This paper describes the comparison and parameterization process of dynamic battery models for cell and system simulation. Three commonly used equivalent circuit battery models are parameterized using a numeric optimization method and basic electrical tests with a lithium-ion polymer battery cell. The maximum model performance is investigated, and the parameterized models are compared regarding the parameterization effort and the model accuracy. For the model with the best tradeoff between the parametrization effort and the model accuracy, a reasonable simplification of the parameterization process is presented. This model is parameterized with the simplified parameterization process and, finally, validated by using a current profile obtained from an electric vehicle simulation performing a real-life driving cycle.

Condition monitoring of rotor problems such as demagnetization and eccentricity in permanent-magnet synchronous motors (PMSMs) is essential for guaranteeing high motor performance, efficiency, and reliability. However, there are many limitations to the offline and online methods currently used for PMSM rotor quality assessment. In this paper, an inverter-embedded technique for automated detection and classification of PMSM rotor faults is proposed as an alternative. The main concept is to use the inverter to perform a test whenever the motor is stopped and to detect rotor faults independent of operating conditions or load torque oscillations, which is not possible with motor current signature analysis (MCSA). The d-axis is excited with a direct-current+alternating-current signal, and the variation in the inductance pattern due to the change in the degree of magnetic saturation caused by demagnetization or eccentricity is observed for fault detection. An experimental study on a 7.5-kW PMSM verifies that demagnetization and eccentricity can be detected and classified independent of the load with high sensitivity.

No abstract

The work proposes a method for estimation of the magnet temperature in permanent-magnet synchronous machines by exploiting the d-axis saturation effects in the steel stator core produced by the d-current and rotor flux excitation. The method implies an intermittent injection of a voltage pulse in the d-axis of the motor. The resulting d-current response is a function of both the initial value of the d-current itself and the magnetization level of the magnets. Thus, a temperature dependent variation in the magnetization level of the permanent magnets is reflected in a variation of the d-current slope upon the voltage pulse. Experimental validation of the method is demonstrated with surface permanent-magnet motor.Index Terms-magnet temperature, rotor temperature, permanent-magnet synchronous motor, saturation effects, voltage pulse injection I.978-1-61284-972-0/11/$26.00

No abstract

No abstract

No abstract

Unlike single cage rotor fault detection, FFTbased steady state spectrum analysis techniques can fail to detect outer cage faults in double cage induction motors due to the small outer cage current under running conditions. Double cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer cage current, detection of outer cage faults under the startup transient is investigated. A Discrete Wavelet Transform-based method is proposed as a viable solution to detection of outer cage faults for double cage motors. An experimental study on fabricated copper double cage induction motors shows that the proposed method provides sensitive and reliable detection of double cage rotor faults compared to FFT.