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<p>This paper presents a model-reference adaptive system (MRAS) speed estimator based on a quasi-proportional-resonant (QPR) controller to enhance the position and speed estimation accuracy of a permanent magnet synchronous machine (PMSM)-driven electric vehicle (EV). Speed and position encoders increase the cost of EVs and make them prone to failure in harsh conditions. The MRAS is a widely used method for estimating speed and position, utilizing a reference model, estimation model, and adaption scheme. The MRAS employs a proportional-integral (PI) controller and low-pass filter (LPF) to facilitate its speed and position estimation adaptation mechanism in its conventional form. However, using a low-pass filter (LPF) in speed estimation can result in phase delay and drift issues, leading to inaccurate orientation of the flux and torque components. The QPR controller performs better than the PI control as it cancels harmonics and has zero phase shift at the resonance frequency. The PI and QPR controller results are obtained using a WAVECT WCU 300 rapid control prototyping (RCP) controller and analyzed to compare the performance for the different drive cycles.</p>

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Sensorless limp-home mode control of PMSM-driven electric vehicle using quasi-proportional resonant controller MRAS speed estimator

  • 期刊论文

  • 发表日期：

    2025年4月10日

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  • Sanjay Kumar Kakodia,
  • Giribabu Dyanamina

摘要

This paper presents a model-reference adaptive system (MRAS) speed estimator based on a quasi-proportional-resonant (QPR) controller to enhance the position and speed estimation accuracy of a permanent magnet synchronous machine (PMSM)-driven electric vehicle (EV). Speed and position encoders increase the cost of EVs and make them prone to failure in harsh conditions. The MRAS is a widely used method for estimating speed and position, utilizing a reference model, estimation model, and adaption scheme. The MRAS employs a proportional-integral (PI) controller and low-pass filter (LPF) to facilitate its speed and position estimation adaptation mechanism in its conventional form. However, using a low-pass filter (LPF) in speed estimation can result in phase delay and drift issues, leading to inaccurate orientation of the flux and torque components. The QPR controller performs better than the PI control as it cancels harmonics and has zero phase shift at the resonance frequency. The PI and QPR controller results are obtained using a WAVECT WCU 300 rapid control prototyping (RCP) controller and analyzed to compare the performance for the different drive cycles.