Lumped Parameter Thermal Network Model Research Articles

Lumped Parameter Thermal Network Modeling and Thermal Optimization Design of an Aerial Camera.

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera's unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.

Thermal and loss modeling of scaled permanent magnet synchronous machines for automotive driving cycles

With the goal of enhancing the efficiency of electric vehicles and improving the driving range, the appropriate dimensioning, so-called “right-sizing,” of the electric machines is a promising approach. This paper presents an efficient approach of scaling the thermal parameters in a low order lumped parameter thermal network (LPTN) model to estimate the temperature of the scaled permanent magnet synchronous machine (PMSM) by taking into account the temperature dependence of the power losses. At an early stage of development, the proposed scaling approach enables a preliminary evaluation of the thermal limit of the PMSM within the optimization of the electric powertrain and offers a notable benefit in that the low order LPTN model can easily be parameterized and scaled based on the experimental measurements. Validation for both axial scaling and radial scaling with factors ranging from 0.8 to 1.2 was carried out on a previously validated Ansys Motor-CAD model for typical automotive driving cycles. The maximum temperature error between the scaling approach and the Ansys Motor-CAD model is less than 3.5°C.

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

Transient Thermal Exchange Analysis of an External Rotor Hub Motor for Electrified Defense Vehicles Applications Based on FVM and TNM

This paper investigates the transient temperature distribution law of a 75kW external rotor hub motor for electrified defense vehicles (EDV) using the finite volume method (FVM) and the thermal network method (TNM). Based on thermal exchange and fluid flow theories, both a transient fluid-solid coupled thermal exchange model and a lumped parameter thermal network model (LPTNM) with transient thermal capacity parameters are established. An iterative algorithm for the heat transfer coefficient is proposed to improve the accuracy of LPTNM. Compared with existing methods, this algorithm exhibits higher accuracy and stronger practicality. In addition, an experimental platform of the hub motor temperature rise is built for measuring the end winding temperature. The calculated results are in good agreement with the experimental results, verifying the rationality of the 3D transient fluid-solid coupled thermal exchange model and the LPTNM. The axial parallel water channel structure was adopted to improve the cooling performance of the end winding, taking into account the structural characteristics and assembly mode of the hub motor. This paper provides theoretical references for the transient thermal analysis and cooling structure design of the EDV hub motor.

Thermal Modeling and Analysis of Hybrid-Magnetic-Circuit Variable Flux Memory Machine

Variable flux memory machine (VFMM) can be regarded as a promising candidate for electric vehicles (EVs) owing to its advantages of excellent flux adjustment capability and overall efficiency improvement over an extended operating range. However, the uncertain internal thermal behavior due to magnetization state (MS) change has an immense influence on electromagnetic performance of permanent magnet, which makes the precise MS estimation of low coercive force PM a challenging issue. To explore the benefits of MS manipulation on temperature rise reduction and provide an effective method to accurately evaluate temperature rise in VFMM, a magnetic-thermal coupling model of the newly developed hybrid-magnetic-circuit VFMM accounting for different MSs is established in this article. The lumped parameter thermal network (LPTN) model and computational fluid dynamics based finite-element (FE) method are employed to analyze the temperature characteristics, respectively. First, the losses of the investigated machine are calculated by adopting both the FE method and analytical equations. Afterward, the equivalent thermal resistances and heat transfer coefficients are determined by employing the LPTN model. Meanwhile, FE modeling is employed to analyze the three-dimensional thermal field distribution. Finally, a prototype machine is fabricated and experimentally measured, which can confirm the validity of the theoretical analyses.

Development and validation of lumped parameter thermal network model on rotational oil spray cooled motor for electric vehicles

The rotational oil-spray-cooling method with motor has recently attracted attention because of its compact design and cooling performance. Rotational oil-spray-cooled motors require high computational resources and manufacturing costs; therefore, a precise simulation model is required. In this study, an asymmetric lumped parameter thermal network (LPTN) model of a rotational oil-spray-cooling motor is developed. The heat loss is calculated using correlation equations and electro-magnetic analysis, and the internal temperature distribution of the motor is predicted using conjugate heat transfer and multiphase flow computational fluid dynamics (CFD) analysis. The temperature of the coil inside the motor is measured using experiments. The developed LPTN model determined that the temperature prediction errors of coil parts were 0.15% and 4.42% at the nominal and maximum speeds, respectively.

