Evaluation of thermal couple impedance model of power modules for accurate die temperature estimation up to 200 ◦C

This paper presents an experimental evaluation of the thermal couple impedance model of power modules (PMs), in which Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect-Transistor (MOSFET) dies are implemented. The model considers the thermal cross-coupling effect, representing the temperature rise of a die due to power dissipations by the other dies in the same PM. We propose a characterization method to obtain the thermal couple impedance of the SiC MOSFET-based PMs for model accuracy. Simulation based on the proposed model accurately estimates the measured die temperature of three PMs with different die placements. The maximum error between measured and simulated die temperatures is within 8.1 ◦C in a wide and practical operation range from 70 ◦C to 200 ◦C. The thermal couple impedance model is helpful to design die placements of high power PMs considering the thermal cross-coupling effect.

A Model of Sky Temperature Estimation at 1.42 GHz using University of Baghdad’s Radio Telescope

The objective of this study is to select a suitable observing region at Baghdad location (44o 22' 48", 33o 16' 30") with low interference that may affect frequency of 1.42 GHz. Baghdad University Radio Telescope (BURT) is used in this study to determine a convenient region for observation in Baghdad sky. Different azimuths and elevations were chosen at different observations time. The results of this study showed that the best observations regions were located at azimuth (120o-160o) and (210o-260o). These regions included less sky temperature and estimated to be (42.8 to 163) K. The sky temperature model could be represented as a polynomial of third degree that could fit the behavior of the observation points.

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.

Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks

Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 ∘C <T< 60 ∘C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed.