Deep learning-based EV battery fault diagnosis using RBBMO with CAR-TATNET detection approach

Ensuring the safety and dependability of power batteries has become a major concern due to the rapid expansion of electric vehicles (EVs), and fault detection has emerged as an essential approach for guaranteeing system stability. This study develops a Deep Learning (DL)-based defect prediction technique that combines an optimization algorithm and a Context-Aware Recurrent Neural Network with Adaptive Temporal Transformer (CAR-TATNet) to overcome these drawbacks. The aim is to reduce noise and improve the extraction of pertinent operating modes from EV battery signals. The dataset was first pre-processed by data cleaning and with Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Key time-frequency features linked to EV battery fault patterns were captured using Short-Time Fourier Transform (STFT). Inspired by the way magpies forage, the Red-Billed Blue Magpie Optimization (RBBMO) algorithm finds the most important traits for effective feature selection. Finally, the CAR-TATNet model was employed to model long-term dependencies in temporal sequences and adaptively respond to time-varying fault patterns. The developed methodology performs better than current methods, according to experimental validation in Python software, with a superior accuracy of 94.94%, providing a potential alternative for precise EV battery fault detection.