Thermal Network Model Research Articles

Investigating the thermal runaway features of lithium-ion batteries using a thermal resistance network model

Accurate measurement of the characteristic temperatures of thermal runaway, which are affected by many factors, is important for battery safety evaluation. A one-dimensional thermal resistance network model is built in this study to investigate the influences of various factors on the thermal runaway features of lithium-ion batteries. In the model, the battery is divided into four independent components in the thickness direction, with thermal resistances connecting different nodes. The gas thermal resistance is added to simulate swelling and rupture of the battery. The model can effectively fit the battery thermal runaway behavior under both adiabatic thermal runaway and oven test conditions. Model-based analyses show that the thermal runaway features and characteristic temperatures are significantly affected by the test conditions, thermocouple positions, and battery thickness. The onset temperature of thermal runaway (T2) obtained in the oven test is 48.1 °C lower than that obtained in the adiabatic thermal runaway test. The measured T2 varies at different positions, and the difference can exceed 20% when the battery thickness increases to 10 cm. Moreover, the maximum thermal runaway temperature (T3) at the surface is approximately half that at the other positions. Finally, several suggestions for reasonable thermocouple placement are proposed, which can provide useful guidance for accurately evaluating battery thermal runaway performance.

Experimental Validation and Parameter Study of a 2D Geometry-Based, Flexible Designed Thermal Motor Model for Different Cooled Traction Motor Drives

For identifying new improvement potentials for electric traction motors, accurate models are needed. In this paper, a geometry-based 2D lumped parameter thermal network model for different electric traction motor and cooling concepts is studied and validated. In the second section, the design and functionality of the thermal model is explained. In the third section, the best fit of the literature correlations for describing the different heat transfer mechanisms was identified and a parameter study of the heat transfer coefficients was carried out and discussed. In the last section, the model is validated with measurement results from six different electric traction motors and drives units. For validation measurement results of stationary operating points, peak operating points and drive cycles are used. Based on the validation results, a model error of less than 10% is achieved for the most motor components in the different cooling concepts and traction motor designs. Inaccuracies and deviations are discussed and suggestions for improvement are made.

Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Thermal deformation caused by temperature rise affects the contact status of bearings in motorized spindles. In turn, the change in the contact status will affect the temperature rise and thermal deformation of the system. However, the latter has been rarely focused on in the previous literature. Therefore, a thermal network model of motorized spindle is enhanced by considering the thermal–mechanical coupling effect. Then, an iterative method is presented to solve the coupled equations, and a temperature test rig of the motorized spindle is set up to verify the proposed model. The predicted results by the proposed model are compared with the experimental results and the predicted results by the previous model. The relative error between the predicted and experimental results at two test points decreases by 9.56% and 3.44%, respectively, after considering the thermal–mechanical coupling effect. The temperature changes the contact angle and contact load of the bearings, thereby causing changes in frictional heat and temperature field. Such changes cause the difference between the proposed and previous models. The comparison with the experimental results shows that the proposed model with thermal–mechanical coupling effect can obtain a more accurate temperature field than the previous model.

A dynamic state-space model for predicting the thermal performance of ventilated electric heating mortar blocks integrated with phase change material

Thermal active building system (TABS) is promising to increase the flexibility of building energy systems. In this research, the thermal characteristics of a ventilated mortar block in the operation mode that the air is directly exhausted are analyzed by models. The block is integrated with phase change material (PCM) and ventilation tubes to increase both heat storage capacity and heat extraction flexibility. The simplified thermal model of the block, which combines the thermal network model and number of transfer unit model (NTU), is established and rearranged in standard state-space form based on the experimental system. The calculation process is also improved. Different modeling methods are compared, and the accuracy of the model is also validated by the testing data of three kinds of ventilated mortar blocks, which have different layers of PCM inside. Furthermore, parametric analysis is also conducted. The results show that for the current operational mode where the air is directly exhausted, the thickness of the mortar block and the diameter of the tube have a dominant impact on the surface and outlet air temperature, while velocity has a lower impact on the outlet air temperature.

Power Dense HTS Networks – Designs For Contingencies and Resiliency of Electric Transport Systems

The ability of HTS cable networks to provide a solution for the high power density requirements of electric transportation applications was explored. The high current density and the temperature-dependent critical current features of HTS cables were shown to allow resilient designs for power distribution in electric transportation applications. Heat loads at the terminations are shown to be the primary challenge. Appropriate designs in terms of ampacities of the current leads are essential in using the beneficial features of HTS cables. Thermal network models were used to understand the implications of using HTS cables to carry the necessary additional current to mitigate contingencies and to aid in the designs of effective HTS cable networks for electric aircraft and ships.

