Research on the Challenges and Optimization Path of Fault Diagnosis Technology for Intelligent New Energy Vehicles

Abstract

With the continuous increase in the number of new-energy vehicle owners and the rapid development of intelligent connected functions, vehicle failure modes are gradually changing from traditional mechanical wear to complex anomalies caused by the combined effects of batteries, motors, electronic control systems, software and communication modules, etc. Fault diagnosis ability is now required to ensure the safety of the car and the user experience; otherwise, after-sales service will be affected. Currently, intelligent fault diagnosis for new-energy vehicles still faces several problems in its technological application, such as complex fault mechanisms, fragmented data resources, insufficient adaptability of diagnostic models, imperfect service collaboration mechanisms, unclear data security responsibility boundaries, and a shortage of multi-skilled maintenance personnel. Addressing these challenges, this article analyzes the operational logic and application value of intelligent new energy vehicle fault diagnosis, proposing a diagnostic optimization system based on data governance, supported by collaboration between the vehicle, cloud, and service ends, and guaranteed by standards, privacy protection, and talent cultivation. This system should cover status perception, anomaly identification, risk warning, maintenance decision-making, and feedback updates. The study argues that intelligent fault diagnosis should not be limited to single-point detection and fault code identification but should be integrated into the entire lifecycle management process of new energy vehicles, thereby improving vehicle safety management and the resilience of the after-sales service system.