Study on the linkage between intelligent network connection network security mapping and power battery failure prediction for new energy vehicles based on digital twins

Abstract

Under the background of sustainable development, the new energy vehicle industry is booming, but the problems of smart grid network security and power battery failure are highlighted. This study introduces digital twin technology, aiming to solve the above key problems. A digital twin-based intelligent network security mapping system is constructed to assess risks and warnings by collecting data on network traffic, device status and user behaviour; a power battery failure prediction system is constructed to use high-precision sensors to collect data, and machine learning and deep learning algorithms to predict failures. The study also explores the linkage mechanism between the two, builds a unified data platform to integrate data from multiple sources, and employs multimodal deep learning algorithms to explore the potential correlation. It is found that cybersecurity and power battery failures interact with each other, e.g., cyberattacks can damage the battery management system, and power battery failures can create security loopholes. The linkage mechanism can comprehensively monitor management and improve risk response capability. This study provides a new path to improve the safety and reliability of new energy vehicles, which is of great significance to promote the development of new energy vehicle industry.