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Fault Diagnosis for PMSM Drive System in Electric Vehicle

[+] Author Affiliations
Jiyu Zhang, Giorgio Rizzoni, Andrea Cordoba-Arenas

The Ohio State University, Columbus, OH

Paper No. DSCC2014-6116, pp. V002T36A006; 10 pages
doi:10.1115/DSCC2014-6116
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME

abstract

Electric and hybrid electric vehicles (EV/HEVs) have attracted considerable interest among automobile manufactures worldwide due to their advantages of better fuel economy. To guarantee safe, clean and reliable operation of electric drive systems, it is imperative to develop reliable and robust diagnostic schemes so that appropriate corrective actions can be taken in case a component or subsystem fail to operate normally. This paper proposes a diagnostic scheme for permanent magnet synchronous motor (PMSM) drives in EV/HEV applications. The proposed strategy uses two generalized observers to detect and isolate current, speed, and rotor position sensor faults in a PMSM drive. Since in real driving cases, the load torque is usually unknown due to unexpected road disturbance, the proposed diagnostic scheme uses an Unknown Input Observer (UIO) to estimate the load torque. The diagnostic algorithm is validated in Matlab/Simulink using the Ohio State University EcoCAR as testbed. The simulation results show that the proposed scheme is effective in detecting and isolating various sensor faults under road disturbance.

Copyright © 2014 by ASME

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