Desain Dan Optimasi Sudut Magnet V-Shape IPMSM Berbasis Genetic Algorithm Dan FEA Untuk Memaksimalkan Performa Motor Listrik

Kalengkongan, Audric Jethro (2026) Desain Dan Optimasi Sudut Magnet V-Shape IPMSM Berbasis Genetic Algorithm Dan FEA Untuk Memaksimalkan Performa Motor Listrik. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Interior Permanent Magnet Synchronous Motor (IPMSM) dengan rotor V-shape banyak dipilih untuk traksi kendaraan listrik karena padat daya, efisien, dan stabil di putaran tinggi. Sudut V-shape sangat menentukan aliran fluks magnetik dan respons torsi, sehingga berpengaruh langsung pada torsi rata-rata, cogging torque, torque ripple, dan efisiensi motor. Memilih sudut yang tepat adalah kunci untuk mendapatkan kinerja yang optimal dan efisien. Tujuan penelitian ini adalah mengoptimalkan sudut magnet V-shape pada IPMSM untuk memaksimalkan torsi dan efisiensi motor, sekaligus meminimalkan cogging torque dan torque ripple pada aplikasi traksi kendaraan listrik. Metodologi penelitian mengintegrasikan Finite Element Analysis (FEA) dan Genetic Algorithm (GA). Dengan cara melakukan simulasi FEA untuk 41 variasi sudut (130–170°) dengan parameter operasi tetap, mengekstrak data torsi, cogging torque, torque ripple, dan efisiensi. Data ini disimpan dalam database lookup table. Genetic Algorithm yang memanfaatkan database lookup table tanpa melakukan pengulangan menggunakan FEA, mencari sudut optimal berdasarkan fungsi objektif multi-kriteria Hasil optimasi menunjukkan bahwa sudut optimal adalah 133°, menghasilkan peningkatan signifikan dibandingkan baseline 138° dengan torsi meningkat 1,95%, cogging torque menurun 4,1%, efisiensi meningkat 0,21%, dan torque ripple terkontrol pada 6,35%. Kontribusi utama penelitian adalah optimasi sudut V-shape dari 138° menjadi 133° yang menghasilkan peningkatan performa motor dengan torsi 1,95%, efisiensi 0,21%, dan penurunan cogging torque 4,1%.
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Interior Permanent Magnet Synchronous Motor (IPMSM) with V-shaped rotors are widely chosen for electric vehicle traction applications due to their high power density, efficiency, and stability at high speeds. The V-shape angle strongly determines magnetic flux distribution and torque response, directly affecting average torque, cogging torque, torque ripple, and motor efficiency. Selecting the appropriate angle is the key to achieving optimal and efficient motor performance. The objective of this research is to optimize the V-shape magnet angle on an IPMSM to maximize torque and motor efficiency while simultaneously minimizing cogging torque and torque ripple in electric vehicle traction applications. The research methodology integrates Finite Element Analysis (FEA) and Genetic Algorithm (GA). The approach involves conducting FEA simulations for 41 angle variations (130–170°) at fixed operating parameters, extracting data on torque, cogging torque, torque ripple, and efficiency. This data is stored in a database lookup table. The Genetic Algorithm then leverages the database lookup table without incurring repeated FEA computational overhead, searching for the optimal angle based on a multi-objective fitness function. Optimization results demonstrate that the optimal angle is 133°, achieving significant improvements compared to the baseline 138° angle: torque increases by 1,95%, cogging torque decreases by 4.1%, efficiency improves by 0.21%, and torque ripple is controlled at 6.35%. The main contribution of this research is the optimization of the V-shape angle from 138° to 133°, resulting in significant motor performance improvement: torque increase of 1.95%, efficiency improvement of 0.21%, and cogging torque reduction of 4.1%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	IPMSM, V-shape angle, Genetic Algorithm, cogging torque, torque ripple, efisiensi, MotorXP-PM, FEA
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK4055 Electric motor
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21201-(S1) Undergraduate Thesis
Depositing User:	Audric Jethro Kalengkongan
Date Deposited:	04 Feb 2026 01:03
Last Modified:	04 Feb 2026 01:03
URI:	http://repository.its.ac.id/id/eprint/131941

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