Thermal network modeling of tunnel cable considering circumferential heat transfer and tunnel curvature

Taking wind direction and flow separation into account, the circumferential temperature difference of cable laid in forced-ventilation tunnel is investigated. Combining the heat conduction, the convection and the radiation, the formula calculating the thermal resistance is derived, followed by cable thermal network model in tunnel. The circumferential heat transfer and temperature profile for tunnel cable is analyzed. The results indicate that the temperature of cable is not only transmitted radially, but also circumferentially. The temperature on the leeward side is higher than that on the windward side. As the load increases, the circumferential temperature difference rises. The larger the wind direction angle, the smaller the temperature difference. When the angle is greater than 40°, continue to increase the angle does not have a great impact on the heat dissipation and temperature difference. It suggests that the temperature calculated by IEC-60287 is conservative as it does not take account of multi-factor interactions. The transient thermal network is also established.

Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

Analytical thermal resistance model for calculating mean die temperature of eccentric quad flat no-leads packaging on printed circuit board

Quad Flat No-leads (QFN) packaging is one of the most commonly used packaging types for microelectronic devices due to its outstanding thermal performance. However, it is difficult to analyze the thermal performance of QFN packaging because numerical simulations and experiments often require a large amount of time and resources. In this work, an analytical thermal resistance network model for calculating the mean die temperature of eccentric QFN packaging on the printed circuit board (PCB) is presented. Based on the heat dissipation path from the die to ambient, the thermal resistance network is established. The analytical solution of the mean die temperature of QFN packaging is obtained by the analytical expressions of each thermal resistance in the thermal resistance network. Simulations by commercial software ANSYS provide a reference for verification of the present model. The comparison of the results between simulation and analytical solution shows that the network model can calculate the mean die temperature of QFN packaging accurately over wide ranges of PCB’s thermal conductivity and heat transfer coefficient, and the maximum relative error is only 4.73%. The present model is fast and resource-saving to predict the mean die temperature of QFN packaging; moreover, it provides an optimization method for QFN packaging design to achieve good reliability.

Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems

This paper presents control strategies to activate energy flexibility for zones with radiant heating systems in response to changes in electricity prices. The focus is on zones with radiant floor heating systems for which the hydronic pipes are located deep in the concrete and, therefore, there is a significant thermal lag. A perimeter zone test-room equipped with a hydronic radiant floor system in an environmental chamber is used as a case study. A low order thermal network model for the perimeter zone, validated with experimental measurements, is utilized to study various control strategies in response to changes in the electrical grid price signal, including short term (nearly reactive) changes of the order of 10–15 min notice. An index is utilized to quantify the building energy flexibility with the focus on peak demand reduction for specific periods of time when the electricity prices are higher than usual. It is shown that the developed control strategies can aid greatly in enhancing the zone energy flexibility and minimizing the cost of electricity and up to 100% reduction in peak power demand and energy consumption is attained during the high-price and peak-demand periods, while maintaining acceptable comfort conditions.

Optimization Design of Oil-Immersed Iron Core Reactor Based on the Particle Swarm Algorithm and Thermal Network Model

In this paper, according to the design parameters of oil-immersed iron core reactor, the thermal network model of windings is established by the thermo-electric analogy method, and the temperature distribution of the windings can be obtained. Meanwhile, a fluid-thermal coupled finite element model is established, the temperature and fluid velocity distribution are extracted, and the simulation results show that the error coefficient of temperature is less than 3% compared with the thermal network model, so the correctness of thermal network model has been verified. Taking the metal conductor usage and loss of windings as the optimization objects, the optimization method based on the particle swarm algorithm and thermal network model is proposed, and the Pareto optimal solutions between the metal conductor usage and loss of windings are given. The optimization results show that the metal conductor usage is reduced by 23.05%, and the loss is reduced by 20.25% compared with the initial design parameters, and the maximum temperature of winding does not exceed the expected value. Thus, the objects of low metal conductor usage and loss of windings are conflicted and cannot be optimized simultaneously; the optimization method has an important guiding significance for the design of oil-immersed iron core.

