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Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review (Short Paper)

Authors Stan Muñoz Gutiérrez , Adil Mukhtar , Franz Wotawa

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Stan Muñoz Gutiérrez
  • Institute of Software Technology, TU Graz, Austria
Adil Mukhtar
  • Institute of Software Technology, TU Graz, Austria
Franz Wotawa
  • Institute of Software Technology, TU Graz, Austria

Funding

This study was conducted within the framework of the project ARCHIMEDES, receiving funding from the Key Digital Technologies Joint Undertaking (KDT JU) - the Public-Private Partnership for research, development, and innovation under Horizon Europe – and National Authorities under Grant Agreement No 101112295 funded by the European Union and the FFG under Grant Agreement No FO999899377.

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Stan Muñoz Gutiérrez, Adil Mukhtar, and Franz Wotawa. Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.20

Abstract

Traditional onboard vehicle diagnostics are rapidly evolving concomitant to the rise of electrified powertrains, digital transformation, and intelligent technologies for advanced system management. The big data now available in modern vehicles offers unprecedented opportunities for condition monitoring and prognosis, but also presents challenges in scaling and integrating multimodal sensor data across components with varying timescale dynamics. Machine learning techniques have proven particularly effective in implementing diagnostic functions within electrified vehicle powertrains. This study systematically reviews intelligent, data-driven techniques for health monitoring and prognosis of electrified powertrains. We categorize existing research based on diagnostic functions and machine learning methods, with a focus on approaches that do not require prior knowledge of faulty operational states. Our findings indicate that deep learning methods are state-of-the-art across several diagnostic functions, fault modes, system levels, and multimodal sensor integration.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning algorithms
  • Hardware → Safety critical systems
  • Hardware → Failure recovery, maintenance and self-repair
Keywords
  • Diagnostic functions
  • Machine Learning
  • Powertrain
  • Electrified vehicles

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