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Enhancing PMSM Drive Performance for Electric Vehicles Through ANFIS-HCC Integration.

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Title: Enhancing PMSM Drive Performance for Electric Vehicles Through ANFIS-HCC Integration.
Authors: Sangar, Brijendra1 (AUTHOR) brijendar@gmail.com, Singh, Madhusudan1 (AUTHOR) madhusudan@dce.ac.in, Sreejeth, Mini1 (AUTHOR) minisreejeth@dce.ac.in
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Mar2026, Vol. 51 Issue 6, p7821-7834. 14p.
Subjects: Permanent magnet motors, Adaptive fuzzy control, Adaptive control systems, PID controllers, Electric vehicles, Field orientation principle
Abstract: The efficient operation of permanent magnet synchronous motors (PMSM) has been significantly improved by modern motor control algorithms. One such innovative application is PMSM motor control using adaptive neuro-fuzzy inference systems (ANFIS) control. For PMSM drive control, this integrates the benefits of fuzzy logic with neural networks, leading to improved dynamic performance, increased efficiency, and precise motor control. The incorporation of ANFIS control in the drive system enables field-oriented control (FOC) of PMSMs. To assess the drive's performance, we use stator voltage and torque equations under different speed and torque conditions. The performance metrics are compared between an ANFIS speed controller with hysteresis current control (HCC) and a conventional proportional-integral (PI) control integrated with HCC. This comparison illustrates the potential improvements in performance achieved by using ANFIS control over traditional PI control methods. The conventional PI gain settings are difficult to use due to PMSM's nonlinearity, which causes unwanted overshoot. It is discovered that the created and constructed ANFIS-HCC controller solves this issue and offers consistently improved performance characteristics. The suggested innovative controller design's improved dynamic properties and increased performance make it a viable option for new generation EVs, as demonstrated by simulation studies and experimental validation. [ABSTRACT FROM AUTHOR]
Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193141698
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PubType: Academic Journal
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  Data: Enhancing PMSM Drive Performance for Electric Vehicles Through ANFIS-HCC Integration.
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  Data: <searchLink fieldCode="DE" term="%22Permanent+magnet+motors%22">Permanent magnet motors</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+fuzzy+control%22">Adaptive fuzzy control</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22PID+controllers%22">PID controllers</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Field+orientation+principle%22">Field orientation principle</searchLink>
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  Label: Abstract
  Group: Ab
  Data: The efficient operation of permanent magnet synchronous motors (PMSM) has been significantly improved by modern motor control algorithms. One such innovative application is PMSM motor control using adaptive neuro-fuzzy inference systems (ANFIS) control. For PMSM drive control, this integrates the benefits of fuzzy logic with neural networks, leading to improved dynamic performance, increased efficiency, and precise motor control. The incorporation of ANFIS control in the drive system enables field-oriented control (FOC) of PMSMs. To assess the drive's performance, we use stator voltage and torque equations under different speed and torque conditions. The performance metrics are compared between an ANFIS speed controller with hysteresis current control (HCC) and a conventional proportional-integral (PI) control integrated with HCC. This comparison illustrates the potential improvements in performance achieved by using ANFIS control over traditional PI control methods. The conventional PI gain settings are difficult to use due to PMSM's nonlinearity, which causes unwanted overshoot. It is discovered that the created and constructed ANFIS-HCC controller solves this issue and offers consistently improved performance characteristics. The suggested innovative controller design's improved dynamic properties and increased performance make it a viable option for new generation EVs, as demonstrated by simulation studies and experimental validation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s13369-025-10509-y
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        Text: English
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      – SubjectFull: Adaptive fuzzy control
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      – SubjectFull: Adaptive control systems
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      – SubjectFull: PID controllers
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      – SubjectFull: Electric vehicles
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      – SubjectFull: Field orientation principle
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      – TitleFull: Enhancing PMSM Drive Performance for Electric Vehicles Through ANFIS-HCC Integration.
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              Text: Mar2026
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              Y: 2026
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