Journal of Applied Science and Engineering

Published by Tamkang University Press

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Fault diagnosis of new energy vehicle system based on deep learning

 2026

 Volume 34

Zhengqian Wu

Hunan Mechanical & Electrical Polytechnic, Changsha, 410151 China

Received: May 05, 2026
Accepted: June 26, 2026
Publication Date: August 12, 2026

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Architecture of the Adaptive Gated Long Short-Term Memory (AG-LSTM) Unit

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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New energy vehicles (NEVs), powered by sustainable energy, are emerging as a cleaner alternative to conventional vehicles. Their performance and safety depend heavily on the health of critical components, making accurate fault diagnosis essential. This study proposes an advanced fault detection framework for NEVs, focusing on fault identification and classification in the drivetrain using deep learning techniques. Sensor data from electric vehicles, including current, voltage, motor speed, and environmental conditions, are pre-processed through normalization and missing-value imputation to ensure consistency. Feature extraction is performed using Wavelet Transform (WT) and Fast Fourier Transform (FFT) to capture both transient and steady-state behaviours. The proposed Enriched Crow Search Optimizer-based Adaptive Gated Long Short-Term Memory (ECS-AG-LSTM) model enhances LSTM adaptability and fault classification accuracy through optimization. Experimental results demonstrate superior performance, achieving 98.5% accuracy, 98.31% recall, 98.42% precision, 98.51% F1-score, and an R2 value of 0.956 . The findings confirm that ECS-AG-LSTM provides a robust and reliable solution for improving NEV safety and operational dependability.

Keywords: New Energy Vehicles (NEVs), Fault Diagnosis, Deep Learning (DL), Enriched Crow Search Optimizer-driven Adaptive Gated Long Short-Term Memory (ECS-AGLSTM), Sensor Data ion.

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