Design Optimization of Traction Electric Machines for EV and HEV Perspectives from Compact to Racing Cars

Transcription

1 Design Optimization of Traction Electric Machines for EV and HEV Perspectives from Compact to Racing Cars Dan M. Ionel, Ph.D., IEEE Fellow CWIEME Chicago, October 4, 2017 Introduction February,

2 Outline Introduction PEIK and at University of Kentucky Design optimization with CAI Differential Evolution (DE) Hybrid Electric Vehicle (HEV) optimization examples Rated performance Influence of cooling Design for driving cycle Without (ferromagnetic) core In-wheel axial flux coreless machines for EV solar PV racing cars Cored vs. coreless Flux vs. current weakening Without magnets Lucid Motors: new luxury EV, induction motor, multi-physics analysis NASA and OSU: 10MW ring electric motor for aircraft engines Conclusion. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

3 Dan M. Ionel, Ph.D., FIEEE Dan M. Ionel is Professor and L. Stanley Pigman Chair in Power at University of Kentucky (UK). At UK he also serves as the Director of the Power and Energy Institute of Kentucky (PEIK) and of the. Previously, he worked in industry for more than 25 years, most recently as Chief Engineer for Regal Beloit Corp., and before that as the Chief Scientist for Vestas Wind Turbines. He contributed to technology developments with long lasting industrial impact, designed machines and drives with ratings between and 10,000hp, published more than 150 technical conference and journal papers, including 5 winners of IEEE Paper Awards, and holds more than 30 patents, including a medal winner at the Geneva Invention Fair. Dr. Ionel is an IEEE Fellow, was the Chair of the IEEE Power and Energy Society Electric Motor Subcommittee, the General Chair of IEEE IEMDC 2017 Conference, and is currently the Chair of the IEEE WG for 1812 Test Guide revision. dan.ionel@uky.edu Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

4 and PEIK at University of Kentucky (UK) UK enjoys a longstanding tradition in electric machines and drives Early developments on linear and PM motors, and vector control Many learned machines using the Nasar and Boldea text books PEIK, Power and Energy Institute of Kentucky, launched with DOE grant in 2010 Core faculty in electric power engineering and many others in related fields Endowment established and inaugural L. Stanley Pigman Chair started in 2015 and other laboratories; faculty: 10+; graduate research students: 60+ Strategic collaborators include ANSYS Inc., NREL, Center for Applied Energy Research (CAER) at UK and others. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

5 Systematic Optimization and Comparative Studies Multi-objective optimization problems, for example: minimum cost, and maximum efficiency (min. losses), and minimum torque ripple Etc. many-many objectives Evolutionary process hundreds of generations thousands of candidate designs Aims establish Pareto-sets and fronts, i.e. best compromise designs systematic comparison of different solutions, e.g. design topologies. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

6 Differential Evolution Optimization Randomly select three designs Apply crossover and mutation Crossover: Mutation: if (rand(0,1) x Cr + F( x x 2 ) r0 r1 r ) Selection: Single objective Multi-objective (Pareto-dominance) Repeat until convergence Objective function evaluation (model execution) Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

7 Ultra-fast Time Stepping Electromagnetic FEA Traditional approach solve all time steps sequentially: Bigger Faster t 000 t 1 t 2 t 3 t 4 t n Higher Fidelity New approach (TDM) solve all time steps simultaneously: t 0 t 1 t 2 t 3 t 4 t n Courtesy of ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

8 Past Example Optimization for Formula E Racing Cars Published paper: A. Fatemi, D. M. Ionel, M. Popescu, N. A. O. Demerdash, Design Optimization of Spoke-Type PM Motors for Formula E Racing Cars, IEEE ECCE Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

9 Single Point Performance Optimization for Reference Design Rating: 400Nm 1,500rpm (typical peak for HEV automobiles) Topology: 48-slot 8-pole IPM Set values: 22.5 Arms/mm2, slot fill factor Independent Variables: ten (10) total for stator and rotor geometry Objectives: Minimum losses and Minimum cost of active materials Constraints: Torque ripple < 15% Magnet B_min > 0.3 Br Optimization method: CMODE Differential Evolution (DE) based Generations: 60; members per generation 80. x D k si g K wt k wtt k dpm k wpm k wq h PM α PM d y Description R si /R so Air-gap w T /Slot-pitch tip/slot-opening d PM /d PM,max w PM /w PM,max w q /w q,max PM height Magnet pole arc Yoke depth Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

10 Possible Improvements through AI DE Optimization G59M12 Reference Design G59M12 Reference Active material cost [pu] 93.4 Stator losses [W] 5680 Input voltage THD [%] 11 Torque ripple [%] 14 B PM,min [T] Torque angle at MTPA [deg.] 134 o 138 o Power factor Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

11 Optimal Designs with Different Cooling Systems NC (natural cooled) FC (forced cooled) and LC (liquid cooled). Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

