Lumped Parameter Thermal Network Research Articles

Influence of Insulation Thermal Aging on the Temperature Assessment in Electrical Machines

Thermal modeling and temperature assessment of electrical machines often rely on the use of lumped-parameter thermal networks. A historic limitation of analytical thermal models is their need for an experimental fine-tuning, necessary for selecting the appropriate values of thermal conductivity and convection heat transfer coefficients. This evaluation procedure is commonly carried out at the design stage of a new machine, by assuming that its thermal behavior will remain unchanged throughout its whole lifetime. This paper demonstrates, through an in-depth experimental investigation, how the capability of heat extraction from a machine's hot spot towards the coolant can be strongly affected by the level of thermal aging of its insulation system. Based on the experimental findings, a decrement of the winding equivalent thermal conductivity is noted as the thermal aging accumulates, with a corresponding progressive increment in hot-spot temperature.

Induction Motor Thermal Analysis Based on Lumped Parameter Thermal Network

Many industry applications required the use of the induction motors. In such envirenement the electrical machines are facing of many stressed operating conditions. One of the critical creteria which decide the choice of the induction motor is the thermal behaviour under different mode operation. In this paper a study of the thermal behavior of an induction motor is presented. In order to predict the temperature in the different machine components, a model based on the lumped parameter thermal network has been developed. The geometry of the machine and the thermal properties of its various components are used to express the developed model. The joule and the iron losses are considering as the inputs. The proposed model is implemented and tested using MATLAB software. It is a simple model which could predict rapidly the different temperatures.
 Keywords: Induction motor, Thermal analysis, Lumped parameters thermal network, Modeling, Heat sources

Electric Motor Design of an Integrated Motor Propulsor for Unmanned Vehicles: The Effect of Waterproofing Can

This study investigates the effect of waterproofing can on the electromagnetic performance and thermal characteristic of the electric motor, which is a major part of an integrated motor propulsor for unmanned vehicles. To satisfy the design target, the electromagnetic performance of a designed motor with variable thickness and materials of waterproofing can was examined by two-dimensional finite element analysis (FEA) considering its eddy current loss. The thermal problem of the motor with waterproofing was solved by using an analytical lumped parameter thermal network method based on the motor losses which was obtained from FEA. A 39-kW electric motor and proper waterproofing can be manufactured and tested to verify the analytical expectation.

Thermal modeling and measurement of a drive motor in a pure electric vehicle

This research is concerned on the thermal modeling and measurement of an electric drive motor. The motor is a high-efficiency permanent magnet synchronous motor (PMSM) and is actively cooled by circulating coolant through a water jacket. A lumped-parameter thermal network (LPTN) based on the Amesim is established for thermal modeling, and the temperature histories of rotor and copper winding are obtained in a condition of 120kph@3% grade. Slip ring is employed to handle the problem of effectively testing the temperature of motor rotor. The LPTN thermal model is validated by comparing simulation results with the test ones. Based on this equivalent thermal model, temperature distribution along axial direction of the motor can be obtained. The proposed LPTN model is useful for motor design to avoid over heat and demagnetization.

An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor

In a traditional lumped-parameter thermal network, no distinction is made between the heat and non-heat sources, resulting in both larger heat flux and temperature drop in the uniform heat source. In this paper, an improved lumped-parameter thermal network is proposed to deal with such problems. The innovative aspect of this proposed method is that it considers the influence of heat flux change in the heat source, and then gives a half-resistance theory for the heat source to achieve the temperature drop balance. In addition, the coupling relationship between the boundary temperature and loading position of the heat generator is also added in the lumped-parameter thermal network, so as to amend the loading position and nodes’ temperature through iterations. This approach breaks the limitation of the traditional lumped-parameter thermal network: that the heat generator can only be loaded at the midpoint, which is critical to determining the maximum temperature in asymmetric heat dissipation. By adjusting the location of heat generator and thermal resistances of each branch, the accuracy of temperature prediction is further improved. A simulation and an experiment on a U-core motor show that the improved lumped-parameter thermal network not only achieves higher accuracy than the traditional one, but also determines the loading position of the heat generator well.

Improved thermal model of forced air‐cooled motors considering heat transfer in wire‐wound winding and end region

This study introduces an innovative thermal model to accurately consider the heat transfer in the winding and end region of forced air-cooled motors, which is an attractive supplement of classical lumped parameter thermal network approaches. Through coupling of the analytical method and local numerical calculations, the effective length of the winding heat path and the air convection of the end region can be exactly considered. An improved analytical derivation of the equivalent thermal conductivity of the winding is applied. To reduce the errors caused by the irregular slot type, the winding thermal resistance is calculated by the 2D numerical calculations. After that, the 3D model of the end region is built to accurately calculate the end air velocity and end convection resistance. The unknown boundaries of the 3D end model are determined by a coupling iteration. The errors of the winding thermal resistance and end convection resistance are less than 3.12 and 11%, respectively, verified by simulations and temperature tests of the prototype. Finally, the mechanism of the end region convection and its influence on the temperature rise of the entire motor are discussed in detail.

