Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models.
Contents

• Background and State of the Art
• Diagnosis of Electrical Faults in Electric Machines
• Diagnosis of Mechanical Faults in Electric Machines

Target Groups

• Researchers and students of mechanical engineering, especially automotive powertrains in electric vehicles
• Research and development engineers in this field

About the AuthorTunan Shen did his PhD project at the Institute of Automotive Engineering (IFS), University of Stuttgart, Germany. Currently he is Software Developer for Cross Domain Computing Solutions at a German automotive supplier.

Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models.