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Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric

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Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric applied sciences Article Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles Soo-Whang Baek 1 and Sang Wook Lee 2,* 1 Department of Automotive Engineering, Honam University, 417 Eodeung-daero, Gwangsan-gu, Gwangju 62399, Korea; [email protected] 2 Department of Mechanical Design Engineering, Wonkwang University, 460 Iksan-daero, Iksan-si, Jeollabuk-do 54538, Korea * Correspondence: [email protected]; Tel.: +82-63-850-6968 Received: 9 April 2020; Accepted: 3 May 2020; Published: 6 May 2020 Featured Application: Optimal design of permanent magnet synchronous motors (PMSMs) used in electric compressors in electric vehicles. Based on the proposed optimization method, the efficiency and cogging torque characteristics of the PMSM can be improved. Abstract: In this study, a shape design optimization method is proposed to improve the efficiency of a 3 kW permanent magnet synchronous motor (PMSM) used in an electric compressor intended for use in an electric vehicle. The proposed method improves the efficiency performance of the electric compressor by improving the torque characteristics of the initial PMSM model. The dimensions of the rotor were set as the design variables and were chosen to maximize efficiency and reduce cogging torque. During the determination of the design points with conventional Latin hypercube design, the experimental points may be closely related to each other. Therefore, the optimal Latin hypercube design was used to optimally distribute the experimental points evenly and improve the space filling characteristics. The Kriging model was used as an interpolation model to predict the optimal values of the design variables. This allowed the formulation of more accurate prediction models with multiple design variables, complex reactions, or nonlinearities. A genetic algorithm was used to identify the optimal solution for the design variables. It was used to satisfy the objective function and to determine the optimal design variables based on established constraints. The optimal design results obtained based on the proposed shape optimization method were confirmed by finite element analyses. For practical verification, the optimal model of the prototype PMSM of an electric compressor was manufactured, and a 1.5% improvement in its efficiency performance was confirmed based on an experimental dynamometer test. Keywords: optimal design; metamodeling; electric compressor; permanent magnet; synchronous motor 1. Introduction As the environmental pollution of internal combustion engine vehicles becomes serious, interest in eco-friendly vehicles and hybrid electric vehicles (HEVs) are increasing worldwide [1]. Among them, electric vehicles (EVs) are in the spotlight as pollution-free automobiles that reduce fuel efficiency [2] and have no harmful emissions because they use only electric energy in the system instead of the engine of an internal combustion engine [3]. Therefore, EV has been proposed as one of the methods to solve fossil fuel depletion and environmental problems, and research on it has been actively conducted worldwide [4]. Appl. Sci. 2020, 10, 3235; doi:10.3390/app10093235 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 3235 2 of 16 Recently, the PMSM has been used in parts for electric vehicles because it has excellent efficiency characteristics compared with induction motors [5]. PMSM is a structure in which a permanent magnet is embedded in the iron core of the rotor, and the magnetic flux weakening operation area can be widened depending on the difference in inductance of the d-q axis [6]. Because of these advantages, it is widely used in EV or HEV where variable speed operation is required [7]. An electric compressor for air conditioning of electric vehicles has been developed, and by applying high-efficiency PMSM to the compressor, it has the advantage of increasing fuel efficiency regardless of the vehicle’s driving speed [8]. Optimal design is essential to meet the various design requirements of PMSM. Optimal design is a method of finding the value of a design variable and obtaining an optimal solution within a limited design area [9]. The previous researches on the optimal design of PMSM can be divided into two categories. First, there are studies using the magnetic equivalent circuits (MEC) model [10,11]. In [10], it was proposed as a study on optimization using MEC model. It has been reported that the MEC optimization method combined with the optimization algorithm can optimize the volume and energy loss of PMSM. In addition, a novel MEC model of PMSM was developed to obtain maximum efficiency, minimum weight and price [11]. However, MEC-based design has a problem in that it is difficult to accurately consider the nonlinearity of parameters. Second, there are studies that combine numerical analysis methods of electrical devices such as finite element analysis (FEA) with optimal design algorithms [12,13]. FEA was performed to take into account the nonlinearity of permanent magnets and electrical steel sheets, which are difficult to consider in MEC. In [12], an optimal design of PMSM based on dynamic characteristics and finite element analysis of PMSM was performed. Design variables that influenced the efficiency characteristics of PMSM were selected and the efficiency and response characteristics were improved through experiments. In [13], the authors announced the optimization design process of PMSM for electric compressors of air conditioners applied to electric vehicles and hybrid vehicles. Research on the optimal design of PMSM using the response surface method has been carried out. In particular, the response surface method is often used in the optimization design of permanent magnet motors. The response surface method typically uses a second order polynomial regression model. However, it is difficult to accurately predict the optimum value because the response surface method becomes highly nonlinear and yields an unstable high-order prediction function. As a similar study related to PMSM, the rated efficiency was improved through the optimum design of 110 W small brushless direct current (BLDC) motor [14]. In addition, a study was conducted to improve the performance of the electric variable valve timing system for improving fuel efficiency and emission of automobiles [15]. Cogging torque is one of the most representative components of torque ripple in PMSM. This phenomenon can be a critical issue to automotive applications that need precision control of the PMSM and are sensitive to noise and vibration. Therefore, recently, studies to improve efficiency and reduce cogging torque have been actively conducted, and mainly focused on reducing the cogging effect by adjusting the combination of pole slot number, pole arc, and core shape [16,17]. Furthermore, the study of [18] performed multi-purpose shape optimization of PMSM based on FEA and particle swarm optimization algorithms. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. A study has been conducted on the multi-purpose optimized design PMSM of fractional slotted windings [19]. The optimization results demonstrated the accuracy of the proposed model with comparison to numerical models such as FEA, but with much faster computational performance which makes it much more suitable to be used in evolutionary optimization design approaches. Most of the previous studies were applied to the optimal design using metamodels created in one way [20,21]. In general, FEA’s design of experiment (DOE) consists of a modeling process using CAD Appl. Sci. 2020, 10, 3235 3 of 16 tools, an FEA analytical condition setting process, a FEA process, and a post-process for extracting and configuring results. A lot of DOE has to be done to get reliable and optimal design results. AsAppl. a Sci. related 2020, 10 study,, x FOR PEER Taguchi REVIEW method was used to optimize the efficiency and cogging3 torqueof 15 of BLDC motors used in automotive electric oil pumps. However, to obtain DOE results, 336 FE analyses extracting and configuring results. A lot of DOE has to be done to get reliable and optimal design for five design variables were required [22]. results. ThisAs study a related differs study, from Taguchi previous method studies was givenused to that optimize five designthe efficiency variables and werecogging selected torque forof the DOEBLDC and 54 motors experiments used in automotive were performed. electric Tooil improvepumps. However, the efficiency to obtain of theDOE design results, variable 336 FE analyses distribution, the optimalfor five design Latin hypercubevariables were design required (OLHD) [22]. [23,24] was used with an equal number of levels for all the designThis study variables differs that from has previous a better studies fill performance given that five than design Latin variables hypercube were selected design (LHD)for the [25]. NumerousDOE and studies 54 experiments have been publishedwere performed. on metamodeling To improve conductedthe efficiency based of the on thedesign application variable of a singledistribution, method [26 the,27 optimal]. However, Latin hypercube there are design suitable (OLHD) metamodels [23,24] was for used each with optimal an equal design number problem. of Correspondingly,levels for all the the design metamodels variables that need has to abe better written fill performance and compared than Lati in variousn hypercube ways. design Given (LHD) that the prediction[25]. Numerous performance
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