0000-0002-5954-1675 About
Xinyu Du is a Technical Fellow in the Energy and Propulsion Systems Research Lab at the General Motors Global R&D Center in Warren, Michigan. His work focuses on vehicle health management and intelligent automotive systems, with an emphasis on scalable diagnostic and prognostic technologies that improve safety, reliability, and customer experience across GM’s global vehicle portfolio.
He received the B.Sc. and M.S. degrees in Automation from Tsinghua University, Beijing, China, in 2001 and 2004, respectively, and the Ph.D. degree in Electrical Engineering from Wayne State University, Detroit, Michigan, USA, in 2012. He joined General Motors Global R&D Center in 2010 and has since led or participated in more than ten research, engineering, and NSF GOALI projects, often at the intersection of advanced analytics, control, and large-scale embedded systems.
Dr. Du’s technical contributions span integrated starting systems, ECU/CAN networks, wiring harnesses and connectors, chassis systems (including EPS, rotor, bearing, and suspension), autonomous vehicle sensor alignment and SLAM (LiDAR/camera), and high-voltage/low-voltage battery management systems. He has developed several key enablers and frameworks for vehicle health management, including automatic data labeling systems, machine learning–based prognostics (Gaussian processes, convolutional neural networks, contrastive and federated learning), vehicle functional testers, and cloud/distributed/edge prognostics architectures for large connected fleets. In recent years, he has also been active in applying advanced artificial intelligence methods, including large language models (LLMs) and retrieval-augmented generation (RAG), to enhance knowledge management, engineering workflow automation, and decision support for vehicle health and autonomous systems.
His research has had significant production impact. Dr. Du’s work on integrated starting system prognosis has been implemented in more than 10 million GM vehicles, earning the Boss Kettering Award for best innovation at General Motors in 2015 and contributing to GM’s INFORMS Prize in 2016. He is the key developer and technical lead of GM’s online sensor alignment system, which underpins the 360° viewing system, Super Cruise, and other advanced driver assistance system (ADAS) features; this work received a Boss Kettering Award in 2023 and has become a core capability for high-precision perception and automated driving features in production. Overall, 29 of his inventions have been adopted in GM production across multiple vehicle platforms and regions.
Dr. Du has coauthored more than 60 peer-reviewed papers, 100+ patents and patent applications, and 11 GM internal inventions. Three of his papers in vehicle health management received Best Conference Paper Awards in 2019, 2020, and 2025, recognizing his contributions to data-driven diagnostics, prognostics, and fleet-level health management strategies. In 2020, he was selected as one of 100 outstanding early-career engineers to participate in the National Academy of Engineering Frontiers of Engineering Symposium, highlighting his multidisciplinary impact and leadership potential.
He has extensive editorial and professional service experience. Dr. Du served as an Associate Editor for the Journal of Intelligent and Fuzzy Systems (2012–2024) and IEEE Access (2018–2025), and currently serves as an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems (since 2022) and IEEE Transactions on Fuzzy Systems (since 2024). He has been repeatedly invited to serve as a panelist, session chair, and technical committee member, and to deliver keynote talks and demonstrations at international conferences, journals, and professional societies in the areas of vehicle health management, intelligent transportation systems, computational intelligence, and applied AI.
Employment
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General Motors Technical Fellow2013 - Present
Education
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Wayne State University Ph. D.2004 - 2010
Projects & Funding
Projects & funding information is unavailable.
Publications (110)
- Insights into anode loss of active material mechanisms and detection algorithms Save
- Lithium Plating Detection and Intensity Estimation Using Current Pulsation Save
- Prognostics of internal short circuit in lithium-ion batteries using a hybrid deep learning approach Save
- Industrial EV Battery Performance Evaluation: A Review From the Instrumentation Perspective Save
- Online Camera-to-Ground Targetless Calibration for Advanced Driver Assistance Systems Save
- Prognostics of Internal Short Circuit in Lithium-Ion Batteries -Literature Survey and Performance Evaluation Save
- Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles Save
- DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection Save
- Adaptive Prognostics Using Distance and Temporal Based Self-Assessment Save
- Methods and systems for camera to ground alignment Save