Windings For Permanent Magnet Machines Yao Duan R

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Windings For Permanent Magnet Machines Yao Duan, R. G. Harley and T. G. Habetler

Windings For Permanent Magnet Machines Yao Duan, R. G. Harley and T. G. Habetler Georgia Institute of Technology 1

OUTLINE • Introduction • Overall Design Procedure • Analytical Design Model • Optimization •

OUTLINE • Introduction • Overall Design Procedure • Analytical Design Model • Optimization • Comparison • Conclusions 2

Introduction • The use of permanent magnet (PM) machines continues to grow and there’s

Introduction • The use of permanent magnet (PM) machines continues to grow and there’s a need for machines with higher efficiencies and power densities. • Surface Mount Permanent Magnet Machine (SMPM) is a popular PM machine design due to its simple structure, easy control and good utilization of the PM material 3

Distributed and Concentrated Winding Distributed Winding(DW) • Advantages of CW § § § Modular

Distributed and Concentrated Winding Distributed Winding(DW) • Advantages of CW § § § Modular Stator Structure Simpler winding Shorter end turns Higher packing factor Lower manufacturing cost Concentrated Winding(CW) • Disadvantages of CW More harmonics § Higher torque ripple § Lower winding factor Kw § 4

Overall design procedure Challenge: developing a SMPM design model which is accurate in calculating

Overall design procedure Challenge: developing a SMPM design model which is accurate in calculating machine performance, good in computational efficiency, and suitable for multiobjective optimization 5

Surface Mount PM machine design variables and constraints • Stator design variables § Stator

Surface Mount PM machine design variables and constraints • Stator design variables § Stator core and teeth • • • § Steel type Inner diameter, outer diameter, axial length Teeth and slot shape Winding • • Winding layer, slot number, coil pitch Wire size, number of coil turns • Major Constraints Flux density in stator teeth and cores § Slot fill factor § Current density § 6

Surface Mount PM machine design variables and constraints • Rotor Design Variables § §

Surface Mount PM machine design variables and constraints • Rotor Design Variables § § § Rotor steel core material Magnet material Inner diameter, outer diameter Magnet thickness, magnet pole coverage Magnetization direction Pole coverage • Major Rotor Design Constraints Flux density in rotor core § Airgap length § Radial Magnetization Parallel Magnetization 7

Current PM Machine Design Process • How commercially available machine design software works Manually

Current PM Machine Design Process • How commercially available machine design software works Manually input design variables Machine performance Calculation Output Meet specifications and constraints ? • Disadvantages: § § § Repeating process – not efficient and time consuming Large number of input variables: at least 11 for stator, 7 for rotor -- even more time consuming Complicated trade-off between input variables Difficult to optimize Not suitable for comparison purposes 8

Proposed Improved Design Process— reduce the number of design variables Magnet Design: • Permanent

Proposed Improved Design Process— reduce the number of design variables Magnet Design: • Permanent magnet material – Nd. Fe. B 35 § Magnet thickness – design variable § where Bm: average airgap flux density hm: magnet thickness Br : the residual flux density. g: the minimum airgap length, 1 mm mr: relative recoil permeability. kleak: leakage factor. kcarter: Carter coefficient. 9

Proposed Improved Design Process— reduce the number of design variables • Magnet Design: Minimization

Proposed Improved Design Process— reduce the number of design variables • Magnet Design: Minimization of cogging torque, torque ripple, back emf harmonics by selecting pole coverage and magnetization § Pole coverage – 83% § Magnetization direction. Parallel § 75 o 10

Design of Prototypes • Maxwell 2 D simulation and verification § Rated torque =

Design of Prototypes • Maxwell 2 D simulation and verification § Rated torque = 79. 5 Nm Transient simulation Concentrated winding Distributed winding Cogging Toque Peak-to-Peak value 4. 0 Nm = 5. 0 % of rated 4. 3 Nm = 5. 38% of rated Torque ripple Peak-to-Peak value 9. 2 Nm = 11. 25 % of rated 11. 3 Nm = 13. 75 % of rated 11

Design specifications and constraints Distributed winding Concentrated winding Slot number 12, 24, 36 (full

Design specifications and constraints Distributed winding Concentrated winding Slot number 12, 24, 36 (full pitched) 3, 6 (short pitched) Number of layers Double Flux density in teeth and back iron 1. 45 T (steel_1010) Covered wire slot fill factor Around 60% Around 80% Current density Around 5 A/mm 2 • Major parameters to be designed: Geometric parameters: Magnet thickness, Stator/Rotor inner/outer diameter, Tooth width, Tooth length, Yoke thickness § Winding configuration: number of winding turns, wire diameter § 12

Analytical Design Model - 1 • Build a set of equations to link all

Analytical Design Model - 1 • Build a set of equations to link all other major design inputs and constraints – analytical design model § With least number of input variables § Minimizes Finite Element Verification needed – high accuracy model 13

Analytical design model - 2 14

Analytical design model - 2 14

Analytical Design Model - 3 • Motor performance calculation § Active motor volume §

Analytical Design Model - 3 • Motor performance calculation § Active motor volume § Active motor weight § Loss • Armature copper loss • Core loss • Windage and mechanical loss § Efficiency § Torque per Ampere 15

Verification of the analytical model -1 • Finite Element Analysis used to verify the

Verification of the analytical model -1 • Finite Element Analysis used to verify the accuracy of the analytical model(time consuming) 16