Using scaled FE solutions for an efficient coupled electromagnetic–thermal induction machine model

PurposeThis paper aims to use a scaling approach to scale the solutions of a beforehand-simulated finite element (FE) solution of an induction machine (IM). The scaling procedure is coupled to an analytic three-node-lumped parameter thermal network (LPTN) model enabling the possibility to adjust the machine losses in the simulation to the actual calculated temperature.Design/methodology/approachThe proposed scaling procedure of IMs allows the possibility to scale the solutions, particularly the losses, of a beforehand-performed FE simulation owing to temperature changes and therefore enables the possibility of a very general multiphysics approach by coupling the FE simulation results of the IM to a thermal model in a very fast and efficient way. The thermal capacities and resistances of the three-node thermal network model are parameterized by analytical formulations and an optimization procedure. For the parameterization of the model, temperature measurements of the IM operated in the 30-min short-time mode are used.FindingsThis approach allows an efficient calculation of the machine temperature under consideration of temperature-dependent losses. Using the proposed scaling procedure, the time to simulate the thermal behavior of an IM in a continuous operation mode is less than 5 s. The scaling procedure of IMs enables a rapid calculation of the thermal behavior using FE simulation data.Originality/valueThe approach uses a scaling procedure for the FE solutions of IMs, which results in the possibility to weakly couple a finite element method model and a LPTN model in a very efficient way.

Control method for cooling fans inside traction battery boxes installed in battery powered EMU based on thermal network model

AbstractRecently, there has been a gradual increase in the use of battery powered electric multiple unit trains (EMUs), replacing the conventional diesel trains for nonelectrified lines of approximately 10–30 km. Battery powered EMUs are typically equipped with closed‐type traction battery boxes, in which cooling fans are mounted. To improve the performance of battery cooling, we developed a control method for the cooling fans. Our novel method is derived from a thermal network model of the battery box, and is based on the temperature difference between the battery modules and outside air. We conducted an evaluation test for the Series BEC819, an AC‐fed and battery‐powered hybrid EMU of JR Kyushu. The test results demonstrated that the developed control method reduced maximum temperature of the battery by 1–2°C in comparison with the conventional constant speed control.

Investigation on Single Pulse Avalanche Failure of 1200-V SiC MOSFETs via Optimized Thermoelectric Simulation

The dynamic avalanche reliability of 1200-V silicon carbide (SiC) power metal-oxide semiconductor field-effect transistors (MOSFETs) is studied in this article. The unclamped inductive switching (UIS) tests are conducted to locate failure points. An optimized thermal network model with the definition of the material above the epitaxial layer is used to simulate the avalanche process of SiC MOSFETs. The simulation and experiment results are matched, which verifies the validity of this model. Further simulation results show that a slight change in the doping profile of the p-well region will make the avalanche capability significantly different. Then, the effect of the deviation in cell parameters on the avalanche capability is studied by multicell simulation. The results demonstrate that uneven distribution of internal parameters makes the parasitic bipolar junction transistor (BJT) of some cells turn-on first, causing a significant concentration of current and heat in a very short time, and eventually forming hot spots often observed in failed devices.

Scrutinizing modeling and analysis methods for evaluating overheating risks in passive houses

In response to the increasing demand for energy efficient houses, passive houses are quickly becoming a required standard for new buildings. Despite their energy efficiency, the occupants have reported thermal discomfort during the summer. This study investigates the effects of choices made in the process of evaluating overheating risks in passive houses. Key questions raised in this study relate to the effects of different thermal comfort criteria, the relevance of the building’s energy model to represent airflow phenomena, and the role of uncertainty associated with simulation models utilized for the analysis. The overheating assessment results were observed to be highly dependent on thermal comfort criterion choices, and the energy model was found to undervalue the overheating hours by 33.33% due to design infiltration and ventilation rate overestimation. This result highlighted the necessity of properly modeling airflow phenomena by coupling the thermal and airflow network models for overheating analysis. The sensitivity analysis results indicated that although most of the dominant uncertain parameters are included in the thermal model, when analyzing the output variance caused by the uncertainty related to each modeling method, both the thermal (82.5%) and airflow network (17.5%) models showed a significant variance in the number of overheating hours.

Thermal Analysis of Axial Permanent Magnet Flywheel Machine Based on Equivalent Thermal Network Method

The axial permanent magnet magnetic bearingless flywheel machine (APM-BFM) mostly operates under the state of high-power fast charging and discharging. Due to the characteristics of high-power density per unit volume, the centralized windings have small heat dissipation area and high thermal loads, the temperature rise on APM-BFM is a very important issue. Thermal analysis is thus of particular importance particularly for modern machines since the temperature rise and distribution are significant during their running under higher electromagnetic loads commonly used nowadays. In this paper, the equivalent thermal network method is proposed to evaluate the temperature rise of the APM-BFM. The equivalent thermal network model is established based on the basic theorem of heat transfer. The thermal resistance and heat source in the machine are analyzed and calculated, and the thermal balance equation is established. The temperature rise of each node is solved, and finally the validity of the model is verified by finite element magnetic-thermal coupling simulation.

