EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 85 (2026) ITM Web Conf., 85 (2026) 03006 References
Open Access
Issue
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
Article Number 03006
Number of page(s) 5
Section Data Science, IoT, Optimization & Predictive Analytics
DOI https://doi.org/10.1051/itmconf/20268503006
Published online 09 April 2026
  1. P. S. Hou, R. Chen, Q. Shi, Q. Huang, and Y. Wang, "Energy harvest of multiple smart sensors with real-time fault detection," IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1592–1606, 2023. [Google Scholar]
  2. P. Wu, N. Lyu, Y. Song, X. Jiang, and Y. Jin, "Li-ion battery failure warning methods for energy-storage systems," Chinese Journal of Electrical Engineering, vol. 10, no. 1, pp. 86–100, 2023. [Google Scholar]
  3. V. S. R. Kosuru and A. K. Venkitaraman, "A smart battery management system for electric vehicles using deep learning-based sensor fault detection," World Electric Vehicle Journal, vol. 14, no. 4, p. 101, 2023. [Google Scholar]
  4. I. Rojek, D. Mikolajewski, A. Mroziñski, and M. Macko, "Machine learning- and artificial intelligence-derived prediction for home smart energy systems with PV installation and battery energy storage," Energies, vol. 16, no. 18, p. 6613, 2023. [Google Scholar]
  5. N. Rathika et al., "IoT-based automatic braking control system for electric vehicles and monitoring system." [Google Scholar]
  6. A. Rath and S. K. Lenka, "Smart speed control for EVs," Journal of Technical Education, p. 82. [Google Scholar]
  7. P. Mane et al., "Adaptive braking system for e-bikes using electromagnetic brakes," Computer Integrated Manufacturing Systems, vol. 29, no. 5, pp. 237–253, 2023. [Google Scholar]
  8. C. V S. Babu, N. S. Akshayah, and R. Janapriyan, "IoT-based smart accident detection and alert system," in Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT. Hershey, PA, USA: IGI Global, 2023, pp. 322–337. [Google Scholar]
  9. I. Khaleque et al., "IoT-based smart battery management and monitoring system for electric vehicles," AIUB Journal of Science and Engineering, vol. 22, no. 2, pp. 181–188, 2023. [Google Scholar]
  10. A. Jadhav, H. Rathod, and V. Kanade, "Smart battery parameter monitoring system for electric vehicles." [Google Scholar]
  11. R. Hemalatha, P. BharathiRajan, and R. Sathwik, "IoT-based automatic braking control system for EV vehicle and monitoring system," in Proceedings of the 2nd International Conference on Computer, Communication and Control (IC4), 2024. [Google Scholar]
  12. S. Bergies et al., "An IoT-based deep-learning architecture to secure automated electric vehicles against cyber-attacks and data loss," IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024. [Google Scholar]
  13. H. Jamil et al., "Analysis on the driving and braking control logic algorithm for mobility energy efficiency in electric vehicle," Smart Grids and Sustainable Energy, vol. 9, no. 1, p. 12, 2024. [Google Scholar]
  14. M. Sunil et al., "Automatic braking system and driver safety using Arduino Uno Atmega 328p," in Proceedings of the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024. [Google Scholar]
  15. M. Wagle et al., "Proactive electric vehicle braking system," in Proceedings of the 4th International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), 2024. [Google Scholar]
  16. Y. He et al., "Personalized automated braking for one-pedal driving in electric vehicles using model predictive control," IEEE Transactions on Transportation Electrification, 2024. [Google Scholar]
  17. G. Shini, J. L. Febin Daya, and P. Balamurugan, "IoT-based real-time monitoring of supercapacitors used in electric vehicles," Journal of Applied Research and Technology, vol. 22, no. 1, pp. 42–51, 2024. [Google Scholar]
  18. B. Dey et al., "Design, simulation, building, and testing of a microcontroller-based automatic drowsiness detection, vehicle braking, and alert system," Journal of Engineering Research and Reports, vol. 26, no. 3, pp. 208–223, 2024. [Google Scholar]
  19. S. D. Bensam et al., "An enhanced electric vehicle monitoring system using IoT," in Proceedings of the Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 2024. [Google Scholar]
  20. H. Dui et al., "IoT-enabled real-time traffic monitoring and control management for intelligent transportation systems," IEEE Internet of Things Journal, 2024. [Google Scholar]
  21. P. Wu, N. Lyu, Y. Song, X. Jiang, and Y Jin, "Li-ion battery failure warning methods for energy-storage systems," Chinese Journal of Electrical Engineering, vol. 10, no. 1, pp. 86–100, 2023. [Google Scholar]
  22. V. S. R. Kosuru and A. K. Venkitaraman, "A smart battery management system for electric vehicles using deep learning-based sensor fault detection," World Electric Vehicle Journal, vol. 14, no. 4, p. 101, 2023. [Google Scholar]
  23. I. Khaleque et al., "IoT-based smart battery management and monitoring system for electric vehicles," AIUB Journal of Science and Engineering, vol. 22, no. 2, pp. 181–188, 2023. [Google Scholar]
  24. G. Shini, J. L. Febin Daya, and P. Balamurugan, "IoT-based real-time monitoring of supercapacitors used in electric vehicles," Journal of Applied Research and Technology, vol. 22, no. 1, pp. 42–51, 2024. [Google Scholar]
  25. I. Rojek, D. Mikolajewski, A. Mroziñski, and M. Macko, "Machine learning- and artificial intelligence-derived prediction for home smart energy systems with PV installation and battery energy storage," Energies, vol. 16, no. 18, p. 6613, 2023. [Google Scholar]
  26. P. S. Hou et al., "Energy harvest of multiple smart sensors with real-time fault detection," IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1592–1606, 2023. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.