Real-Time Collaborative Control of 6G-Enabled Edge Computing in the Intelligent Manufacturing of New Energy Vehicles

  • Li Fuxiao BYD Auto Industry Company Limited, Shenzhen, Guangdong, China
DOI: https://doi.org/10.30560/sdr.v7n2p161
Keywords: 6G technology, edge computing, new energy vehicles, intelligent manufacturing, real-time collaborative control

Abstract

With the rapid development of the new energy vehicle industry, intelligent manufacturing has become the key to improving the competitiveness of the industry. 6G technology, with its excellent performance, such as ultra-high speed, ultra-low latency and ultra-large number of connections, has brought new opportunities for the application of edge computing in the intelligent manufacturing of new energy vehicles. This paper deeply studies the real-time collaborative control of edge computing enabled by 6G in the intelligent manufacturing of new energy vehicles, expounds the relevant technical background and advantages, analyzes the real-time collaborative control architecture and key technologies, and discusses the application effect in combination with actual cases, and looks forward to future development trends. The study shows that the integration of 6G and edge computing can significantly improve the real-time, collaborative and intelligent level of intelligent manufacturing of new energy vehicles, providing strong support for industrial development.

References

[1] Liu, G., Bilal, M., Xu, X., & Xia, X. (2025). Digital twins-assisted service recommendation with preference prediction in 6G-enabled edge computing. Communications Magazine, IEEE, 63(3), 54–60. https://doi.org/10.1109/MCOM.001.2300694
[2] Wang, C., Peng, J., & Huang, G. Z. (2024). AI-enabled spatial-temporal mobility awareness service migration for connected vehicles. IEEE Transactions on Mobile Computing, 23(4), 3274–3290. https://doi.org/10.1109/TMC.2023.3274682
[3] Ibn-Khedher, H., Laroui, M., Alfaqawi, M., Magnouche, A., Moungla, H., & Afifi, H. (2024). 6G-edge support of Internet of autonomous vehicles: A survey. Transactions on Emerging Telecommunications Technologies, 35(1), e4899. https://doi.org/10.1002/ett.4899
[4] Nivetha, A., & Preetha, K. S. (2025). Efficient joint resource allocation using self-organized map based deep reinforcement learning for cybertwin enabled 6G networks. Scientific Reports, 15(1), 1717. https://doi.org/10.1038/s41598-025-51717-7
[5] Yin, P., Liang, W., Wen, J., Kang, J., Chen, J., & Niyato, D. (2025). Multi-agent DRL for multi-objective twin migration routing with workload prediction in 6G-enabled IoV. IEEE Internet of Things Journal. Advance online publication. https://doi.org/10.1109/JIOT.2024.3479730
[6] Liu, X. (2022). Blockchain-enabled collaborative edge computing for intelligent education systems using social IoT. International Journal of Distributed Systems and Technologies, 13(1), 1–19. https://doi.org/10.4018/IJDST.313432
Published
2025-06-26
Issue
Vol 7 No 2 (2025)
Section
Articles
Creative Commons License

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

Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).