Multicriteria PM Motor Design Based on ANFIS Evaluation of EV Driving Cycle Efficiency

This paper proposes a multicriteria design optimization methodology for permanent magnet (PM) motors used in electric vehicle (EV) applications. In the process, an adaptive-network-based fuzzy inference system (ANFIS) is utilized, coupled with a multiobjective optimization algorithm, as a surrogate model of the electric motor. This allows for the consideration of the full drive cycle and respective efficiency map for every motor design. The prediction error of the ANFIS is minimized by employing appropriate membership functions, initial training data, and an adaptive learning scheme via iterative training. The efficiency map is then implemented in a vehicle dynamic model to compute the total consumed energy over the driving cycle. The optimization profile accounts for total energy efficiency, torque density, and additionally considers complementary design criteria via an a posteriori selection procedure on the resulting Pareto set. The methodology developed is applied to optimize a surface PM motor with concentrated fractional slot winding, mounted on a light EV that competes in fuel economy races. The selected motor design has been validated through measurements on a prototype.

• Record URL:
  • https://doi.org/10.1109/TTE.2018.2810707
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23327782
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Krasopoulos, Christos T
  • Beniakar, Minos E
  • Kladas, Antonios G
• Publication Date: 2018-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 525-535
• Serial:
  • IEEE Transactions on Transportation Electrification
  • Volume: 4
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2332-7782
  • Serial URL: http://ieeexplore.ieee.org/servlet/opac?punumber=6687316

Subject/Index Terms

• TRT Terms: Adaptive control; Electric drives; Electric vehicles; Fuzzy systems; Machine learning; Multiple criteria decision making; Networks; Optimization; Permanent magnets; Vehicle design
• Subject Areas: Design; Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01679180
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 27 2018 2:05PM