基于异构令牌与选择性状态空间的锂电池故障诊断方法
By combining heterogeneous tokens with a selective state-space model, the problems of high computational complexity and insensitivity to early faults in lithium battery fault diagnosis are solved, achieving efficient and interpretable fault diagnosis, which is suitable for edge computing devices.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing lithium battery fault diagnosis technologies suffer from problems such as high computational complexity, insensitivity to early faults, and poor interpretability, making it difficult to efficiently process long sequences, multidimensional, and heterogeneous data with limited computing resources.
A fault diagnosis method based on heterogeneous tokens and selective state space is adopted. By materializing electrochemical mechanism knowledge, the method uses feature token mechanism and selective state space model to perform efficient and interpretable spatiotemporal feature extraction and fault evolution modeling, including real-time data acquisition, construction of heterogeneous feature sets, feature tokenization and selective state space model processing.
It achieves high-precision, real-time diagnosis of lithium battery faults under limited computing resources, significantly improving early fault detection capabilities and mechanism interpretability, and is suitable for real-time deployment of edge computing devices.
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Figure CN122410364A_ABST