Analysis on heat transfer and start-up performance of mercury heat pipe

Mercury heat pipe has the advantages of good thermal stability and low saturated vapor pressure, which is the best choice for the transition from water heat pipe to liquid metal heat pipe. The effects of heating power and heat pipe structure on start-up time and steady-state heat transfer performance of mercury heat pipe were studied by using transient thermal network model. The results showed that: 1) Increasing the length of condenser is beneficial to reducing the start-up time and thermal resistance; 2) Increasing the heating power or wall thickness will reduce the thermal resistance, but increase the start-up time, and increasing the porosity of wick is just the opposite; 3) Increasing the thickness of wick can increase both the start-up time and the thermal resistance.

Precise multi‐dimensional temperature‐rise characterisation of switchgear based on multi‐conditional experiments and LPTN model for high‐capacity application

As the power load increases, the capacity of switchgear becomes larger so the problem of overheating of switchgears becomes more significant. Whether the switchgears operate safely and reliably affects the reliability and economy of the entire power system. Different methods have been tried to study the thermal problems of switchgears. However, there are few experimental studies on the switchgear entity. In this study, a series of temperature rise tests were carried out on medium-voltage high-capacity switchgear to explore laws of temperature rise. First, five groups of multi-conditional experiments were carried out including changes of the load current, ventilation conditions and loop resistance. Then, multi-dimensional temperature-rise characterisation was analysed based on the experimental results and the relating theory. Finally, a circuit-based lumped-parameter thermal network (LPTN) model was developed by analysing the heat dissipation in switchgear and used to determine the steady-state temperature distribution of the switchgear. The model is verified by comparing the simulation results with the experimental results.

A novel cooling geometry for subsea variable speed drives

Experimental and theoretical analyses are conducted to evaluate the passive cooling performance of a novel geometry for subsea variable speed drives, a common piece of equipment in deep-sea oil exploration. Relying on the sea water as a low-temperature thermal reservoir, the new design forms an enclosed, annular space with centrally located modular boards that compose the power electronics inverter. Buoyancy-induced motion of a dielectric coolant conveys the heat dissipated by the electronic boards to the sea water through the outer and innermost walls of the annular enclosure. A thermal network model is implemented and used to optimize the enclosure geometry through a genetic algorithm, which served as a reference for a scaled experimental setup. A Computational Fluid Dynamics (CFD) simulation of the conjugate heat transfer yielded temperature distributions on the electronic boards and temperature and fluid velocity fields inside the enclosure. A comparison between the experimental data and the modeling results indicated a good agreement, with average RMS deviations of a modified Nusselt number of 7.0% and 8.5% for the thermal network and CFD analysis, respectively. For a 140-W operating point dissipation rate in the scaled test setup, the thermal network and the CFD models presented maximal deviations of 4°C and 2.3°C with respect to the heat sink temperature measurements.

Nonlinear tribo-dynamic model and experimental verification of a spur gear drive under loss-of-lubrication condition

Gearbox is required to serve for ≥ 30 min in helicopter design to improve survivability, when the gear drive loses lubrication due to battle damage. Under such loss-of-lubrication condition, tooth friction, wear and temperature pose serious problems to dynamic performance of gears. Therefore, a nonlinear tribo-dynamic model of spur gear drives is established and experimentally validated in this work. The time-varying friction coefficient is predicted under loss-of-lubrication condition on the basis of computational inverse technique. The temperature rise and tooth wear caused by friction were included by using thermal network model and dynamic wear model. The flexibility of gear shafts and the gyroscopic effect of gear rotors are also considered. The results indicate the gyroscopic effect apparently affects the natural frequency of the gear drive, while friction has negligible effect on the natural frequency but has considerable effect on the nonlinear behavior. The influence of temperature and gyroscopic effect on nonlinear behavior are reflected in the region of middle and high speed, while tooth wear mainly affects the bifurcation at the middle speed. In addition, tooth wear not only aggravates gear vibration but also can change the phase of dynamic transmission error. The analysis and experiment results can be an important reference in reducing friction and wear, restraining nonlinear behavior, and reducing vibration and noise.

Review of physical and mathematical modelling aspects of thermal management of induction motors

This review paper summarizes the importance of thermal management system and mathematical models used in the prediction of temperature distribution of an induction motor. The lumped parameter thermal network modelling approach is explained in detail with a suitable example. The recent computational methods and their pros and cons on estimating the temperature distribution are discussed. The experimental method on estimating equivalent value for thermal resistance and capacitance is highlighted from the literature. The understanding on physical mechanism of heat transfer is emphasized to recognize thermal flow path for better utilization of cooling techniques. The recent trend in coupling electromagnetic and thermal phenomena is also considered. This study will be helpful on future design of induction motor to meet difficult constraints.