A Simplified LPTN Model for a Fault-Tolerant Permanent Magnet Motor under Inter-Turn Short-Circuit Faults

This paper proposes a simplified lumped parameter thermal network (LPTN) model for thermal analysis under the inter-turn short-circuit (SC) faults of a five-phase fault-tolerant permanent magnet (PM) motor for electric vehicles. The proposed model can consider circumferential heat transfer, variable copper loss, and uneven loss distribution. Firstly, an analytical calculation formula of inter-turn SC current is proposed to separate the copper loss of SC turns from electromagnetic simulation, and the accuracy of the formula is verified by finite element analysis (FEA). Secondly, a simplified LPTN model is proposed, the calculation formulas of the thermal resistance are given, and the simplified principle is explained. By comparing the results of different simplifications, it is found that the error between the simplified model and the original model is small. Finally, the proposed model is verified by experiments. The results show that the simplified model can achieve not only a high calculation speed but also a high accuracy. Moreover, the proposed model is applicable to all cases of asymmetrical temperature distribution.

Thermal Model Approach to the YASA Machine for In-Wheel Traction Applications

The axial-flux permanent magnet (AFPM) machines with yokeless and segmented armature (YASA) topology are suitable for in-wheel traction systems due to the high power density and efficiency. To guarantee the reliable operation of the YASA machines, an accurate thermal analysis should be undertaken in detail during the electrical machine design phase. The technical contribution of this paper is to establish a detailed thermal analysis model of the YASA machine by the lumped parameter thermal network (LPTN) method. Compared with the computational fluid dynamics (CFD) method and the finite element (FE) method, the LPTN method can obtain an accurate temperature distribution with low time consumption. Firstly, the LPTN model of each component of the YASA machine is constructed with technical details. Secondly, the losses of the YASA machine are obtained by the electromagnetic FE analysis. Then, the temperature distribution of the machine can be calculated by the LPTN model and loss information. Finally, a prototype of the YASA machine is manufactured and its temperature distribution under different operating conditions is tested by TT-K-30 thermocouple temperature sensors. The experimental data matches the LPTN results well.

High-speed PMSM thermal analysis of totally enclosed fan cooled axial ventilation for centrifugal blower application

A temperature rise occurs when an electrical machine is fully loaded. The winding insulations of high-speed permanent magnet synchronous machines (PMSMs) are the most temperature-sensitive components, which can impact the machine’s longevity. Thus, recently, prediction for high-speed PMSM winding temperature has been given more and more attention. Thermal analysis by numerical (computational fluid dynamics (CFD) and finite element method (FEM)) and analytical lumped parameter thermal network (LPTN) methods have been widely used to estimate the temperature of totally enclosed fan cooled axial ventilation system (TEFCAVS) machines. Although numerical methods have more accuracy, their computation wastes time. Therefore, LPTN is being utilized in this paper due to the fastness of its computation. Firstly, estimated losses of high-speed PMSM with TEFCAVS via electromagnetic analysis, including copper, iron, permanent magnet (PM) eddy current, sleeve eddy current, and mechanical, are coupled to the LPTN model, which acts as heat sources for temperature prediction. Moreover, analysis shows that slot windings’ maximum temperature exceeds the winding insulation class with initial cooling configuration. Secondly, in order to mitigate slot winding temperature, the sensitivity study for liner conductivity, air gap’s heat transfer and lamination to housing’s contact is conducted by LPTN to identify which thermal parameter has more influence on mitigating the maximum temperature of the slot winding. Investigation shows that improving an air gap’s heat transfer has more influence than other parameters for mitigating slot winding maximum temperature below its insulation class. Lastly, the machine is designed and tested with the best thermal-sensitive parameters. Then test results for the maximum temperature of the winding are compared with estimated results to ensure the proposed LPTN correctness, and the calibration process confirms LPTN accuracy.

Thermal Analysis of Axial-Flux Permanent Magnet Motors for Vehicles Based on Fast Two-Way Magneto-Thermal Coupling

Axial-flux permanent magnet motor (AFPMM) have small size and high power density. It has a good application prospect in the field of new energy vehicle driving. In this paper, based on a 56 kW AFPMM, the magnetic circuit characteristics are calculated by the split loop method, considering the influence of pulse width modulated (PWM) power supply. The loss is taken as the heat source, combined with the motor structure characteristics and cooling conditions, the lumped-parameter thermal network model of the motor is established to solve the steady-state and transient temperature distribution of each structure. By this way, fast and accurate thermal calculation of the motor is realized in design stage. The accuracy of the lumped-parameter thermal network model is verified by experiment. At the same time, the effects of spliting the permanent magnet (PM) into pieces, flow rate of cooling water, and loss distribution on temperature rise are analyzed. This research work provides an effective fast thermal calculation method for AFPMM and provides a reference basis for the design of similar motors. It has important value of theoretical significance and engineering practical.

