Digital Twinning of an Electric Two-Wheeler for Real-Time Battery State Estimation and Augmented Reality Visualization
Augmented Reality; battery management system; Digital twin; electric two-wheeler; ESP32; Internet of Things; LiFePO4 battery; Model Viewer; range estimation; State of Charge; State of Health; ThingSpeak
This paper describes the design and structure of a digital twin system for an electric two-wheeler. It enables real-time battery state estimation and augmented reality visualization. Using digital twin technology, the system improves battery monitoring, rider experience, and operational efficiency through real-time simulation and optimization. The structure includes a physical sensing layer, an ESP32-based processing unit, cloud-based data management using ThingSpeak, and an AR front-end. It tackles key challenges in estimating the state of LiFePO4 batteries, such as voltage plateau behavior, temperature sensitivity, and accuracy of algorithms. The system also considers the limits of microcontroller-based sensing and data interfacing, ensuring reliable performance in real-time. 2025 IEEE.
Uthaman, Ujwal; Dsilva, Alan Chris; Johny, Danny; Parag Jose, C.; Venkataswamy, R.; Wang, Yong
2025 IEEE International Conference on Vehicular Technology and Transportation System, ICVTTS 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICVTTS67119.2025.11296457 https://www.scopus.com/pages/publications/105032070394?origin=resultslist
Uthaman U., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Dsilva A.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Johny D., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Parag Jose C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Venkataswamy R., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Wang Y., Binghamton University, New York, United States