Surrogate-Based Optimization of Firing Angles for Switched Reluctance Motor Conference Paper

abstract

• 2018 IEEE. In this work, optimal firing angles of Switched Reluctance Motors (SRMs) are explored by surrogatebased optimization in order to minimize the torque ripple. Surrogate-based optimization is facilitated via Neural Networks (NNs) which are regression tools capable of learning complex multi-variate functions. Flux and torque calculations of a nonlinear 16/20 SRM are evaluated with a NN, and consequently the computation time is expedited by replacing the look-up tables of flux and torque with the surrogate NN model. An optimization algorithm is proposed to discover optimal firing angle objects to minimize the 16/20 SRM torque ripple for a certain electrical load requirement. The resulting optimal firing angles are also represented by simple NN models to expedite online control. Comprehensive simulation and experimental results are provided to validate the theoretical findings.

name of conference

• 2018 IEEE Transportation Electrification Conference and Expo (ITEC)

authors

• Toliyat, Hamid

published proceedings

• 2018 IEEE Transportation Electrification Conference and Expo (ITEC)

author list (cited authors)

• Anvari, B., Kaya, M., Englebretson, S., Hajimirza, S., & Toliyat, H. A.

citation count

• 1

complete list of authors

• Anvari, Bahareh||Kaya, Mine||Englebretson, Steven||Hajimirza, Shima||Toliyat, Hamid A

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

Digital Object Identifier (DOI)

• 10.1109/itec.2018.8450092

International Standard Book Number (ISBN) 13

• 9781538630488

start page

• 1023

end page

• 1028

volume

• 00

URL

• http://dx.doi.org/10.1109/itec.2018.8450092