Shen Zhang

Calculating the unsaturated inductance of 4/2 switched reluctance motors at arbitrary rotor positions based on partial differential equations of magnetic potentials

Phase inductance is a key parameter in switched reluctance motor (SRM) design because torque and the dynamics of phase voltages and currents are directly related to inductance. Therefore, accurate calculation of the inductance at arbitrary rotor positions is important in determining the SRM behavior. Contrary to the popular but time-consuming approach to determine the flux linkage and inductance of a SRM with finite element analysis (FEA), this paper proposes a novel analytical solution based on the partial differential equations of magnetic potentials to calculate the inductance profile of a 4/2 SRM with respect to rotor position, which makes the inductance prediction more time-efficient. The accuracy of this method is validated by comparing its estimated inductance profile to the inductance obtained by FEA.

Multi-objective design and optimization of generalized switched reluctance machines with particle swarm intelligence

This paper proposes a fast and generalized multi-objective design and optimization method for the Switched Reluctance Machines (SRM). An analytical design model for SRMs with any feasible stator and rotor slot combinations is firstly developed, which can accurately evaluate a SRM design much faster than the prevalent finite element analysis (FEA) method. In addition, a novel method for multi-objective optimization of SRM is proposed based on this analytical model, and the number of prime variables to be optimized is reduced to only five. A canonical Particle Swarm Optimization (PSO) algorithm with penalty function is applied to find the optimal solution for a user defined objective function. After several rounds of searching process with the PSO, the optimal regions can be found for the design variables in terms of the performance indices (PIs). Finally, the optimized designs are validated by FEA. This method can generate the optimized SRM designs subject to different design requirements and accelerate the entire optimization process.

Fast and accurate analytical calculation of the unsaturated phase inductance profile of 6/4 switched reluctance machines

Accurate calculation of the phase inductance profile of switched reluctance machines (SRMs) is of crucial importance in SRM design because it is a key parameter to predict the performance indices such as the torque and core loss. Instead of using the time-consuming finite element analysis (FEA) or the methods that require prior knowledge of magnetic fields from an FEA such as curve fitting and magnetic equivalent circuit (MEC), this paper proposes a fast and accurate analytical approach to determine the unsaturated phase inductance of a 6/4 SRM at arbitrary rotor positions by solving the partial differential equations of magnetic scalar/vector potentials based on Maxwells equations. Conformal mapping is applied to deal with the non-radial or non-tangential geometric structures when calculating the inductance of the SRM. The agreement between the results of the proposed analytical method and FEA validates the analysis.

A multi-objective analytical design approach of switched reluctance machines with integrated active current profile optimization

The large torque ripple of a switched reluctance machine (SRM) caused by commutation and the inherent nonlinear torque-producing nature is a major disadvantage for its widespread application. To mitigate this problem, current profiling for torque ripple minimization has been extensively pursued, however, most current profiling attempts are performed on existing SRM designs and thus will not guarantee a multi-objective optimized design in the entire design space for any specific applications. This paper thus proposes a built-in active current profile optimization approach, which is integrated to the early multi-objective design and optimization stage of SRMs. In addition, the proposed method is very time-saving, especially when the number of prime design variables and the entire search domain are large. Multi-objective optimization process involving 200, 1,000 and 5,000 trail designs are carried out with the multi-objective differential evolution (MODE) algorithm and their Pareto fronts are approximated at a speed more than 100 times faster than the popular finite element analysis (FEA) based electric machine analysis methods, offering machine designers handy, convenient and accurate initial designs, which can be further verified or fine-tuned by FEA in later processes.

A high-frequency rotating flux injection based rotor thermal monitoring scheme for direct-torque-controlled interior permanent magnet synchronous machines

Interior permanent magnet synchronous machine drives have widespread applications in electric traction systems and various industrial processes. However, prolonged exposure to high temperatures while operating can demagnetize the permanent magnets to the point of irreversible demagnetization. In addition, direct measurements with infrared sensors or contact-type sensors with wireless communication can be expensive and intrusive to the machine drive structure. This paper thus proposes a nonintrusive thermal monitoring scheme for the permanent magnets inside the direct-torque-controlled interior permanent magnet synchronous machines. By applying a high-frequency rotating flux offset to the hysteresis controllers in the motor drive, high-frequency currents can be injected into the stator windings of the machine. The permanent magnet temperature can thus be monitored based on the estimated high-frequency resistance, which is the byproduct of the induced magnet eddy current. The method is nonintrusive because it eliminates the infrared sensors and requires no hardware change to the existing motor drive, and the proposed method is validated by real-time experimental results.