Verification of the analytical model - 2 17

Verification of the analytical model - 2 17

Particle Swarm Optimization - 1 • The traditional gradient-based optimization cannot be applied §

Particle Swarm Optimization - 1 • The traditional gradient-based optimization cannot be applied § Equation solving involved in the machine model § Wire size and number of turns are discrete valued • Particle swarm § Computation method, gradient free § Effective, fast, simplementation 18

Particle Swarm Optimization - 2 § Objective is user defined, multi-objective function • One

Particle Swarm Optimization - 2 § Objective is user defined, multi-objective function • One example with equal attention to weight, volume and efficiency • Weight: typically in the range of 10 to 100 kg • Volume: typically in the range of 0. 0010 to 0. 005 m 3 • Efficiency: typically in the range of 0 to 1. 19

Particle Swarm Optimization - 3 • PSO is an evolutionary computation technique that was

Particle Swarm Optimization - 3 • PSO is an evolutionary computation technique that was developed in 1995 and is based on the behavioral patterns of swarms of bees in a field trying to locate the area with the highest density of flowers. x(t-1) inertia gbest(t) Pbest(t) v(t) 20

Particle Swarm Optimization - 4 Implementation • § 6 particles, each particle is a

Particle Swarm Optimization - 4 Implementation • § 6 particles, each particle is a three dimension vector: airgap diameter, axial length and magnet thickness § Position update where w: inertia constant pbest, n: the best position the individual particle has found so far at the n-th iteration c 1: self-acceleration constant gbest, n: the best position the swarm has found so far at the n-th iteration c 2: social acceleration constant 21

Position of each particle 22

Position of each particle 22

Output of particles Iteration No. 0 20 40 60 80 100 gbest Particle No.

Output of particles Iteration No. 0 20 40 60 80 100 gbest Particle No. 6 1 3 2 4 1 -6 Weight 37. 5 30. 3 30. 9 31. 7 31. 4 10000*Volume 53. 3 41. 62 40. 2 43. 0 42. 5 1000*(1 -eff) 37. 6 51. 2 50. 2 46. 9 Efficiency 96. 2% 94. 9% 95. 0 % 95. 4% 95. 3 % Objective 128. 4 123. 1 121. 3 121. 0 120. 9 23

Different Objective functions - 1 • Depending on user’s application requirement, different objective function

Different Objective functions - 1 • Depending on user’s application requirement, different objective function can be defined, weights can be adjusted • More motor design indexes can be added to account for more requirement where Wt. Magnet: weight of the permanent magnet, Kg Tper. A: torque per ampere, Nm/A 24

Different Objective Function - 2 Weight From obj 1 obj 2 31. 4 28.

Different Objective Function - 2 Weight From obj 1 obj 2 31. 4 28. 8 10000*Volum e 42. 5 1000*(1 -eff) 46. 9 Efficiency 95. 3% 95. 2% Objective 403. 4 From obj 1 obj 3 31. 4 31. 0 10000*Volume 42. 5 43. 4 Efficiency 95. 3% 95. 4% Wt. Magnet 0. 88 0. 92 Tper. A 3. 56 3. 58 Objective 94. 2 93. 8 Weight 47. 7 48. 2 384. 4 25

Comparison of two winding types • Objective function § obj 1 pays more attention

Comparison of two winding types • Objective function § obj 1 pays more attention to the weight and volume § obj 2 pays more attention to the efficiency and torque per ampere 26

Comparison of optimization Result Objective Function 1 CW Objective Function 2 DW CW DW

Comparison of optimization Result Objective Function 1 CW Objective Function 2 DW CW DW Des. 1 Des. 2 Weight / kg 28. 5 27. 9 30. 0 29. 4 32. 12 32. 39 32. 02 33. 23 Volume / m 3 0. 0031 0. 0032 0. 003 8 0. 0037 0. 0043 0. 0041 0. 0048 0. 0047 Efficiency 93. 3% 94. 7% 93. 7% 95. 1% 94. 9% 95. 9% Torque/Amper e (Nm/Arms) 2. 79 3. 54 2. 79 3. 74 3. 73 3. 75 Magnet Weight / kg 0. 685 0. 780 0. 95 0. 600 1. 48 1. 26 1. 12 1. 04 Obj. Function 122. 5 123. 2 134. 3 134. 4 56. 38 56. 42 52. 39 52. 17 • • CW designs have smaller weight and volume, mainly due to higher packing factor CW designs have slightly worse efficiency than DW, mainly due to short end winding 27

Conclusion • Concentrated winding has modular structure, simpler winding and shorter end turns, which

Conclusion • Concentrated winding has modular structure, simpler winding and shorter end turns, which lead to lower manufacturing cost • Before optimization, the torque ripples and harmonics can be minimized by careful design of the magnet pole coverage, magnetization and slot opening • Analytical design models have been developed for both winding type machines and PSO based multi-objective optimization is applied. This tool, together with user defined objective functions, can be used for analysis and comparison of both winding type machines and different applications • Optimized result shows CW design have superior performance than convention DW in terms of weight, volume, and have comparable efficiencies. 28

Acknowledgement • Financial support for this work from the Grainger Center for Electric Machinery

Acknowledgement • Financial support for this work from the Grainger Center for Electric Machinery and Electromechanics, at the University of Illinois, Urbana Champaign, is gratefully acknowledged. 29

Thanks! Questions and Answers 30

Thanks! Questions and Answers 30