Dynamic Safety Monitoring and Active Safety Enhancement for a Lightweight Energy-Efficient Electric Vehicle
Pemantauan Keselamatan Dinamis dan Peningkatan Keselamatan Aktif pada Kendaraan Listrik Hemat Energi Berbobot Ringan
DOI:
https://doi.org/10.21070/ups.10397Keywords:
Dynamic Safety Monitoring, Energy Saving Electric Vehicles, Embedded Systems Architecture, Multi-Sensor IntegrationAbstract
Energy efficient electric vehicles are generally designed with lightweight structures and minimal system architectures to optimize power efficiency; however, the integration of embedded active safety systems remains limited. This study proposes an embedded dynamic safety monitoring framework based on multi-sensor integration to enhance the active safety capability of a lightweight prototype electric vehicle. The system integrates an accelerometer–gyroscope module and a vibration sensor within a microcontroller-based architecture to perform three-axis acceleration data acquisition and real-time dynamic condition analysis using a threshold-based classification algorithm.
Unlike conventional accident detection approaches that focus on post-event response, the proposed system is designed as an active safety enhancement mechanism through continuous monitoring of vehicle dynamic behavior. The implementation was carried out on the Jayandaru EV prototype developed by the IMEI Team. Experimental results indicate that the dual-verification configuration improves detection reliability and minimizes misclassification caused by transient vibration fluctuations.
Downloads
References
I. Anshory, F. Teknik, and U. Muhammadiyah, “Performance Analysis Stability Of Speed Control Of BLDC Motor Using PID-BAT Algorithm In Electric Vehicle,” vol. 1, no. 1, pp. 22–28, 2017.
A. Y. F. Hidavat et al., “Implementation of MPU6050 Module Based On ROS and PID Controller as Stabilization Control and Rotational Motion of SAR Robot,” in 2024 International Conference on Electrical and Information Technology (IEIT), 2024, pp. 121–126. doi: 10.1109/IEIT64341.2024.10763060.
C. Cholilurrohmana, I. Sulistiyowati, and A. Wicaksana, “System Telemetry for Mobile Devices Using the GPS Neo-6M and DHT11 Modules A Case Study by IMEI Team,” JEEMECS (Journal Electr. Eng. Mechatron. Comput. Sci., vol. 6, no. 2, pp. 57–66, 2023, doi: 10.26905/jeemecs.v6i2.9945.
M. Pokydko, O. Oliinyk, and V. Tymchenko, “MEMS Gyroscope Based on MPU-6050 Sensor and ATmega328 Microcontroller,” in 2024 IEEE 7th International Conference on Smart Technologies in Power Engineering and Electronics (STEE), 2024, p. TT3.39.1-TT3.39.6. doi: 10.1109/STEE63556.2024.10748180.
I. E. Paromtchik, M. Perrollaz, and C. Laugier, “Fusion of telemetric and visual data from road scenes with a lexus experimental platform,” in 2011 IEEE Intelligent Vehicles Symposium (IV), 2011, pp. 746–751. doi: 10.1109/IVS.2011.5940571.
J. R. Chandiramani, S. Bhandari, and S. A. Hariprasad, “Vehicle Data Acquisition and Telemetry,” in 2014 Fifth International Conference on Signal and Image Processing, 2014, pp. 187–191. doi: 10.1109/ICSIP.2014.35.
N.-T. Hoang, H.-P. Vo, P.-T. Le, C.-L. Tran, N.-D. Trinh, and T.-A.-D. Pham, “The Innovative Design of the Electric Vehicles for Shell Eco-Marathon Asia Contest,” in 2022 6th International Conference on Green Technology and Sustainable Development (GTSD), 2022, pp. 296–302. doi: 10.1109/GTSD54989.2022.9988988.
F. Anugreni, D. N. Ilham, M. K. Harahap, P. A. Selatan, P. G. Medan, and U. Asahan, “Implementation of the Internet of Things on a Wheelchair using the MPU6050 Sensor,” vol. 1, no. 1, pp. 28–33, 2023.
S.-T. Hsieh and C.-L. Lin, “Fall Detection Algorithm Based on MPU6050 and Long-Term Short-Term Memory network,” in 2020 International Automatic Control Conference (CACS), 2020, pp. 1–5. doi: 10.1109/CACS50047.2020.9289769
Downloads
Additional Files
Posted
License
Copyright (c) 2026 UMSIDA Preprints Server

This work is licensed under a Creative Commons Attribution 4.0 International License.
