eprintid: 27156 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/02/71/56 datestamp: 2026-02-04 23:30:14 lastmod: 2026-02-04 23:30:15 status_changed: 2026-02-04 23:30:14 type: article metadata_visibility: show creators_name: Akhtar, Iqra creators_name: Nabeel, Mahnoor creators_name: Shahid, Umair creators_name: Munir, Kashif creators_name: Raza, Ali creators_name: Delgado Noya, Irene creators_name: Gracia Villar, Santos creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: irene.delgado@uneatlantico.es creators_id: santos.gracia@uneatlantico.es creators_id: title: Enhancing fault detection in new energy vehicles via novel ensemble approach ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Transportation, New energy vehicles, Fault detection, Deep learning, Sensor data, NEV reliability, Ensemble learning 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. date: 2026-01 publication: Scientific Reports volume: 16 number: 1 id_number: doi:10.1038/s41598-025-29667-y refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-025-29667-y access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Akhtar, Iqra; Nabeel, Mahnoor; Shahid, Umair; Munir, Kashif; Raza, Ali; Delgado Noya, Irene; Gracia Villar, Santos y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, santos.gracia@uneatlantico.es, SIN ESPECIFICAR (2026) Enhancing fault detection in new energy vehicles via novel ensemble approach. Scientific Reports, 16 (1). ISSN 2045-2322 document_url: http://repositorio.unincol.edu.co/id/eprint/27156/1/s41598-025-29667-y.pdf