Vibration noise reduction in high-speed dual-structure six-phase switched reluctance motor via magnetic circuit analysis and genetic algorithm-optimized back propagation for slot design
Restricted accessResearch articleFirst published online July, 2026
Vibration noise reduction in high-speed dual-structure six-phase switched reluctance motor via magnetic circuit analysis and genetic algorithm-optimized back propagation for slot design
The high-speed dual-structure six-phase switched reluctance motor (HDSSRM) exhibits pronounced vibration and acoustic noise, arising from inherent characteristics of the switched reluctance motor (SRM) and the non-uniform circumferential arrangement of its dual-structure six-phase configuration. This paper first analyzes the magnetic flux circuits to compare the effects of long and short magnetic circuits on radial force and operating conditions, and then mitigates the radial force associated with the short magnetic circuit. Next, the Maxwell stress tensor method is employed to examine how modifications to the rotor tooth geometry and the introduction of slots at the stator tooth tips reduce the radial magnetic pull force, and the slot parameters are optimized using a genetic algorithm–optimized back-propagation (GA-BP) neural network. Finally, experiments validate the proposed noise-reduction strategy, and finite-element analysis is used to compare the motor’s torque and radial-force characteristics.
WangYLiPRenG.Electric vehicles with in-wheel switched reluctance motors: coupling effects between road excitation and the unbalanced radial force. J Sound Vib2016; 372: 69–81.
2.
LiHBilginBEmadiA.An improved torque sharing function for torque ripple reduction in switched reluctance machines. IEEE Trans Power Electron2019; 34(2): 1635–1644.
3.
FurqaniJWigunaCAChibaA, et al. Experimental verification of acoustic noise and radial force sum variation in switched reluctance motor. IEEE Trans Ind Appl2021; 57(3): 2481–2493.
4.
CallegaroADBilginBEmadiA.Radial force shaping for acoustic noise reduction in switched reluctance machines. IEEE Trans Power Electron2019; 34(10): 9866–9878.
5.
FurqaniJKawaMWigunaCA, et al. Current reference selection for acoustic noise reduction in two switched reluctance motors by flattening radial force sum. IEEE Trans Ind Appl2019; 55(4): 3617–3629.
6.
GundogmusOElaminMYasaY, et al. Acoustic noise mitigation of switched reluctance machines with windows on stator and rotor poles. IEEE Trans Ind Appl2020; 56(6): 6224–6233.
7.
JianLIXueguanSONGYunhyunCHO. Comparison of 12/8 and 6/4 switched reluctance motor: noise and vibration aspects. IEEE Trans Magn2008; 44(11): 4131–4134.
8.
KiyotaKKakishimaTChibaA. Cylindrical rotor design for acoustic noise and windage loss reduction in switched reluctance motor for HEV applications. In: 2014 IEEE energy conversion congress and exposition (ECCE), 2014, pp.1814–1821. Pittsburgh, PA: IEEE, http://ieeexplore.ieee.org/document/6953638/ (accessed 20 May 2025).
9.
CastanoSMBilginBFairallE, et al. Acoustic noise analysis of a high-speed high-power switched reluctance machine: frame effects. IEEE Trans Energy Convers2016; 31(1): 69–77.
10.
GanCWuJShenM, et al. Investigation of skewing effects on the vibration reduction of three-phase switched reluctance motors. IEEE Trans Magn2015; 51(9): 1–9.
11.
GaoZWangSAnZ, et al. Numerical investigation on vibration performance of an improved switched reluctance machine with double auxiliary slots. Shock Vib2021; 2021(1): 8976731.
12.
LukmanGFAhnJW.Radial force reduction method of switched reluctance motor with partial holes in the rotor. J Electr Eng Technol2022; 17(3): 1775–1784.
13.
FurqaniJKawaMKiyotaK, et al. Current waveform for noise reduction of switched reluctance motor in magnetically saturated condition. In: 2016 IEEE energy conversion congress and exposition (ECCE), 2016, pp.1–7. Milwaukee, WI: IEEE, http://ieeexplore.ieee.org/document/7854981/ (accessed 20 May 2025).
14.
KawaMKiyotaKFurqaniJ, et al. Acoustic noise reduction of a high efficiency switched reluctance motor for hybrid electric vehicles with novel current waveform. In: 2017 IEEE international electric machines and drives conference (IEMDC), 2017, pp.1–6. Miami, FL: IEEE, http://ieeexplore.ieee.org/document/8002137/ (accessed 20 May 2025).
15.
GundogmusODasSSozerY, et al. NVH performance improvement in switched reluctance machines by simultaneous radial force and torque ripple minimizations. IEEE Trans Transp Electrification2024; 10(4): 8019–8029.
16.
Klein-HesslingAHofmannADe DonckerRW.Direct instantaneous torque and force control: a control approach for switched reluctance machines. IET Electric Power Appl2017; 11(5): 935–943.
17.
ZhangMBahriIMiningerX, et al. A new vibration reduction control strategy of switched reluctance machine. In: 2017 IEEE international electric machines and drives conference (IEMDC), 2017, pp.1–6. Miami, FL: IEEE, http://ieeexplore.ieee.org/document/8002114/ (accessed 16 April 2025).
18.
PupadubsinRMecrowBCWidmerJD, et al. Smooth voltage PWM for vibration and acoustic noise reduction in switched reluctance machines. IEEE Trans Energy Convers2021; 36(3): 1578–1588.
19.
LeeDHPhamTHAhnJW.Design and operation characteristics of four-two pole high-speed srm for torque ripple reduction. IEEE Trans Ind Electron2013; 60(9): 3637–3643.
20.
Dorneles CallegaroALiangJJiangJW, et al. Radial force density analysis of switched reluctance machines: the source of acoustic noise. IEEE Trans Transp Electrification2019; 5(1): 93–106.
21.
GaoXNaRJiaC, et al. Multi- objective optimization of switched reluctance motor drive in electric vehicles. Comput Electr Eng2018; 70: 914–930.
22.
XuJHuangCCaoW, et al. Torque Ripple Control Strategy of switched reluctance motor based on BP Neural Network. J Phys Conf Ser2022; 2242(1): 012036.
23.
CaoFTanYCaiM.Sparse algorithms of random weight networks and applications. Expert Syst Appl2014; 41(5): 2457–2462.
24.
HuangXRaoYJingL.Vibration analysis of switched reluctance motor based on multi-objective genetic algorithm. J Electr Eng Technol2023; 18(2): 1003–1011.
25.
HuangCJiaRChenB, et al. Research on cogging torque reduction based on multi-parameter optimization of auxiliary slots and pole-arc coefficient. J Electr Eng Technol2025; 20(3): 1439–1449.
