@article{1444961,
      recid = {1444961},
      author = {Shen, Tunan,},
      title = {Diagnosis of the powertrain systems for autonomous  electric vehicles /},
      pages = {1 online resource :},
      abstract = {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  Author Tunan 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.},
      url = {http://library.usi.edu/record/1444961},
      doi = {https://doi.org/10.1007/978-3-658-36992-7},
}