Robust Design Optimization of BLDC Motor for Electric Oil Pump Using Characteristic Function

Author(s):  
Soo-Whang Baek

In this paper, we propose and perform the robust design optimization (RDO) algorithm for the shape of the BLDC motor used in the electric oil pump. The proposed RDO algorithm improves the torque characteristics of the BLDC motor to improve the performance of the electric oil pump. The previous deterministic design optimization (DDO) method derives an appropriate combination of the design goal and the specific target performance. However, not only the target performance but also other performance constraints must be considered for RDO. To overcome these problems, we consider the penalty function. In conclusion, we can confirm the improvement of the torque characteristics of the BLDC motor used in the EOP by using the proposed RDO algorithm.

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 153 ◽  
Author(s):  
Keun-Young Yoon ◽  
Soo-Whang Baek

In this paper, we propose and evaluate a robust design optimization (RDO) algorithm for the shape of a brushless DC (BLDC) motor used in an electric oil pump (EOP). The components of the EOP system and the control block diagram for driving the BLDC motor are described. Although the conventional deterministic design optimization (DDO) method derives an appropriate combination of design goals and target performance, DDO does not allow free searching of the entire design space because it is confined to preset experimental combinations of parameter levels. To solve this problem, we propose an efficient RDO method that improves the torque characteristics of BLDC motors by considering design variable uncertainties. The dimensions of the stator and the rotor were selected as the design variables for the optimal design and a penalty function was applied to address the disadvantages of the conventional Taguchi method. The optimal design results obtained through the proposed RDO algorithm were confirmed by finite element analysis, and the improvement in torque and output performance was confirmed through experimental dynamometer tests of a BLDC motor fabricated according to the optimization results.


Author(s):  
Souvik Chakraborty ◽  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Sondipon Adhikari

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.


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