Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting more attention in the automotive industry with the technology improvement and increasing focus on fuel economy. For EVs and HEVs, especially all-wheel drive (AWD) EVs with two electric motors powering front and rear axles separately, an accurate motor speed measurement through resolver is significant for vehicle performance and drivability requirement, subject to resolver faults including amplitude imbalance, quadrature imperfection and reference phase shift. This paper proposes a diagnostic scheme for the specific type of resolver fault, amplitude imbalance, in AWD EVs. Based on structural analysis, the vehicle structure is analyzed considering the vehicle architecture and the sensor setup. Different vehicle drive scenarios are studied for designing diagnostic decision logic. The residuals are designed in accordance with the results of structural analysis and the diagnostic decision logic. Residual evaluation is performed by change detection technique. Simulation is done in MATLAB/Simulink based on residual design and residual evaluation to verify the diagnostic scheme with different vehicle drive scenarios. The simulation results demonstrate the proposed diagnostic strategy can detect and isolate the resolver fault with the wheel speed sensor fault for all the considered vehicle drive scenarios. The results from this paper can help design more robust powertrain control system for AWD EVs and the method can be extended to other EVs/HEVs with different vehicle architecture.


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    Titel :

    Motor Resolver Fault Diagnosis for AWD EV based on Structural Analysis


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Badreddine, Bader (Autor:in) / Rizzoni, Giorgio (Autor:in) / Boesch, Mathew (Autor:in) / Ahmed, Qadeer (Autor:in) / Li, Tianpei (Autor:in)

    Kongress:

    WCX World Congress Experience ; 2018



    Erscheinungsdatum :

    2018-04-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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