TY  - JOUR
A1  - Akhtar, Iqra
A1  - Nabeel, Mahnoor
A1  - Shahid, Umair
A1  - Munir, Kashif
A1  - Raza, Ali
A1  - Delgado Noya, Irene
A1  - Gracia Villar, Santos
A1  - Ashraf, Imran
UR  - http://doi.org/10.1038/s41598-025-29667-y
N2  - New energy vehicles (NEVs) has emerged as a sustainable alternative to conventional vehicles, however have unresolved reliability challenges due to their complex electronic systems and varying operating conditions. Faults in drivetrain and battery systems, occurring at rates up to 12% annually, present significant barriers to the widespread adoption of NEVs. This study proposes a robust fault detection framework that applies multiple machine learning and deep learning models to address these challenges. The research utilizes the benchmark NEV fault diagnosis dataset, which contains real-world sensor data from NEVs. The models tested include logistic regression, passive-aggressive classifier, ridge classifier, perceptron, gated recurrent unit (GRU), convolutional neural network, and artificial neural network. The proposed ensemble GRULogX model stands out among the implemented model, leveraging GRU with logistic regression and other key classifiers, and achieved 99% accuracy, demonstrating high precision and recall. Cross-validation and hyperparameter optimization were adopted to further ensure the model?s generalizability and reliability. This research enhances the fault detection capabilities of NEVs, thereby improving their reliability and supporting the wider adoption of clean energy transportation solutions.
KW  - Transportation
KW  -  New energy vehicles
KW  -  Fault detection
KW  -  Deep learning
KW  -  Sensor data
KW  -  NEV reliability
KW  - 
Ensemble learning
AV  - public
IS  - 1
ID  - unic27156
SN  - 2045-2322
Y1  - 2026/01//
JF  - Scientific Reports
VL  - 16
TI  - Enhancing fault detection in new energy vehicles via novel ensemble approach
ER  -