Taguchi-EM-AI Design Optimization Environment for SynRM Drives in Traction Applications

Authors

  • A.A. Arkadan Colorado School of Mines, Dept. of Electrical Engineering, Golden CO 80401, USA
  • N. Al Aawar 2 Marquette University, Dept. of Electrical and Computer Engineering, Milwaukee WI, 53233, USA

Keywords:

design optimization, Taguchi algorithm

Abstract

Multi-objective design optimization environments are used for electric vehicles and other traction applications to arrive at efficient motor drives. Typically, the environment includes characterization modules that involve the use of Electromagnetic Finite Element and State-Space models that require large number of iterations and computational time. This work proposes the utilization of a Taguchi orthogonal arrays method in conjunction with a Particle Swarm Optimization search algorithm to reduce computational time needed in the design optimization of electric motors for traction applications. The effectiveness of the Taguchi method in conjunction with the optimization environment is demonstrated in a case study involving a prototype of a Synchronous Reluctance Motor drive system.

References

A. A. Arkadan, F. Isaac, and O. A. Mohammed, “Parameters Evaluation of Axially Laminated Anisotropic Synchronous Reluctance Motor Drives,” IEEE Trans. on Magnetics, vol. 36, pp. 1950-1955, July 2000.

A. A. Arkadan, A. Hanbali, and N. Al-Aawar, “Characterization and Design Optimization of ALA Rotor Synchronous Reluctance Motor Drives for Traction Applications,” 22nd Annual Review of Progress in Applied Computational Electromagnetics (ACES), pp. 249-256, Florida, U.S., Mar. 2006.

I. Boldea and S. A. Nasar, Vector Control of AC Drives. CRC Press, Boca Raton, FL, pp. 167-200, 1992.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. IEEE Int. Conf. on Neural Networks, pp. 1942-1948, 1995.

A. A. Arkadan, N. Al-Aawar, and A. O. Hariri, “EM-Taguchi Identifier for PSO Design Optimization Environment,” Presented at the 30th Applied Computational Electromagnetics Society, ACES, Symposium, Jacksonville FL, Mar. 23-27, 2014.

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Published

2020-11-07

How to Cite

A.A. Arkadan, & N. Al Aawar. (2020). Taguchi-EM-AI Design Optimization Environment for SynRM Drives in Traction Applications. The Applied Computational Electromagnetics Society Journal (ACES), 35(11), 1372–1373. Retrieved from https://journals.riverpublishers.com/index.php/ACES/article/view/7575

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