Thermal Model Approach to Multisector Three-Phase Electrical Machines

Multisector machines reveal a high fault-tolerant capability, since failure events can be isolated by de-energizing the faulty sector, while the healthy ones contribute in delivering the required power. This article is focused on the thermal analysis of multisector three-phase machines in healthy and faulty operations. First, a 3-D lumped parameter thermal network (LPTN) of a single sector is developed and finetuned against experimental data, through a genetic algorithm for identifying the uncertain parameters. According to the operating conditions, the varying housing surface temperature affects the heat exchanged to the ambient. Hence, an analytical formula is proposed to adjust the natural convection coefficient value depending on the operating condition. Then, the 3-D LPTN, modeling the whole machine, is built aiming at investigating the thermal behavior during faulty conditions. Finally, the complete 3-D LPTN is employed for predicting the machine thermal performance under several faulty conditions. Furthermore, the current overload experienced by the healthy sector (in order to keep the same torque level as during the pre-fault operation) is determined, in accordance with the magnet wire thermal class. The effectiveness of the 3-D LPTN in predicting the temperature is experimentally demonstrated.

Online Estimation Method for Permanent Magnet Temperature of High‐Density Permanent Magnet Synchronous Motor

Since it can be difficult to directly estimate the temperature of permanent magnets (PM) in high‐density permanent magnet motor rotors, this paper proposes a method based on lumped parameter thermal networks (LPTN) for online estimation of PM temperature. Firstly, a parameter model is established that contains a four‐node thermal network model and a loss model. For the online estimation, an extended Kalman filter algorithm with fading factor is used to dynamically update the thermal network parameter model in order to estimate the PM temperature. The simulation and experimental results show that the parameter model can quickly converge to a value that is close to the true value. Therefore, this online estimation method for PM temperature can accurately monitor the PM temperature online without increasing the required motor hardware; thus not affecting the motor operation. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An Improved Analytical Model of Permanent Magnet Eddy Current Magnetic Coupler Based on Electromagnetic-Thermal Coupling

There is a serious nonlinear between the sensitive parameters and the transmission capacity of the permanent magnet eddy current magnetic coupler (PMEC), and how to establish the accurate analytical model is very important to study its characteristics, optimize design and improve control accuracy. In order to solve the problem that the accuracy of analytical model deteriorates under the variable parameters of the PMEC, based on the electromagnetic-thermal theory, the electromagnetic-thermal nonlinearity of materials, such as the thermal effect of magnetic properties, the thermal effect of permeability, the thermal effect of conductivity, the magnetic saturation effect, the skin effect, the eddy current inductive reactance, have been deeply studied in this paper. On this basis, the multi-field coupling analytical model of the PMEC sensitive parameters and material electromagnetic thermal nonlinearity is established by using the magnetic equivalent circuit theory and lumped parameter thermal network method. Combined with numerical simulation and experiment, the research on electromagnetic characteristics, temperature characteristics and transmission characteristics of the PMEC are carried out. The comparative study shows that the proposed analytical model can accurately analyze the electromagnetic characteristics, temperature characteristics and transmission characteristics of the PMEC without any correction coefficient under different parameters. The accuracy of the analytical model with variable parameters is effectively solved, and the model theory of the PMEC is improved. The accurate analytical modeling method has laid theoretical foundation for the rapid optimization design and wide application of the PMEC.

Design of Surface-mounted Permanent Magnet Synchronous Motor using Electromagnetic and Thermal Analysis

This paper proposes a design method of the surface-mounted permanent magnet synchronous motor using electromagnetic and thermal analysis. Since the electromagnetic and thermal fields are related, the permanent magnet synchronous motor should be considered not only in terms of the power density but also the thermal characteristics. The analytic method was used to investigate the power density of the concentrated winding model using the same number of poles. In the thermal design process, the analytic prediction was carried out by using the electromagnetic and thermal analysis called the lumped parameter thermal network (LPTN). The optimized geometry and losses which were calculated by the electromagnetic finite-element analysis were considered in the LPTN. As a result, an improved model was designed with superior power density and thermal characteristics to the prototype. Finally, the experiments were conducted to verify the validity of the design process and results.

Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model

Accurate temperature estimation inside an electrical motor is key for condition monitoring, fault detection, and enhanced end-of-life duration. Additionally, thermal information can benefit motor control to improve operational performance. Lumped-parameter thermal networks (LPTNs) for electrical machines are both flexible and cost-effective in computation time, which makes them attractive for use in real-time condition monitoring and integration in motor control. However, the accuracy of these thermal networks heavily depends on the accuracy of its system parameters, some of which are difficult to calculate analytically or even empirically and need to be determined experimentally. In this paper, a methodology for the thermal condition monitoring of long-duration transient and steady-state temperatures in an induction motor is presented. To achieve this goal, a computationally efficient second-order LPTN for a 5.5 kW squirrel-cage induction motor is proposed to apprehend the dominant heat paths. A fully thermally instrumented induction motor has been prepared to collect spatial and temporal temperature information. Using the experimental stator and rotor temperature data collected at different motor operating speeds and torques, the key thermal parameter values in the LPTN are identified by means of an inverse methodology that aligns the simulated temperatures of the stator windings and rotor with the corresponding measured temperatures. Validation results show that the absolute average thermal modelling error does not exceed 1.45 °C with maximum absolute error of 2.10 °C when the motor operates at fixed speed and torque. During intermittent motor-loading operation, a mean (maximum) stator temperature error of 0.38 °C (0.92 °C) was achieved and mean (maximum) rotor errors of 2.11 °C (3.40 °C). These results show the validity of the proposed thermal model but also its ability to predict in real time the temperature variations in stator and rotor for condition monitoring and motor control.

A Simplified Thermal Model and Online Temperature Estimation Method of Permanent Magnet Synchronous Motors

Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of ±5 °C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.

Thermal Analysis of Modular-Spoke-Type Permanent-Magnet Machines Based on Thermal Network and FEA Method

Modular-spoke-type permanent-magnet (MSTPM) machines are novel in-wheel traction machines for electric vehicles (EVs). Considering the strict constraints including the mass and volume of electric machines in EVs, which are heavily dependent on the dissipation condition, an accurate thermal model and resultant temperature distribution analysis are key challenges. Therefore, in this paper, a lumped parameter thermal network (LPTN) model for thermal analysis of MSTPM machines is proposed, where the heat transfer coefficients are determined theoretically instead of empirically, which means that the proposed LPTN model can be applicable for the MSTPM machines with different specifications and parameters. Both the steady-state and transient-state temperature rises are analyzed by the LPTN model, and the results are verified by both 3-D finite element analysis (FEA) predictions and experiments, indicating that the proposed method has advantages in both computational efficiency and accuracy.

Improved Thermal Management and Analysis for Stator End-Windings of Electrical Machines

In an electrical machine design, thermal management plays a key role in improving the performance and reducing size. End-windings are commonly identified as the machine hot-spot. Hence, lowering and predicting end-windings temperature are crucial tasks in thermal management of electrical machines. This paper proposes and investigates a noninvasive but effective cooling method that aims for a uniform cooling of a machine's winding by implementing direct cooling on its end-windings. Modeling and experimental results show that a 25% hot-spot temperature reduction on a particular application can be achieved. To analyze the proposed technique in detail, an accurate but computationally economic lumped parameter thermal network is developed. Comparison between a “standard” thermal network and its simplified equivalent (with less nodes) is presented where the models are developed and fine-tuned based on experimental data. All the above is used to investigate the potential of the proposed end-winding cooling method with different configurations of the methodology.

Fast Iron Loss and Thermal Prediction Method for Power Density and Efficiency Improvement in Switched Reluctance Machines

To effectively find an optimal solution in the development process of electrical machines, it is essential to predict machine iron loss and thermal behavior in the predesign stage. Most existing publications only consider thermal behavior by roughly limiting the maximum current density. However, especially for high-speed application, iron loss can also be an essential part in thermal-behavior estimation. This paper proposes a simplified model to efficiently calculate iron loss and thermal behavior in the design procedure of switched reluctance machines. For this purpose, a maximum coenergy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. Besides, a simplified lumped parameter thermal network is applied for machine sizing. Furthermore, the impact of number of turns and peak magnetomotive force on the losses and machine sizing is discussed. The efficiency and power density of various machine geometries are comparatively analyzed. Finally, the accuracy of the simplified model is verified by comparison with finite element simulations and experiment results.

