Enhancing fault detection in new energy vehicles via novel ensemble approach

Akhtar, Iqra and Nabeel, Mahnoor and Shahid, Umair and Munir, Kashif and Raza, Ali and Delgado Noya, Irene and Gracia Villar, Santos and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irene.delgado@uneatlantico.es, santos.gracia@uneatlantico.es, UNSPECIFIED (2026) Enhancing fault detection in new energy vehicles via novel ensemble approach. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Transportation, New energy vehicles, Fault detection, Deep learning, Sensor data, NEV reliability, Ensemble learning
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	04 Feb 2026 09:36
Last Modified:	04 Feb 2026 09:36
URI:	http://repositorio.funiber.org/id/eprint/27156

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