AI-Based Multi-Fault Diagnostic Correlation Web Model for the Analysis of Electronic Vehicle Charging System

A sort of massive, high-power, package-loading electronics, the electric car charging battery exhibits flexibility and power-sharing traits. However, comparable battery failure signatures, sensor defects, and interaction issues frequently lead to misdiagnosis. Based on fault diagnostic systems and AI, this work implements the online multi-fault diagnostic correlation model (OM-FDCM) to quantify electronic vehicle charging. Indicators of errors and assessments of human error have been subjected to the technology of correlation coefficients. Battery problems can be discovered using techniques that analyse the relationship between subsequent voltages and failure signals. Furthermore, connection and sensor voltage faults separate the surrounding voltage differential and power correlation coefficients. The online multi-diagnostic can tolerate battery variations in temperature range, loading state, and health. Creating an integrated early warning system for electric car charging and discharging can significantly increase assessing safety and promote the long-term growth of electric vehicles. Power supply equipment, battery charging stacks and fault correlation are evaluated to build an integrated online diagnostic that may considerably enhance the safety of charging electronic cars. When compared to other methods, the simulation's outcomes perform better in terms of accuracy (94.7%), interaction (92.6%), safety (93.5%), probability (92.9%), reduced temperature (35.7%), voltage (26.9%), and energy consumption (22.8%).