Dynamic Equivalent Magnetic Network Analysis of an Axial PM Bearingless Flywheel Machine

The multi-modal problem and high computational cost represent challenges in the optimization of electric machines owing to their highly nonlinear electromagnetic response. To overcome these challenges, this paper proposes a multi-fidelity model-based sequential optimization method in which both low-and highfidelity models are employed in two phases. In phase 1, the reluctance network (RN) is adopted as the lowfidelity model and mainly contributes to alleviating the abovementioned challenges. To overcome the low accuracy of the RN, the optimal design is obtained using a finite element model (FEM) in phase 2. The multistart strategy and gradient-based algorithm are utilized instead of a heuristic algorithm in all phases to avoid excessive calculations. This multi-fidelity model concept presents a novelty compared to previous research that focused only on algorithm development. The effectiveness of the proposed method is validated with two examples, consisting of the TEAM workshop problem 25 and the torque ripple minimization of an interior permanent magnet synchronous motor. The optimal designs and computational time resulting from the proposed method are compared with those of the conventional method, where only a FEM is used during optimization. The results show that the proposed method is remarkable in finding superior optimal designs, while ignoring unimportant local optima. Additionally, it can save up to 90% of the computational time required by the conventional method.INDEX TERMS Computational efficiency, electric machine, finite element model, multi-fidelity model, multi-modal problem, reluctance network.

Section: A Multi-fidelity Concept Including Reluctance Networkmentioning

confidence: 99%

Multi-Fidelity Model-Based Size Optimization of Electric Machines

Ahn

Min

2022

“…For the DPME machine in this paper, due to =0. where FSPM is the MMF generated by a stator PM; RSPM indicates the reluctance of a stator PM; Rg3 is the reluctance of the airgap right below a stator PM; Rg4 represents the reluctance of the flux lines starting from a stator PM and passing through a stator slot, which can be calculated with reference to [47][48][49]; Rg5 stands for the reluctance of the airgap right below a stator tooth.…”

Section: A Derivation Of Fr and Fr1mentioning

confidence: 99%

Quantitative Analysis of Back-EMF of a Dual-Permanent-Magnet-Excited Machine: Alert to Flux Density Harmonics Which Make a Negative Contribution to Back-EMF

Shi

Zhong

Jian³

2021

Recently, the dual-permanent-magnet-excited (DPME) machine has attracted growing attention due to its high torque density. Due to the bidirectional field modulated effect (BFME), airgap flux density harmonics (AFDHs) are more complex and abundant than traditional permanent magnet synchronous machines (PMSMs). Moreover, the back-electromotive force (EMF) generated by AFDHs is also complex. Unfortunately, only a few papers qualitatively analyze back-EMF. The qualitative analysis for back-EMF can reveal some important conclusions; for example, only AFDHs meeting specific pole pair numbers (PPNs) can generate back-EMF. However, it may also ignore some details and valuable findings. In this paper, a purely analytical magnetomotive force (MMF) permeance model (PAMPM) for a DPME machine is built to quantitatively analyze the back-EMF. The PAMPM does not require a numerical method, such as conformal transformation. With the PAMPM, AFDHs that contribute to the generation of back-EMF can be recognized and quantified. Interestingly, AFDHs with m2p2=np3 cause PM flux-linkage to have a dc bias, and not all AFDHs play a positive role in the generation of back-EMF. The main recognition results are as follows: 1) the S-II and R-II types of AFDHs in a 12/10 DPME machine overall make a negative contribution to the generation of back-EMF; and 2) AFDHs with PPN=22 in the two types mainly cause a negative contribution. To further verify the above results, 2D finite element simulation and experimental tests of a prototype machine are also conducted.

“…However, more representative MECs could be derived by discarding the previous assumptions. Of particular interest is the incorporation of the rotor/mover position in order to extend the study to the machine time-varying features [19], [36]. Further accuracy could be gained by a 3D discretization of the machine geometry into elementary MECs, allied to an increase of the CPU time required for the MEC resolution [37].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Effects of the Pole Shoe Geometry on the IPM T-LSM Features: Application to Free Piston Engines

Souissi

Abdennadher²,

Masmoudi³

2022

The paper is aimed at an analytical approach dedicated to the investigation of the effects of the pole shoe geometry on the no-load flux features of inner permanent magnet tubular-linear synchronous machines (IPM T-LSMs). It considers the most common geometries: (i) rectangular pole shoes, and (ii) trapezoidal pole shoes. The proposed approach combines two analytical models which are the magnetic equivalent circuit (MEC) and the permeance function. First, a basic MEC of an elementary part of the machine is considered to predict a preliminary air gap flux density trapezoidal waveform. Then, the accuracy of the proposed approach is enhanced by the incorporation of a mover permeance function that accounts for the PM spoke-type arrangement. The proposed approach is applied to the investigation of the impact of the mover pole shoe shape on the air gap flux density. The developed pole shoe sizing procedure makes it possible the selection of an IPM T-LSM with an appropriate mover geometry for which an investigation of the no-and on-load features is carried out by finite element analysis. The prototyping of the selected topology enables the experimental validation of the back-EMF and the load characteristic.INDEX TERMS Tubular-linear synchronous machines, inner permanent magnets, mover pole shoes, magnetic equivalent circuit, mover permeance function, no-load air gap flux density, back-EMF, cogging force, finite element analysis.