Coupled Electromagnetic and Thermal Analysis of Permanent Magnet Rectifier Generator Based on LPTN

Multiphysics field coupling analysis requires that the electromagnetic and thermal performance be analyzed simultaneously. In this article, electromagnetic field and thermal field based on lumped parameter thermal network (LPTN) are coupled. This method can give a more accurate evaluation of electromagnetic and thermal performance at the design stage. Take a 7500 r/min, 4.5 kW permanent magnet (PM) rectifier generator as an example: after designing the electromagnetic model by finite element method (FEM) and the LPTN model, respectively, the electromagnetic field and thermal field are coupled by temperature-dependent material properties. Experiment shows that the error of simulated temperature and electromagnetic performance is within 5%. Moreover, this method reduces calculation time of thermal analysis dramatically, which can be applied to analyze and optimize a PM rectifier generator, as well as other typed motors.

The Study of Cooling Enhancement in Axial Flux Permanent Magnet Motors for Electric Vehicles

In this article, the thermal properties of axial flux permanent magnet motors are investigated. A modified cooling structure is designed to enhance the cooling performance for end windings compared with the conventional cooling structure. The heat flow path in the end thermal conduction region is studied based on the lumped-parameter thermal network model. The influence of the thermal conductivity of potting material and the thickness of stator yoke on cooling performance is discussed. The computational fluid dynamics thermal analysis is also employed to give verification and to get the temperature of hot spot. In the end, a prototype motor with the modified cooling structure is tested.

Temperature Estimation Using Lumped-Parameter Thermal Network With Piecewise Stator-Housing Modules for Fault-Tolerant Brake Systems in Highly Automated Driving Vehicles

With the increased interest in intelligent transportation, the need for fault-tolerant systems has also increased. In this paper, we propose a piecewise stator-housing module (PSM) and construct a lumped-parameter thermal network (LPTN) that can be used in a fault-tolerant system based on the PSM. The proposed LPTN model considers not only radial and axial heat transfer but also tangential heat transfer; therefore, even if only one of the two circuits is running on a fault-tolerant motor (dual winding motor), the coil temperature can be estimated. To verify the proposed model, three winding-type motors are tested with varying current values during normal and fault operations, and the test and analysis results are in good agreement. Additionally, the usefulness of the proposed model is demonstrated by comparing the temperatures of both the conventional and proposed LPTNs in the event of a brake system fault during braking operation in a virtual traffic jam simulation. This simulation demonstrates that temperature estimation of the motor is important for motor design because the brake operation time is dependent on the motor temperature. Furthermore, the system performance or size can be determined by accurately predicting the temperature, even in the event of a fault in the brake system, where the fault-tolerant motor is used, thus keeping the driver safe. The proposed LPTN can be used in brake systems and in other systems that utilize fault-tolerant dual winding motors.

Design and Experimental Verification of Three‐Phase Medium‐Frequency Transformers for High‐Power DC‐DC Applications

The magnetically coupled three‐phase dual‐active‐bridge (DAB3) DC‐DC converter is highly suitable for high‐power applications. Although the converter has many advantages, the structure, working mode and electromagnetic effect of its key magnetic component—high‐power medium‐frequency three‐phase transformer (MFT3) is more complex, and the capacity, frequency, loss, temperature rise and leakage inductance restrict each other, forming a complex and systematic design problem. This paper focuses on the overall design method and performance of high‐power MFT3. Based on the analysis of steady‐state voltage and current waveforms of DAB3 converter, the expression of harmonic current is derived by using fundamental wave analysis, and the analytical calculation method of copper loss is proposed. According to the piecewise linear flux density waveform excited by six‐step voltage wave, the modified IGSE method is proposed to calculate the core loss. For MFT3 with three‐phase five column core topology, a lumped parameter thermal network model with 14 temperature nodes is established to obtain the temperature rise. The influence of winding arrangement on leakage inductance is analyzed, and the analytical expression of leakage inductance is presented. On this basis, the design method of high‐power MFT3 is proposed based on the free parameter scanning method. A 5 kHz, 15 kW MFT3 model with the nanocrystalline core material is made, and the leakage inductance, core loss, copper loss and temperature rise are extracted by finite element method (FEM) and experiment. The effectiveness of the proposed method is verified by comparing the design results with the FEM simulation and experimental results. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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.

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.

Thermal Analysis Strategy for Axial Permanent Magnet Coupling Combining FEM with Lumped-Parameter Thermal Network

Thermal analysis is exceptionally important for operation safety of axial permanent magnet couplings (APMCs). Combining a finite element method (FEM) with a lumped-parameter thermal network (LPTN) is an effective yet simple thermal analysis strategy for an APMC that is developed in this paper. Also, some assumptions and key considerations are firstly given before analysis. The loss, as well as the magnetic field distribution of the conductor sheet (CS) can be accurately calculated through FEM. Then, the loss treated as source node loss is introduced into the LPTN model to obtain the temperature results of APMCs, where adjusting conductivity of the CS is a necessary and significant link to complete an iterative calculation process. Compared with experiment results, this thermal analysis strategy has good consistency. In addition, a limiting and safe slip speed can be determined based on the demagnetization temperature permanent magnet (PM).

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.

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.