high false alarm rates and delayed responses, making it difficult to meet the needs of high-safety-level energy storage systems.

Method used

A multimodal three-dimensional thermal field probabilistic early warning method is adopted. By fusing RGB-D images, long-wave infrared images and gas sensing data, and combining soft Kalman alignment algorithm and neural radiation field voxel reconstruction algorithm, multi-physics field collaborative perception of thermal runaway precursors is realized. Based on the graded threshold triggering gradient response strategy, a closed-loop control is formed.

Benefits of technology

It improves the accuracy of early thermal runaway identification and the timeliness of intervention, reduces the false alarm rate, and provides a highly reliable intelligent lithium battery energy storage safety solution.

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Abstract

The invention discloses a probabilistic thermal runaway early warning method and system based on a multi-modal three-dimensional thermal field, and relates to the technical field of energy storage safety monitoring, and the early warning method comprises the specific steps: S100, multi-modal data collection; s200, carrying out time alignment processing; s300, feature fusion extraction is carried out; s400, reconstructing a three-dimensional temperature field; s500, calculating a thermal runaway probability; according to the method, multi-modal data of RGB-D, long-wave infrared and gas sensors are fused, space-time synchronization is realized in combination with a soft Kalman alignment algorithm, the limitation of single physical quantity monitoring is broken through, cross-modal correlation features are extracted by adopting a pyramid cross attention adversarial network, and the multi-modal data of the RGB-D, the long-wave infrared and the gas sensors are subjected to hierarchical linkage response. The generated multi-scale feature tensor can accurately describe the spatio-temporal evolution law of a thermal runaway precursor, after lightweight neural radiation field voxel reconstruction, millimeter-level dynamic updating of a three-dimensional temperature field is realized, the generation and diffusion process of local hot spots in an energy storage cabin can be captured in real time, and a high-fidelity thermal field portrait is provided for early risk identification.
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