Vol. 14 No. 3 (2023): Special Issue on Advanced Diagnostics and Prognostics for Automotive Systems

This special issue focuses on theory and application of diagnostics and prognostics (DnP) analytics and methods for condition monitoring, anomaly detection, and health management of automotive systems. With elevated system complexity in electrified powertrains, connected vehicles, and automated driving; new DnP analytics and technologies will support accurate and reliable estimation and predictions of state of health for these complex vehicular systems. The increased capability of the vehicle to exchange data with passengers, surrounding infrastructure, and other vehicles has been both an enabler and provide motivation for developing advanced vehicle health management (VHM) features. Robust VHM systems can mitigate degradation of vehicle components by shifting operating points and even enable reconfigured operation until repairs can be made. At the same time, the development cycle for these new and more complex systems needs to be shortened in order to reduce development cost.
Recent advances in systems and analytics tools such as machine learning and cloud-based computation can leverage the classic model based DnP algorithms to address the complexities, and performance requirements of future VHM systems. Extensive researches have been done to analyze and predict performance of machine learning algorithms that is required for reliable modeling, estimation, and detection algorithms. Re-configurable and stochastic reinforcement learning techniques have also been successfully utilized in recent research to mitigate degradation in autonomous systems. Still, much work is needed to ensure robust performance of the vehicle with any newly developed component and algorithms. This special issue provides critical information on these important matters.  

Published: 2023-02-13

Technical Papers

Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks

Yuan-Ming Hsu, Dai-Yan Ji, Marcella Miller, Xiaodong Jia, Jay Lee
Abstract 791 | PDF Downloads 463 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3130

Ground Fault Diagnostics for Automotive Electronic Control Units

Xinyu Du, Shengbing Jiang, Dongyi Zhou, Alaeddin Bani Milhim, Hossein Sadjadi
Abstract 157 | PDF Downloads 210 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3128

Self-Adaptive Air-path Health Management for a Heavy Duty-Diesel Engine

Tomas Poloni, Paul Dickinson, Jianrui Zhang, Peng Zhou
Abstract 133 | PDF Downloads 122 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3118

Diagnostics-oriented Model for Automotive SCR-ASC

Kaushal Kamal Jain, Peter Meckl, Pingen Chen, Kuo Yang
Abstract 265 | PDF Downloads 185 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3129

Explainable Models for Multivariate Time-series Defect Classification of Arc Stud Welding

Sadra Naddaf Shargh, Mahdi Naddaf-Sh, Maxim Dalton, Soodabeh Ramezani, Amir R. Kashani, Hassan Zargarzadeh
Abstract 333 | PDF Downloads 250 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3125

Machine Learning Based Approach for EVAP System Early Anomaly Detection Using Connected Vehicle Data

Ala Omrani, Pankaj Kumar, Aed Dudar, Michael Casedy, Steven Szwabowski, Brandon Dawson
Abstract 168 | PDF Downloads 188 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3122

Technical Briefs


Special Issue on Advanced Diagnostics and Prognostics for Automotive Systems

Yilu Zhang, Rasoul Salehi, Shiyu Zhou, Xiaodong Jia, Jason Siegel
Abstract 345 | PDF Downloads 228 | DOI https://doi.org/10.36001/ijphm.2023.v14i3.3438