Sensitivity analysis using Sobol indices for the thermal modelling of an electrical machine for sizing by optimization

Purpose The thermal modelling of an electrical machine is difficult because the thermal behavior depends on its geometry, the used materials and its manufacturing process. In the paper, such a thermal model is used during the sizing process by optimization of a hybrid electric vehicle (HEV). This paper aims to deal with the sensitivities of thermal parameters on temperatures inside the electrical machine to allow the assessment of the influence of thermal parameters that are hard to assess. Design/methodology/approach A sensitivity analysis by Sobol indices is used to assess the sensitivities of the thermal parameters on electrical machine temperatures. As the optimization process needs fast computations, a lumped parameter thermal network (LPTN) is proposed for the thermal modelling of the machine, because of its fastness. This is also useful for the Sobol method that needs too many calls to this thermal model. This model is also used in a global model of a hybrid vehicle. Findings The difficulty is the thermal modelling of the machine on the validity domain of the sizing problem. The Sobol indices allow to find where a modelling effort has to be carried out. Research limitations/implications The Sobol indices have a significant value according to the number of calls of the model and their type (first-order, total, etc.). Therefore, the quality of the thermal sensitivity analysis is a compromise between computation times and modelling accuracy. Practical implications Thermal modelling of an electrical machine in a sizing process by optimization. Originality/value The use of Sobol indices for the sensitivity analysis of the thermal parameters of an electrical machine.

Thermal management of a Formula E electric motor: Analysis and optimization

The thermal analysis of a high performance brushless synchronous electric motor with permanent magnets and water jacket cooling is presented. The analysis is carried out following a lumped parameter thermal network approach which allows to identify the most important thermal paths in the motor and the main parameters influencing them. Thanks to its simplicity, the solution of such a thermal network model is very fast, allowing a large number of what-if scenarios to be computed over a short amount of time. For this reason, the model is coupled with external tools for performing systematic sensitivity analyses and optimizations. Goal of the investigation is the reduction of the windings temperature being this temperature inversely proportional to the efficiency and the power delivered by the motor. The sensitivity analysis, performed over a series of material, geometric, and operational factors, leads to the identification of the most relevant parameters influencing the thermal behaviour of the motor. A series of optimizations, focusing on these parameters and including suitable constraints granting the well-posedness of the problem and the feasibility of the solution, bring to the definition of an optimum layout of the water jacket and of the stator geometries. The optimized geometry allows a significant reduction of the windings temperature to be achieved.

Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter

In this paper, a new temperature observer topology is presented which overcomes the shortcomings of previous ones and achieves a higher accuracy, and a more robust disturbance rejection. It makes use of the Gopinath-style flux observer and combines a lumped-parameter thermal network operating at low speeds and a flux-based permanent magnet temperature observer operating at medium and high speeds. Simulation and experimental results on a 50 kW permanent magnet motor show a performance enhancement over standard topologies; particularly, a superior disturbance rejection to voltage estimation errors. A detailed analysis of the optimal controller tuning is also presented. Furthermore, a Kalman filter is incorporated to account for sensor noise and model uncertainties. Experimental results show an effective fusion of independent temperature estimation methods leading to a superior accuracy compared to the previously investigated approaches. Moreover, the Kalman filter-based fusion offers the capability of detecting temperature-related system failures, e.g., cooling circuit malfunctions.

Electromagnetic and thermal analysis and design of a novel‐structured surface‐mounted permanent magnet motor with high‐power‐density

A segmented-core (SC) structure has been widely used for high-power-density (HP) motors. However, the SC motor is associated with a number of problems due to the complexity of both the structure and the manufacturing process. To address these issues, a novel structure of a HP motor is proposed, referred to as the ring-coupled segmented-stator (RSS) model here. The proposed RSS can increase the reliability, the stability, and the manufacturability of motor. Furthermore, useful thermal analysis and design flow which take into account the RSS and asymmetric overhang structure of the motor are proposed in this research. The proposed lumped parameter thermal network (LPTN) for the thermal analysis shows a good agreement with experimental data within 9.8% difference. The proposed analysis and design method can be used for the diverse kinds of motor requiring the HP. The usefulness of the proposed RSS motor, the analysis method, and the design method are verified through the experiment in this research.

Thermal Analysis of a Flux-Switching Permanent-Magnet Double-Rotor Machine With a 3-D Thermal Network Model

A fast and precise lumped-parameter thermal network (LPTN) model is presented for a flux-switching permanent-magnet double-rotor machine used for hybrid electric vehicles (HEVs), which has attracted attention due to the compact structure. The three-dimensional (3-D) LPTN model provides the steady-state thermal solution to derive the temperatures at different parts of the machine, in which the power losses of various working conditions are used as heat sources for calculating the temperatures of different operational modes. In the LPTN studies, a transformation of an irregular slot structure into a regular one is applied to simplify the calculation of the winding thermal resistance. A 3-D finite element thermal model of this machine is also established to calculate the temperatures of the main machine components for a comparative study purpose. Tests performed on the designed machine verify that the defined LPTN model has a high ability to predict the nodal temperatures accurately.