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primary topic: Enhancing fault detection in new energy vehicles via novel ensemble approach

Enhancing fault detection in new energy vehicles via novel ensemble approach
type: AcademicArticle
type: Article
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
volume: 16
issue: 1
is part of: Scientific Reports
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Enhancing fault detection in new energy vehicles via novel ensemble approach (Texto)
has document: Enhancing fault detection in new energy vehicles via novel ensemble approach (Otro)
subject: Engineering

Enhancing fault detection in new energy vehicles via novel ensemble approach (Texto)
type: Document
has file: s41598-025-29667-y.pdf
has part: s41598-025-29667-y.pdf

Scientific Reports
type: Collection
issn: 20452322

Umair Shahid
type: Person
given name: Umair
family name: Shahid

Irene Delgado Noya
type: Person
given name: Irene
family name: Delgado Noya

Kashif Munir
type: Person
given name: Kashif
family name: Munir

Ali Raza
type: Person
given name: Ali
family name: Raza

Santos Gracia Villar
type: Person
given name: Santos
family name: Gracia Villar

Iqra Akhtar
type: Person
given name: Iqra
family name: Akhtar

Mahnoor Nabeel
type: Person
given name: Mahnoor
family name: Nabeel

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Enhancing fault detection in new energy vehicles via novel ensemble approach (Otro)
type: Document
is version of: Enhancing fault detection in new energy vehicles via novel ensemble approach (Texto)
is volatile version of: Enhancing fault detection in new energy vehicles via novel ensemble approach (Texto)
is index codes version of: Enhancing fault detection in new energy vehicles via novel ensemble approach (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #27156 Enhancing fault detection in new energy vehicles via novel ensemble approach
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primary topic: Enhancing fault detection in new energy vehicles via novel ensemble approach

Engineering
type: Concept
preferred label: Ingeniería @es