Battery Management System Voltage Curve Classification

The patent replaces manual visual inspection with an automated machine learning-based image processing system. The system captures voltage curve images, processes them through trained machine learning models, and automatically classifies battery modules as normal or anomalous, eliminating the need for manual inspection while maintaining detection accuracy.

The machine learning model is trained on historical voltage curve data to enable self-service anomaly detection. The system automatically learns patterns of normal and anomalous behavior, performs classification without human intervention, and continuously improves through feedback from additional training data.

The patent substitutes manual classification tasks with an automated machine learning system that processes voltage curve images rapidly. The system maintains classification accuracy by using trained models while increasing productivity through automated high-speed image processing and parallel analysis of multiple battery modules.

The machine learning model is pre-trained on extensive historical data containing both normal and anomalous voltage curves. This preliminary training enables the system to perform accurate classification immediately upon deployment, eliminating the need for manual training or calibration during operation.

The patent uses image processing to capture voltage curves as visual representations, creating a copy of the electrical data in a format suitable for machine learning analysis. This copying approach simplifies the integration of machine learning methods by converting electrical measurements into image data that can be processed using standard computer vision techniques.

The system segments the battery module evaluation process into distinct stages: voltage curve measurement, image capture, machine learning inference, and classification output. This segmentation allows each component to be optimized independently and facilitates integration with existing manufacturing systems.

The machine learning model automatically adapts to different anomaly types through its training process. By exposing the model to diverse examples of normal and anomalous voltage curves during training, the system develops the flexibility to detect various defect types consistently without human intervention or fatigue.

The system handles different anomaly types by changing the parameters and features analyzed in the voltage curve images. The machine learning model automatically adjusts its analysis based on the patterns learned during training, maintaining flexibility across different defect types while ensuring consistent detection reliability.