In this paper, we develop formulation of a multiobjective optimization problem (MOOP) to optimally size a battery unit (BU)-ultracapacitor (UC) hybrid energy storage system (HESS) for plug-in electric vehicle (EV). In this application, the objectives were to minimize cost, weight, volume of the HESS simultaneously maximizing the remaining cycle life of the BU at the end of the driving cycle. The MOOP is solved by the Non-dominated Sorting Genetic Algorithm type 2 (NSGA-II) algorithm. Detailed mathematical models for the BU and UC are given. The thermal effect on performance and sizing are also included in the formulation. The power demand by the EV powertrain is shared amongst the BU and HESS by two methods: first is by using wavelet transformation (WT), while the second is by using power split ratio. The ratio of the power (i.e. power split) handled by each storage unit was determined by the optimizer. A sensitivity analysis was conducted for the power splitting ratio verification. The problem was solved for using the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HFET) driving profiles. This has resulted in sizing of a HESS with lower cost, volume and weight than those existing in literature. Finally, the effect of changing the motor type on the MOOP result was investigated.

With the emerging power electronic technologies, numerous electrical devices use high-frequency and nonsinusoidal supplies. Such sources generate complex harmonic fields that overburden the accurate prediction of core losses using the conventional finite element method (FEM). Thereby, to predict core losses under those high-frequency and nonsinusoidal supplies accurately, this study first built a test system for core losses measurement of ferromagnetic materials (e.g., soft ferrite, laminated steels, and nanocrystalline alloy) fed by the square and pulsewidth modulated (PWM) supplies. Then, the loss prediction problems of the classical core loss model under high-frequency and nonsinusoidal supplies are revealed. Afterward, an improved FEM-based core loss model is proposed, which includes a piecewise variable parameters based hysteresis loss model, an eddy current loss model considering different cross sections of magnetic circuit and supply waveforms, and improved excess loss coefficients. Experimental validations on different materials, the nanocrystalline alloy in an 11-kVA intermediate frequency transformer, the ultrathin silicon sheets in a 10-kW high-speed permanent magnet motor, and the nanocrystalline alloy in a 3-kW wireless power transfer system, are conducted systematically. The effectiveness and generality of the improved core loss model were verified.

This paper investigates the online fault detection (FD) of the inter-turn short-circuit (ITSC) fault in direct torque control (DTC)-driven induction motors (IMs). We conducted the investigations of harmonic, inter-harmonic, and sub-harmonic contents for FD purposes. The harmonics in the flux leakage spectrum appear as a result of diverse causes including stator fault, DTC reaction to the fault, switching activities, and core saturation effect. Physics-based co-simulation was held through the coupling between a finite-element model (FEM) of the faulty IM and the circuit simulation of DTC. Its advantages are: 1) the IM harmonic contents including stator slots, rotor slots, saturation, switching activity, and fault are simulated and 2) dangerous experimentations of a faulty IM can be avoided. Therefore, we can safely study all the characteristics on a faulty IM. We exploited FEM for the verification of the experimental harmonic signatures of the scattered magnetic field. We verified the online FD technique based on the radiated magnetic field's spectrum through the co-simulation and experimental results. This indicated that the method is able to correctly detect the ITSC faults with 1% severity factor.

This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS). The RLS with forgetting factor is firstly adopted to identify the parameters of motor inductances. Then, the online identification of rotor resistance can be realized via the MRAS based on the instantaneous reactive power (IRP) and the proportional integral (PI) regulation adaptive law designed by the Popov hyper-stable theory. Thereby, the rotor temperature is computed by the resistance-temperature relationship of metals. To achieve a fast convergence of the parameter identification, rotor slot harmonics are extracted from the stator current and used to determine the rotor speed in real time. Furthermore, to obtain a more accurate initial value of the stator and rotor leakage inductances and resistances, the first 5-15 cycles of the IM starting process are used to mimic the locked-rotor test condition. A merit of the proposed method is that it requires significantly less time to estimate the rotor resistance than the traditional methods. With a novel test bench, experimental validation is performed on a 22-kW IM. In the test, the rotor temperature is measured in three different ways: 1) wireless sensors inserted in the rotating rotor core and rotor end rings, 2) infrared sensor for rotor end ring temperature, and 3) PT100 installed in stator end winding. With this test bench, the real rotor temperature was revealed, and the effectiveness of the presented method is also verified.

For an inductive power transfer (IPT) system used in electric vehicle (EV) charging, the receiving coil is usually equipped with a shielding structure to reduce iron loss and electromagnetic interference in the chassis. However, the addition of a shielding structure may significantly reduce self-inductance and mutual inductance, and the eddy current on the shielding pad can also produce power loss. To maximize the efficiency of the IPT system used for EV charging and sufficient shielding, this study first analyzed the equivalent circuit model of the IPT system. A 3-D model of an IPT system, with a steel plate and aluminum shielding, is established and investigated in the finite-element analysis (FEA) domain, in which the shielding structure is modeled with some parametric sizes to simplify the modification of the parameters on a shielding structure. The simulation result shows how the ohmic loss on coils and the iron loss on steel plate changes after adding the shielding structure. Afterward, a practical method for optimizing aluminum shielding structure's parameters is proposed to maximize the efficiency of the IPT system, taking iron losses in a steel part and aluminum shielding into account. Furthermore, considering that the optimization procedure becomes complex since the different resonant capacitances are required due to the variable inductances, a new efficiency calculation method by FEA is presented to simplify the optimization procedure. Experimental validation is finally performed on a 3-kW wireless charging prototype; thus, the effectiveness of the proposed method is verified.