|IE 1||Calculation of Temperature Field in
|The operating temperature of a power capacitor has an effect on its service life directly. A 500-kvar power capacitor is taken as the research object. The capacitor fever produced by internal dielectric loss is considered under the running condition of 55 ◦C. The 3-D finite volume method calculation model of capacitor temperature is established, and the temperature distribution characteristics and the internal temperature at the hottest spot of the capacitor are obtained. With the research on a corresponding prototype, the numerical simulation results are consistent with the test values. The accuracy of temperature calculation model is testified, which provides a reliable basis for the design and operation of power capacitor.||2015|
|IE 2||Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market||To avoid inconvenient vehicle stops at charging stations, on-road wireless charging of electric vehicles (EVs) is a promising application in the future smart grid. In this paper, we study a critical yet open problem for this application, i.e., the impact of wireless charging and mobility of EVs on the wholesale electricity market based on locationalmarginal price (LMP), which is mainly determined by the EV mobility patterns. To capture the dynamics in vehicle traffic flow and state of charge of EV batteries, we model the EV mobility as a queuing network based on the statistics obtained via traffic information systems.
Then, the load on each power system bus with respect to EV wireless charging is obtained using the stationary distribution of the queuing network. An economic dispatch problem is formulated to incorporate the EV wireless charging
demand, and the LMP of each power system bus is obtained. Furthermore, a pricing mechanism based on the
LMP variations of power system buses is investigated to enhance the social welfare. The performance of our proposed analytical model is verified by a realistic road traffic simulator (SUMO) based on a 3-bus test system and an IEEE 30-bus test system, respectively. Simulation results indicate that our proposed analytical model can accurately provide an estimation of the LMP variations due to EV wireless charging.
|IE 3||Analysis of Capacitive Impedance Matching Networks for Simultaneous Wireless Power Transfer to Multiple Devices||This paper presents wireless power transfer (WPT) characteristics according to load variation in multidevice WPT systems using capacitive impedance matching networks (IMNs). Two basis IMNs of using series–parallel (SP) capacitors and parallel–series (PS) capacitors are used. Four combinations of capacitive IMNs are considered, i.e., SP in a transmitting side and SP in a receiving (Rx) side (SP-SP), SP-PS, PS-SP, and PS-PS. The optimum capacitance values for each IMN are also derived by circuit analysis. For verification, three cases based on the number of Rx coils are considered, and the calculated results are compared with the simulated and measured results for each case. A WPT system for only a single device has identical power transfer efficiency for four combinations of the IMNs. Multidevice WPT systems with the PS IMN in Rx sides are found to transfer more power toward the Rx coil with lower load impedance according to the load variation. On the other hand, using the SP IMN in Rx sides is less sensitive to load variation than using the PS IMN. In addition, a WPT system using the PS-PS IMN combination is less responsive to the cross coupling between Rx coils than that using the SP-SP IMN combination.||2015|
|IE 4||Model-Based Virtual Thermal Sensors for Lithium-Ion Battery in EV Applications||Continuous monitoring of temperature distribution in lithium-ion (Li-ion) batteries is critical in preventing rapid degradation, mismatch in cell capacity, and potentially thermal runaway. A model based on virtual thermal sensor (VTS) for automotive grade Li-ion batteries is presented in this paper. This model, using a small number of physical sensors, is able to estimate temperature distribution throughout the battery in real time. First, the thermal model of the battery is developed and the characteristic parameters of the battery are tuned using the prediction error minimization method. Then, the tuned model is combined with a Kalman filter to estimate the temperature distribution of the battery under unknown initial values and model uncertainty. The proposed model-based VTS has been experimentally validated on an automotive grade 70-Ah lithium iron phosphate (LiFePO4) battery.||2015|