| abstract |
The invention provides a thermal runaway fault classification and risk prediction method and system for a power lithium battery, including: a module-level power battery model fault injection method, a random fault generation and labeling method, and a power lithium-ion battery based on a deep learning method. A fault multi-classification model and a transfer learning method for applying the model to a real vehicle. The present invention can accurately express the real fault condition of the battery, and transfer it to a specific real vehicle working condition. The trained deep learning algorithm model can be successfully deployed into the real vehicle environment through mathematical processing and code conversion and perform real-time diagnosis of faults without increasing the extra computation of the battery management system, while achieving high estimation accuracy. |