Mass Production Costing of Induction Machines for Automotive Applications

Hundak, Vladimir; Cox, Tom; Vakil, Gaurang; Gerada, Christopher

Abstract

Cost is one of the key parameters of electric machines designed for automotive applications. In order to keep manufacturing cost low, it is desirable to estimate the manufacturing cost of the machine in the early stages of the design process. For this purpose, a costing algorithm for mass production of induction machines is proposed. The case study examines the impact of variations in dimension (stack length and bore diameter) of the input design on cost outputs, in order to validate the base structure of the algorithm. The overall aim of this work is to provide cost optimization of the machine design by implementation into a design optimization routine.

Citation

Hundak, V., Cox, T., Vakil, G., & Gerada, C. (2018). Mass Production Costing of Induction Machines for Automotive Applications. In 2018 IEEE Transportation and Electrification Conference and Expo (ITEC) (500-505). https://doi.org/10.1109/ITEC.2018.8450116

• Conference Name: 2018 IEEE Transportation and Electrification Conference and Expo (ITEC)
• Conference Location: Long Beach, CA, USA
• Start Date: Jun 13, 2018
• End Date: Jun 15, 2018
• Acceptance Date: Jun 13, 2018
• Online Publication Date: Aug 30, 2018
• Publication Date: 2018
• Deposit Date: Oct 30, 2018
• Publicly Available Date: Oct 31, 2018
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 500-505
• Book Title: 2018 IEEE Transportation and Electrification Conference and Expo (ITEC)
• Chapter Number: N/a
• ISBN: 978-1-5386-3049-5
• DOI: https://doi.org/10.1109/ITEC.2018.8450116
• Keywords: Manufacturing; Torque; Induction machines; Optimization; Costing; Mathematical model; Windings
• Public URL: https://nottingham-repository.worktribe.com/output/1211254
• Publisher URL: https://ieeexplore.ieee.org/document/8450116
• Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.