Lithium ion battery failure intelligent prediction system and method based on multi-source data fusion

Through multi-source data fusion and dynamic feature processing, the lithium-ion battery failure prediction system solves the problem of insufficient data islands and model generalization, realizes high-precision battery failure prediction and failure cause positioning, and improves the accuracy and efficiency of battery management.

CN120352768APending Publication Date: 2025-07-22CHANGDE COSPOWERS NEW ENERGY TECH CO LTD
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Patent Information

Application Number
CN202510359781.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-25
Publication Date
2025-07-22

AI Technical Summary

Technical Problem

There are problems in the prior art such as data islands, feature processing defects and insufficient generalization of model, resulting in insufficient accuracy and interpretability of lithium-ion battery failure prediction.

Method used

The multi-source data fusion module is used to integrate multiple data throughout the battery life cycle, combine the feature processor enhanced by domain knowledge and a composite machine learning model, and dynamic model update is achieved through the OTA upgrade interface, and the root cause analysis matrix is used for failure cause positioning.

Benefits of technology

The accuracy of lithium-ion battery failure prediction is improved by 12-15%, the false alarm rate is reduced to below 3%, and the precise positioning of the cause of failure is achieved, improving the adaptability and interpretability of the model.

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Abstract

The invention discloses a lithium ion battery failure intelligent prediction system and method based on multi-source data fusion, and belongs to the technical field of battery health management. Aiming at the problems of data islands, feature processing defects, insufficient model generalization and the like in the prior art, the invention provides a three-level innovation system: 1, a multi-dimensional database architecture; 2, a mixed feature processor; and 3, a composite prediction model. Dynamic updating of the model is realized through an OTA upgrade interface, the failure prediction accuracy can be improved by 12-15% and the false alarm rate can be reduced to 3% or below through actual measurement verification, meanwhile, an interpretable failure root cause positioning function is provided, the problems of misjudgment and missed judgment in battery safety early warning are effectively solved, and the safety early warning effect is improved. The method is suitable for battery intelligent operation and maintenance management of electric vehicles, energy storage power stations and other scenes.
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