The prediction of iron losses in soft magnetic materials is difficult and has not yet been entirely solved. Although much work has been performed for loss prediction under dc bias conditions, little work evaluates the behavior of iron losses with saturation involved. In this paper, measurements of iron losses have been performed to gain information on how losses are affected by a dc bias when saturation occurs. The Steinmetz parameters are calculated together with the Steinmetz premagnetization graph (SPG) method. The SPG is improved by expressing the parameter $\beta $ as a function of both $H_{\textrm {dc}}$ and $\Delta B$ to take the saturation effect into consideration. The calculated losses are compared with the measured ones. The accuracy has always been ≤±10%.

In this paper, a salient-pole permanent magnet synchronous machine (SPPMSM) is introduced to high-speed trains, taking advantage of its high utilization of the reluctance torque from the unbalanced reactance. Torque characteristics, such as average torque and torque ripple, are rather important and should be carefully concerned during the optimal design of the SPPMSM. Both the eccentric air-gap and the advanced step-skewed rotor (ASSR) design are developed and implemented to reduce the torque ripple for comfort and safety issues. Compared to the conventional step-skewed rotor (CSSR), the ASSR takes advantage of the high utilization of permanent magnets (PMs), which are axial continuous assembled, and the easier manufacture with a single rotor lamination model, which is asymmetrically designed with pole shoes skewed by individual skewing angles. No skewing arrangement is applied to the rotor yoke and the PM chambers. The impacts of the eccentric air-gap and the ASSR on the torque characteristics are comprehensively investigated by finite element analysis. The results show that the proposed technique can significantly reduce the torque ripple with a slight expense of the average torque. Based on the optimal eccentric design, experiments of the SPPMSM with the non-skewed rotor, CSSR, and ASSR under various conditions are carried out to validate the estimated results.

In electrical machines, iron losses are essential for electromagnetic and thermal designs and analyses. Although many models have been proposed to predict iron losses in magnetic materials, the calculation of iron losses under non-sinusoidal excitations is still an open field. Most works concern the influences of the value, the change rate or the frequency of flux density in the frequency domain. In this paper, we propose an engineering model for predicting loss characteristics with given waveforms of flux density in the time domain. The characteristics are collected from the knowledge of the iron loss in a laminated ring-shaped transformer. In the proposed model, we derive mathematical formulas for exciting currents in terms of flux density by describing the function methods through multi-frequency tests with sinusoidal excitations. The non-linearity of the material is interpreted by branches of conductances accounting for hysteresis and eddy-current losses. Then, iron losses are calculated based on the law of conservation of energy. An experimental system was built to evaluate the magnetic properties and iron losses under sinusoidal and non-sinusoidal excitations. Actual measurement results verify the effectiveness of the proposed model.

In this paper, dynamic reluctance mesh model (DRM) combined with an improved iron losses prediction model are presented to provide fast and accurate way to evaluate the iron losses for permanent magnet (PM) motors with pulsewidth-modulated power supply. A DRM model for high-speed train PM traction motor is first built. The magnetic field distribution for one cycle is then obtained using the DRM method. An improved engineering model of iron losses prediction is applied. Finally, the simulation and testing results are presented to confirm that the proposed combined method is accurate and effective as the finite element method (FEM) model with much less time consumed.

This paper proposes a novel rotor structure for high-speed interior permanent magnet motors to overcome huge centrifugal forces under high-speed operation. Instead of the conventional axial stacking of silicon-steel laminations, the retaining shield rotor is inter-stacked by high-strength stainless-steel plates to enhance the rotor strength against the huge centrifugal force. Both mechanical characteristics and electromagnetic behaviors of the retaining shield rotor are analyzed using finite-element method in this paper. Prototypes and experimental results are demonstrated to evaluate the performance. The analysis and test results show that the proposed retaining shield rotor could effectively enhance the rotor strength without a significant impact on the electromagnetic performance, while some design constraints should be compromised.

To improve fire prediction accuracy over existing methods, a double weighted naive Bayes with compensation coefficient (DWCNB) method is proposed for fire prediction purposes. The fire characteristic attributes and attribute values are all weighted to weaken the assumption that the naive Bayes attributes are independent and equally important. A compensation coefficient was used to compensate for the prior probability, and a five-level orthogonal testing method was employed to properly design the coefficient. The proposed model was trained with data collected from the National Institute of Standards and Technology (NIST) fire database. Simulation comparisons show that the average prediction accuracy of the proposed method is 98.13%, which is 5.08% and 2.52% higher than the methods of naive Bayes (NB) and double weighted naive Bayes (DWNB), respectively. The experimental results show that the average accuracies of the DWCNB method for test fire and interference sources were 97.76% and 98.24%. Prediction accuracies were 5.06% and 3.74% higher than those of the NB and DWNB methods.