원문정보
초록
영어
This study proposes an intelligent prediction framework that links the Digital Twin (DT) to an edge Battery Management System (BMS) structure based on Federated Learning (FL) to enhance the fault prediction and real-time response capabilities of battery-based Internet of Things (IoT) systems. The proposed system locally learns a lightweight anomaly detection model using battery status data collected from an edge gateway and comprehensively updates the global model through federated learning. Additionally, the reliability of the prediction results is enhanced by verifying various fault scenarios through digital twin simulation. The experimental results demonstrate that the proposed technique outperforms both the existing centralized method and the general federated learning method in terms of prediction accuracy and alarm response time. This demonstrates that the combination of a field-based, distributed learning structure and a real-time verification system is effective in enhancing battery safety.
목차
1. Introduction
2. Related Work
3. System Model
3.1 Overview
3.2 Data organization and Computation structure
3.3 Problem Statement
4. Proposed Method
4.1 Failure prediction model architecture
4.2 Federated Learning Validation
4.3 Proposed algorithm
5. Performance Evaluation
5.1 Experimental environment
5.2 Experimental results
6. Conclusion
References