12 System Level Design Driving cycle Vehicle model Torque& Speed Rolling Resistance FF rr = kk rr mmmmmmmmmm(θθ) Climbing force FF cc = mmmmmmmmmm θθ TT mmmmmmmmmm = FF aa + FF rr + FF cc + FF DD. rr ww nn dd Aerodynamic Resistance FF DD = 0.5ρρ(υυ + υυ 0 ) 2 CC dd AA ff Acceleration FF aa = mmmm Speed profile Torque profile Energy Advanced Vehicle Simulator (ADVISOR); developed by NREL Energy distribution function Combined Dynamometer Driving Schedule Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

13 K-means Clustering Algorithms for Representative Points Cyclic representative points Urban Dynamometer Driving Schedule Cyclic representative points Highway Fuel Economy Driving Schedule Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

14 Multi-objective Optimization Considering Driving Cycle Objective 1: Minimization of loss over the representative load operating points The ratio of loss over net output power multiplied by the energy weights obtained through selection of representative points is summed up to minimize the dissipated energy. LLLLLLLL ii ww TT ii ωω ii ii Objective 2: Minimization of material cost Machine stack length is adjusted to deliver the rated torque corresponding to the maximum current density. Therefore only the rated operating point directly contributes to this objective. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

15 Two Designs from the Pareto Front vs. Reference (P) Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

16 UK Solar Car Gato Del Sol Major on-going projects Design and build our first 4-wheel challenger class solar car Re-engineer Gato Del Sol V to set the world speed record for solar-capable electric vehicles Design a solar cruiser class solar car. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

17 Solar Car Construction and Operation Sources: UK Solar Car; Ali Emadi, Advanced Electric Drive Vehicles, CRC Press, Boca Raton, FL, 2015 (lower left); and UK Lab. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

18 Conventional In-wheel AFPM Machine Original NGM stator-rotor kit Redesigned rotor prototyped; the sponsorship of Arnold Magnetics is gratefully acknowledged High speed constant power operation possible only by mechanically increasing the airgap (flux weakening). Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

19 Coreless In-wheel AFPM Machines MARAND Precision with CSIRO design (top) Commercially available One stator two rotors Distributed winding (3ph) Litz wire Our UK designs (right) Multiple rotor stator disks Concentrated coils Steel only on end rotors Not to exact scale. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

20 FEA of Coreless Machine Designs Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

21 MARAND Machine with Halbach PM Rotor Each rotor pole comprises multiple magnets with different directions of magnetization Total magnets per rotor 4x40=160 No back iron even in the rotor Highest achievable airgap flux density sinewave with a peak close to PM remanence Low(est) mass High(est) efficiency High torque Low speed. Source: High Efficiency Permanent Magnet Motor, wpcontent/uploadsmarand_high_efficiency_motor.pdf Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

22 Pros and Cons of Coreless Machines Pros No core losses, i.e. no fixed losses Lowest rotor losses High(est) efficiency throughout a wide speed range Virtually no cogging and ripple torque Low(est) noise. Cons High(est) cost because of large PM quantity High AC winding losses (special wire and windings) Heating of inner stators Ultra-low inductance (special electronics) Unsuitable for high-speed constant power operation. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

23 Optimal Design Studies Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

24 Performance Comparison One and Two Active Wheels Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

25 Power Electronics and PM Machine Control Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

26 Field (Flux) or Current Weakening? Traditional field (flux) weakening for PM machines Design the machine (IPM) with suitable inductance, hence core Advance the torque angle at rated current to reduce main flux and achieve constant power at high speed Current weakening Design the inverter with voltage overload for given current and power rating Reduce the current at 90 deg constant torque angle to diminish torque and achieve constant power at high speed May not work for machines with core and/or for very cost competitive applications. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

27 Traction Characteristics Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

28 LUCID Car and Induction Motor Courtesy of Lucid Motors and ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

29 The Induction Motor at the Heart of the Powertrain Two cooling mechanisms Water jacket in the motor case Oil splashing onto the end-windings and the rotor. 50% Glycol, 50% Water Transmission Oil Courtesy of Lucid Motors and ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

30 Multi-physics Electromagnetic Thermal Coupled Analysis Rotor Volumetric Losses EM Volume Losses Used in Computational Fluid Dynamics Rotor Bar Losses Stator Volumetric Losses rpm, 59.7 kw Equivalent car 25mph, 20% grade slope Slip = Current Source = 492 A Core loss = 477 W Winding Loss = 2749 W Rotor Bar Loss = 1175 W Total EM Loss = 4353 W All contours use Local Values, Auto Range Courtesy of Lucid Motors and ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

31 Oil-Transient Conjugate Heat Transfer End Windings and Insulation Lateral Cut Axial Cut 282F 139F Rotor Bars Courtesy of Lucid Motors and ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

32 Thermal Analysis Oil Transient and CHT Simulations Oil Transient Simulation Results (VOF 0.1 Iso-Surface, 0-0.5s) Courtesy of Lucid Motors and ANSYS, Inc. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

