RESEARCH PAPER
Improving Electric Vehicle Maintenance by Advanced Prediction of Failure Modes Using Machine Learning Classifications
1
Environment Laboratory, Institute of Mines, Echahid Cheikh Larbi Tebessi University, Tebessa 12002, Algeria
Submission date: 2024-10-10
Final revision date: 2024-12-04
Acceptance date: 2025-02-12
Online publication date: 2025-02-21
Publication date: 2025-02-21
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):201372
HIGHLIGHTS
- Machine learning enhances predictive maintenance for electric vehicles.
- Advanced algorithms identify potential failure modes before they occur.
- Improved efficiency and reduced downtime through accurate failure predictions.
- Optimised maintenance schedules based on real-time vehicle performance data.
- Data-driven insights extend the lifespan of electric vehicle components.
KEYWORDS
TOPICS
ABSTRACT
This study presents a novel approach for analyzing failure modes and their effects in electric vehicles, offering an alternative to traditional methods. We employed machine learning techniques, enabling a significant shift in predictive maintenance. Various models, including Random Forest, Decision Trees, Logistic Regression, and Neural Networks, were tested, with Random Forest and Neural Networks achieving an impressive accuracy of 96.67%. This advancement enhances fault prediction, reduces operational costs, and minimizes downtime by integrating numerical and categorical data. The innovation of this study lies in combining multiple models to detect more complex patterns. Unlike previous studies that relied on simpler methods like decision trees or regression analysis, our multi-layered approach improves prediction accuracy by utilizing real-world data from electric vehicle sensors. This approach overcomes the limitations of simulated data and isolated models, significantly enhancing vehicle reliability, lifespan, and maintenance.
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CITATIONS (1):
1.
Comparative Analysis of AI-Driven Machine Learning Models for Fault Detection and Maintenance Optimization in Photovoltaic Systems
Abdellahi Moulaye Rchid, Moussa Attia, Mohamed Elmamy MOHAMED MAHMOUD, Vatma Elvally, Zoubir Aoulmi, Abdelkader Ould Mahmoud
Solar Energy and Sustainable Development Journal
Abdellahi Moulaye Rchid, Moussa Attia, Mohamed Elmamy MOHAMED MAHMOUD, Vatma Elvally, Zoubir Aoulmi, Abdelkader Ould Mahmoud
Solar Energy and Sustainable Development Journal
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