Gang Lei

System-Level Design Optimization Methods for Electrical Drive Systems: Deterministic Approach

Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level design and optimization methods for electrical drive systems. Two system-level design optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the design of experiments technique, and the sequential subspace optimization method are presented to improve the optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level design optimization method.

System-Level Design Optimization Method for Electrical Drive Systems—Robust Approach

A system-level design optimization method under the framework of a deterministic approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the products reliability and quality in mass production, which are not investigated in the deterministic approach. Therefore, a robust approach based on the technique of design for six sigma is presented for the system-level design optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust approach.

Techniques for Multilevel Design Optimization of Permanent Magnet Motors

The multilevel method has been presented for design optimization of electrical machines and drive systems for optimal system performances and efficiency in our previous work. For framework design of the multilevel optimization method, four techniques are presented in this paper, including the sizing equation, local sensitivity analysis, global sensitivity analysis, and design of experiments techniques. For each technique, a general and theoretical analysis procedure is presented before the application study. To demonstrate the effectiveness, a permanent magnet claw-pole motor with soft magnetic composite core and 3-D finite-element analysis model is investigated to minimize the material cost and maximize the output power while keeping the volume constant. The calculated motor performance based on this 3-D finite-element model has been verified by the experimental results. As shown, these techniques are simple to implement, and the resultant multilevel optimization framework can significantly improve the motor performance and reduce the required sample number of finite-element analysis.

New Axial Laminated-Structure Flux-Switching Permanent Magnet Machine With 6/7 Poles

In this paper, one new axially laminated-structure flux-switching permanent magnet machine (ALSFSPMM) with 6/7 (stator/rotor) poles is proposed. Different from the conventional flux-switching permanent magnet machine (FSPMM), the stator and rotor of ALSFSPMM are laminated parallel to the axial direction, which can make full use of PM flux linkage, decrease part magnetic saturation, and reduce the iron loss particularly in the range of high speed. By the 2-D model prediction of finite element algorithm (FEA), ALSFSPMM has lower cogging torque, stronger flux weakening ability, greater torque density, higher efficiency, etc., and hence it is an ideal candidate for the drive system of plug-in hybrid electrical vehicle (PHEV).

Sequential Optimization Method for the Design of Electromagnetic Device

Three sequential optimization methods, sequential least square method, sequential Kriging method, and sequential linear Bayesian method, are presented for the optimization design of electromagnetic device. Sequential optimization method (SOM) is composed of coarse optimization process and fine optimization process. The main purpose of the former is to reduce the design space; while the target of the latter is to update the optimal design parameters. To illustrate the performance of the proposed methods, an analytic test function and the TEAM Workshop Problem 22 are investigated. Experimental results of test function demonstrate that SOM can obtain satisfactory solutions; and practical application illustrates that the number of finite element sample points is less than 1/10 compared with that by direct optimization method, while the optimal results are even better than that by direct optimization method.

Multi-Objective Model-Predictive Control for High-Power Converters

This paper presents a multi-objective model-predictive control (MOMPC) strategy for controlling converters in high-power applications. The controller uses the system model to predict the system behavior in each sampling interval for each voltage vector, and the most appropriate vector is then chosen according to an optimization criterion. By changing the cost function properly, multiobjectives can be achieved. To eliminate the influences of one step delay in digital implementation, a model-based prediction scheme is introduced. For high-power applications, the converter switching frequency is normally kept low in order to reduce the switching losses; this is done by adding a nonlinear constraint in the cost function. However, to avoid system stability deterioration caused by the low switching frequency, an N-step horizontal prediction is proposed. Finally, the control algorithm is simplified using a graphical algorithm to reduce the computational burden. The proposed MOMPC strategy was verified numerically by using MATLAB/Simulink, and validated experimentally using a laboratory ac/dc converter.

Theoretical Research on New Laminated Structure Flux Switching Permanent Magnet Machine for Novel Topologic Plug-In Hybrid Electrical Vehicle

In this paper, a novel configuration for University of Technology Sydney (UTS) plug-in hybrid electric vehicle (PHEV) is introduced which has only one electric machine functioning as either a motor or generator at a time. For continuous working, more strict requests are made to the drive machine, mainly including good thermal dissipation capability, high torque density, great flux weakening ability, etc. One new laminated structure flux switching permanent magnet machine (LSFSPMM) is proposed in this paper, which stator and rotor are laminated in parallel to the axis. It can make full use of PM flux linkage and reduce the core loss particularly in the high excitation frequency. Based on the 2D model prediction by finite element algorithm (FEA), LSFSPMM has lower cogging torque, higher torque density, greater flux weakening ability, higher efficiency, etc., and hence it can be regarded as one ideal candidate for the UTS PHEV drive system.

Robust Design Optimization of PM-SMC Motors for Six Sigma Quality Manufacturing

In our previous work, soft magnetic composite (SMC) material was employed to design cores for two kinds of permanent magnet (PM) motors, namely transverse flux machine (TFM) and claw pole motor. Compared with motors designed by traditional silicon steel sheets, these motors require 3D flux design with new material and new manufacturing method. Meanwhile, the performances of these motors highly depend on the material and manufacturing parameters besides structure parameters. Therefore, we present a robust design optimization method for high quality manufacturing of these PM-SMC motors to improve their industrial applications. Thereafter, from the design analysis of a PM-SMC TFM, it can be found that the proposed method can significantly improve the manufacturing quality and reliability of the motor, and reduce the manufacturing cost.

System Level Six Sigma Robust Optimization of a Drive System With PM Transverse Flux Machine

From our previous study, permanent magnet (PM) transverse flux machine with soft magnetic composite material core is very promising. However, from the point of view of engineering application, at least two more aspects have to be considered. First, not only the machine but also its control system has to be investigated and optimized simultaneously. Second, robust design requirements must be included in the system level optimization, which is a very important issue in modern quality design. Therefore, to improve the applications of this type of machine, we present a system level six sigma robust optimization method to design a drive system with this motor and field-oriented control scheme. The optimal robust solutions obtained are compared with those from deterministic optimization method and initial design scheme. From the comparison, we can see that the systems reliabilities and robust levels are improved significantly by the proposed method, and the probability of system failure can be reduced a lot.

Sequential Subspace Optimization Method for Electromagnetic Devices Design With Orthogonal Design Technique

We present two new sequential optimization strategies, a sequential subspace optimization method (SSOM) and an improved sequential optimization method (SOM) with orthogonal experimental design technique, to deal with optimization design problems of electromagnetic devices in this work. To implement the proposed methods, we first divide the whole design factors into three sets, namely highly-significant, significant, and nonsignificant factors. Then the whole design space can be correspondingly divided into three subspaces with these three sets of factors. Thereafter, SSOM is presented to sequentially optimize those subspaces. In the subspace, we present an improved SOM based on orthogonal experimental design technique to get optimal solutions. Finally, by investigating TEAM benchmark problem 22, we can see that the sampling efficiency can be improved significantly and the computational cost of finite element analysis can be saved remarkably by the proposed methods.