Recent advances in solid-state semiconductors have led to the development of medium-voltage power converters (e.g., 6-36 kV) which could obviate the need for the step-up transformers of renewable power generation systems. The modular multilevel cascaded converters have been deemed as strong contenders for the development of medium-voltage converters, but the converters require multiple isolated and balanced dc supplies. In this paper, a high-frequency link multilevel cascaded medium-voltage converter is proposed. The common high-frequency link generates multiple isolated and balanced dc supplies for the converter, which inherently minimizes the voltage imbalance and common mode issues. An 11-kV system is designed and analyzed taking into account the specified system performance, control complexity, cost, and market availability of the power semiconductors. To verify the feasibility of the proposed system, a scaled down 1.73-kVA laboratory prototype test platform with a modular five-level cascaded converter is developed and explored in this paper, which converts a 210 V dc (rectified generator voltage) into three-phase 1 kV rms 50 Hz ac. The experimental results are analyzed and discussed. It is expected that the proposed new technology will have great potential for future renewable generation systems and smart grid applications.

Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This paper presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles in the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multi-objective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.

This work presents the analysis, design and optimization of a permanent magnet synchronous motor (PMSM) for an electric vehicle (EV) used for campus patrol with a specific drive cycle. Firstly, based on the collected data like the parameters and speed from a test EV on the campus road, the dynamic calculation of the EV is conducted to decide the rated power and speed range of the drive PMSM. Secondly, according to these requirements, an initial design and some basic design parameters are obtained. Thirdly, optimization process is implemented to improve the performance of the designed PMSM. The permanent magnet (PM) structure, airgap length and stator core geometry are optimized respectively in this step. Different optimization processes are proposed to meet multiple optimization objectives simultaneously. Based on the finite element analysis (FEA) method, it is found that the harmonic of the optimized PMSM is lower than that of the initial design, and the torque ripple is reduced by 24%. The effectiveness of optimization on the core loss and PM eddy loss is validated and the temperature rise is suppressed effectively. Finally, a prototype is fabricated for the optimized PMSM and an experimental platform is developed. The test results verify that the optimized PMSM meets the requirements of the specific campus patrol EV well.

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 product's 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.