Stator temperature estimation of open-loop controlled induction machines via active DC voltage injection

Thermal conditioning is a key component in the lifespan and efficiency of an induction machine, and appropriate thermal monitoring methods could help lengthen the lifetime of induction machines. The installation and purchase of potentially expensive and perishable temperature sensors are not the best option for manufacturers and businesses in todays competitive market. This paper thus proposes a non-intrusive method for thermal estimation of induction motors operating under open-loop controlled systems widely used in industry. By superimposing dc values onto the three-phase voltage commands, the dc signal is injected into the stator windings of the induction machine. The stator winding temperature could thus be estimated upon the extraction of a dc resistance. This method is efficient and non-intrusive because it does not disturb the normal operations of the induction motor, nor does it involve any hardware changes. Also, this method is time efficient because the resistance and temperature estimations can be calculated within seconds after the injection has been made. The effectiveness of the proposed method is illustrated by simulations conducted under various motor conditions.

A survey of electromagnetic — Thermal modeling and design optimization of switched reluctance machines

Switched reluctance machines (SRMs) are gaining interest in the industry and scientific communities owing to the advantages of rigid structures, high reliability, the absence of permanent magnets, robustness, fast dynamic response, and low manufacturing cost. They have become a feasible alternative to conventional electric machines with variable speed drives in many applications. This paper presents a comprehensive review on the status and potential trends of the technology pertinent to the design of SRMs in the following aspects: the mathematical modeling of the electromagnetic and thermal behaviors of SRMs, the enhancement of the performances in terms of torque ripple, efficiency, torque density and acoustic noise, and the multi-objective design optimization. The existing approaches are systematically and comprehensively summarized and compared for each category.

A fast control-integrated and multiphysics-based multi-objective design optimization of switched reluctance machines

This paper proposes a comprehensive approach for the multi-objective design optimization of switched reluctance machines (SRMs). An analytical model that predicts the electromagnetic (EM) behaviors of SRMs with arbitrary geometries, materials or current profiles is first developed and validated by its finite-element analysis (FEA) counterpart in terms of various performance indices. Then, a hybrid thermal model combining a 2-dimensional (2D) finite-difference (FD) formulation and thermal equivalent circuits is used to estimate the temperature distribution within an SRM based on the loss distribution calculated by the analytical EM model. The multi-physics model can evaluate the performances of an SRM much faster than the prevalent finite-element analysis (FEA). In addition, to further reduce the computational cost, artificial neural networks (ANNs) are constructed based on the maximin Latin hypercube design (MLHD) to identify the relationship between the SRM performance indices and the selected design variables within the entire predefined design space. Then, incorporating the pre-trained ANN models, a particle swarm optimization (PSO) based multi-objective optimization approach is utilized to identify the Pareto optimal design candidates in terms of maximizing the efficiency and torque density, and minimizing the torque ripple. The thermal constraints as well as the effects of current profiles are incorporated in the design process. Simulation results demonstrate that the proposed method can effectively generate the Pareto front within about half an hour with the evaluation of over 60,000 design candidates.

A high-frequency torque injection-based rotor thermal monitoring scheme for direct-torque-controlled interior permanent magnet synchronous machines

Interior permanent magnet synchronous machine drives are widely employed in electric traction systems and various industrial processes. However, prolonged exposure to high temperatures while operating can demagnetize the permanent magnets to the point of irreversible demagnetization. In addition, direct measurements with infrared sensors or contact-types sensors with wireless communication can be expensive and intrusive to the motor drive systems. This paper thus proposes a nonintrusive thermal monitoring scheme for the permanent magnets inside the direct-torque-controlled interior permanent magnet synchronous machines. By applying an external high-frequency torque signal to the hysteresis torque controller in the motor drive, the high-frequency currents can be injected into the stator windings. The permanent magnet temperature can thus be monitored based on the I induced high-frequency resistance. The nonintrusive nature of the method is indicated by the elimination of the extra sensors and no hardware change to the existing system. Finally, the effectiveness of the proposed method is validated with experimental results.

Performance evaluation and comparison of multi-objective optimization algorithms for the analytical design of switched reluctance machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines. The multi-physics and multi-objective nature of electric machine design problems are discussed, followed by benchmark studies comparing generic algorithms (GA), differential evolution (DE) algorithms and particle swarm optimizations (PSO) on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front. To better quantify the quality of the Pareto fronts, five primary quality indicators are employed to serve as the algorithm testing metrics. The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately, a significant amount of candidate designs. However, DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.