A Physics-Aware Attention LSTM Autoencoder for Early Fault Diagnosis of Battery Systems

Overview

The proposed autoencoder incorporates mileage-sensitive features and aging-aware calibration of LSTM memory. Evaluations on real electric-vehicle data examine early fault detection under dynamic operating noise and compare data-driven diagnostic baselines.