Paper Title: Comparative Analysis of GRU-RNN and LSTM Models for Battery Management System Visualization in Unreal Engine
Conference Name: Korean Institute of Communications and Information Sciences (KICS Winter 2024) Conference
Abstract: This paper introduces a novel approach to visualizing battery management systems (BMS) using Unreal Engine for Digital Twin Technology. The proposed platform leverages advanced data visualization techniques to monitor and predict battery performance in real-time. We present a comparative study between two predictive models, GRU-RNN and LSTM, for State-of-Charge (SOC) estimation. The 3D visualization in Unreal Engine provides an immersive experience for analyzing battery behavior, with color-coded visual cues representing different SOC levels. The results showcase the potential of our approach in facilitating efficient decision-making for battery optimization.
Status: Accepted
Conference Type: Domestic
Cite: Robin Matthew Medina, Jae-Min Lee, and Dong Seong Kim Comparative Analysis of GRU-RNN and LSTM Models for Battery Management System Visualization in Unreal Engine;, Korean Institute of Communications and Information Sciences (KICS Winter 2024) Conference, Yong Pyong Resort, Pyeongchang, Korea, Janua