33 Electric Machine for NASA Sponsored Project Boeing Aircraft with CFM56-7B Engines Grey - unchanged parts Direct drive 10 MW 5,000 rpm First implementation 770kg; approx. 8hp/lb Credits: Codrin-Gruie (CG) Cantemir and Adrian Munteanu, 10 MW Ring Motor; OSU NNX14AL87A, EnergyTech16 Conf., Cleveland, OH, Nov 2016; NASA Sponsored Project. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

34 Electric Machine for NASA Sponsored Project Boeing Aircraft with CFM56-7B Engines Outer rotor induction machine Air-gap diameter approx. 1.2m Modules of MV winding and power electronic switches Cooling is implemented in the booster s blades. Credits: Codrin-Gruie (CG) Cantemir and Adrian Munteanu, 10 MW Ring Motor; OSU NNX14AL87A, EnergyTech16 Conf., Cleveland, OH, Nov 2016; NASA Sponsored Project. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

35 Electric Machine for NASA Sponsored Project Build of the First Demonstrator Credits: Codrin-Gruie (CG) Cantemir and Adrian Munteanu, 10 MW Ring Motor; OSU NNX14AL87A, EnergyTech16 Conf., Cleveland, OH, Nov 2016; NASA Sponsored Project. Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

36 Conclusion Electric transportation applications are different We exemplified machines with Core and without core Magnets and without magnets The ideal electric machine will have, as always: Virtually no cost ;) Virtually no losses ;) Innovation remains key How to best combine the achievements from different areas? Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,

37 Selected Recent Papers Rallabandi, Vandana, Taran, Narges, and Ionel, D. M., Multilayer Concentrated Windings for Axial Flux PM Machines, IEEE Transactions on Magnetics, Vol. 53, No. 6, /TMAG , 4p (2017). Rallabandi, Vandana, Taran, Narges, Ionel, D. M., and Eastham, J. F., Coreless Multidisc Axial Flux PM Machine with Carbon Nanotube Windings, IEEE Transactions on Magnetics, Vol. 53, No. 6, /TMAG , 4p (2017). Zhang, Peng, Ionel, D. M., and Demerdash, N. A. O., Saliency Ratio and Power Factor of IPM Motors with Distributed Windings Optimally Designed for High Efficiency and Low-Cost Applications, IEEE Transactions on Industry Applications, Vol. 52, No. 6, pp (2016). Wang, Yi, Ionel, D. M., Rallabandi, Vandana, Jiang, M., and Stretz, S., Large Scale Optimization of Synchronous Reluctance Machines Using CE-FEA and Differential Evolution, IEEE Transactions on Industry Applications, Vol. 52, No. 6, pp (2016). Fatemi, A., Ionel, D. M., Demerdash, N. A. O., and Nehl, T., Large-scale Design Optimization of PM Machines over a Target Operating Cycle, IEEE Transactions on Industry Applications, Vol. 52, No. 5, pp (2016). Fatemi, A., Ionel, D. M., Demerdash, N. A. O., and Nehl, T. W., Optimal Design of IPM Motors with Different Cooling Systems and Winding Configurations, IEEE Transactions on Industry Applications, Vol. 52, No.4, pp (2016). Fatemi, A., Ionel, D. M., Demerdash, N. A. O., and Nehl, T.W., Fast Multi-Objective CMODE-Type Optimization of PM Machines Using Multicore Desktop Computers, IEEE Transactions on Industry Applications, Vol. 52, No. 4, pp (2016). Taran, Narges, Rallabandi, Vandana, Ionel, D. M., and Heins, G., A Comparative Study of Coreless and Conventional AFPM Machines for Low and High Speed Operation, IEEE ECCE 2017 Congress, Cincinnati, OH, 6p (Oct. 2017). Liu, X., Cramer, A. M., Rallabandi, Vandana, and Ionel, D. M., Switching Frequency Selection for Ultra-Low-Inductance Machines, IEEE IEMDC 2017 Conference, Miami, FL, 6p (May 2017). Taran, Narges, Rallabandi, Vandana, Heins, G., and Ionel, D. M., A Comparative Study of Conventional and Coreless Axial Flux Permanent Magnet Synchronous Motors for Solar Cars, IEEE IEMDC 2017 Conference, Miami, FL, 7p (May 2017). Rallabandi, Vandana, Taran, Narges, Ionel, D. M., and Eastham, J. F., On the Feasibility of Carbon Nanotube Windings for Electrical Machines: Case Study for a Coreless Axial Flux Motor, IEEE ECCE 2016 Congress, Milwaukee, WI, 7p (Sep. 2016). Fatemi, A., Ionel, D. M., Demerdash, N. A. O., and Popescu, M., Design Optimization of Spoke-Type PM Motors for Formula E Racing Cars, IEEE ECCE 2016 Congress, Milwaukee, WI, 8p (Sep. 2016). Fatemi, A., Ionel, D. M., Demerdash, N. A. O., Stretz, S., and Jahns, T., RSM-DE-ANN Sensitivity Analysis of Material Cost in PM Motors with Distributed and Concentrated Windings, IEEE ECCE 2016 Congress, Milwaukee, WI, 7p (Sep. 2016). Dan M. Ionel, HEV EV Optimization CWIEME, Chicago, October 4,