Real-Time Compact Thermal Models for Health Management of Power Electronics

Musallam, M.; Johnson, C.M.

doi:10.1109/tpel.2010.2040634

Cited by 172 publications

(88 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 very well, and the R and C parameters extracted using the three-step curve fitting method are listed in Table III. Here the unit for the R parameters is C/W, and the unit for the C parameters is J/C.The extracted R and C parameters can indeed reflect the physical structure of the SiC power module which cannot be revealed using an m×m Foster network similar to the previous RC network models [15][16][17][18]21]. For example, the values of C17, C18, C67 and C68 are higher than the values of CI7 and CI8 (I=2, 3, 4 and 5), and the values of R17, R18, R67 and R68 are higher than the values of RI7 and RI8 (I=2, 3, 4 and 5).…”

Section: B R and C Parameters Of The Rc Network Modelmentioning

confidence: 94%

“…Then the compact RC network model consisting of 115 R and C parameters to predict the transient junction temperatures of the 6 MOSFETS has further been developed, where cross-heating effects between the MOSFETs are represented with lateral thermal resistors. Such a compact RC network model represents the real structure and heat-flow paths within the module much better than those RC network models reported in the existing literature [15][16][17][18][19][20][21], but there is no existing method to effectively determine the R and C parameters.…”

Section: Introductionmentioning

confidence: 97%

“…Compact thermal models expressed as thermal resistorcapacitor (RC) networks were more widely employed to rapidly predict junction temperatures or the temperatures at a few critical locations in the power modules for electro-thermal design, thermal management, reliability and lifetime prediction [15][16][17][18][19][20][21][22][23][24]. In the simplified cases, individual power device might be considered and one dimensional heat conduction was assumed to predict the junction temperatures [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Generally speaking, the Foster networks were used as behavior models to calculate the transient junction temperatures, while the R and C parameters in the Cauer networks were taken to have true physical meanings. In most cases, multiple power devices with both self-heating and crossheating effects were considered [15][16][17][18][19][20][21]. In these models, no matter whether it is self-heating or cross-heating, the heating contribution from each power device was described using a Foster network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Physical RC Network Model for Electrothermal Analysis of a Multichip SiC Power Module

Castellazzi

Eleffendi

et al. 2018

IEEE Trans. Power Electron.

View full text Add to dashboard Cite

AbstractThis paper is concerned with the thermal models which can physically reflect the heat-flow paths in a lightweight three-phase half bridge, two-level SiC power module with 6 MOSFETs and can be used for coupled electro-thermal simulation. The finite element (FE) model was first evaluated and calibrated to provide the raw data for establishing the physical RC network model. It was experimentally verified that the cooling condition of the module mounted on a water cooler can be satisfactorily described by assuming the water cooler as a heat exchange boundary in the FE model. The compact RC network consisting of 115 R and C parameters to predict the transient junction temperatures of the 6 MOSFETS was constructed, where cross-heating effects between the MOSFETs are represented with lateral thermal resistors. A three-step curve fitting method was especially developed to overcome the challenge for extracting the R and C values of the RC network from the selected FE simulation results. The established compact RC network model can physically be correlated with the structure and heat-flow paths in the power module, and was evaluated using the FE simulation results from the power module under realistic switching conditions. It was also integrated into the LTspice model to perform the coupled electrothermal simulation to predict the power losses and junction temperatures of the 6 MOSFETs under switching frequencies from 5 kHz to 100 kHz which demonstrate the good electrothermal performance of the designed power module.Index TermsMOSFETs, SiC power module, finite element methods, RC network, curve fitting, three-phase inverters.

show abstract

Section: B R and C Parameters Of The Rc Network Modelmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Physical RC Network Model for Electrothermal Analysis of a Multichip SiC Power Module

Castellazzi

Eleffendi

et al. 2018

IEEE Trans. Power Electron.

View full text Add to dashboard Cite

show abstract

“…IntroductIon P OWER electronic system has high energy conversion efficiency and controllability [1],it has been widely used in aerospace, industrial automation, transportation, renewable energy power generation and other fields which require the highly reliability [2]- [5]. In these fields, the power will fluctuate in a large range [6]- [8], the reliability of power electronic systems with the harsh operating conditions are far less than traditional power equipment. Power semiconductor devices are the core component of power electronic system, and it is also one of the most fragile components in power electronic system [9], so that power device has a significant effect on the reliability of the power electronics system.Thereby, improving the reliability of power devices is one of the focus of power electronics studies.…”

mentioning

confidence: 99%

Review of Power Semiconductor Device Reliability for Power Converters

et al. 2017

View full text Add to dashboard Cite

Adaptive control and signal processing literature survey (No. 20)

2010

Adaptive Control & Signal

View full text Add to dashboard Cite

In order to keep the readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. ADAPTIVE SYSTEMSAbramovich Y, Frazer G, Johnson B. Iterative adaptive Kronecker MIMO radar beamformer: description and convergence analysis. IEEE Transactions on Signal Processing 2010; 58(7):3681-3691. Badwe A, Singh A, Patwardhan S, Gudi R. A constrained recursive pseudo-linear regression scheme for on-line parameter estimation in adaptive control. Journal of Process Control 2010; 20(5):559-572. Cassimatis N, Bignoli P, Bugajska M, Dugas S, Kurup U, Murugesan A, Bello P. An architecture for adaptive algorithmic hybrids. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(3):903-914. Cheah C, Hou S, Zhao Y, Slotine J-J. Adaptive vision and force tracking control for robots with constraint uncertainty. IEEE/ASME Transactions on Mechatronics 2010; 15(3): 389-399. Cheah C, Liu C, Slotine J. Adaptive Jacobian vision based control for robots with uncertain depth information. Automatica 2010; 46(7):1228-1233. De Maio A, Conte E. Adaptive detection in Gaussian interference with unknown covariance after reduction by invariance. IEEE Transactions on Signal Processing 2010; 58(6):2925-2934. Fei J. Robust adaptive vibration tracking control for a micro-electro-mechanical systems vibratory gyroscope with bound estimation. control allocation for non-linear systems with internal dynamics. IET Control Theory and Applications 2010; 4(6):909-922. Liu L, Xie X-J. Decentralized adaptive stabilization for interconnected systems with dynamic input-output and nonlinear interactions. Automatica 2010; 46(6):1060-1067. Malloci I, Daafouz J, Iung C. Stability and stabilization of two time scale switched systems in discrete time. IEEE Transactions on Automatic Control 2010; 55(6):1434-1438. Mileounis G, Babadi B, Kalouptsidis N, Tarokh V. An adaptive greedy algorithm with application to nonlinear communications. IEEE Transactions on Signal Processing 2010; 58(6):2998-3007. Nguyen H-X, Nguyen H-H. Adaptive relaying in noncoherent cooperative networks. IEEE Transactions on Signal Processing 2010; 58(7):3938-3945. Pigg S, Bodson M. Adaptive algorithms for the rejection of sinusoidal disturbances acting on unknown plants. IEEE Transactions on Control Systems Technology 2010; 18(4): 822-836. Tucci M, Raugi M. Adaptive FIR neural model for centroid learning in self-organizing maps. IEEE Transactions on Neural Networks 2010; 21(6):948-960. Wang L, de Lamare R, Yukawa M. Adaptive reducedrank constrained constant modulus algorithms based on

show abstract

Real-Time Compact Thermal Models for Health Management of Power Electronics

Cited by 172 publications

References 7 publications

A Physical RC Network Model for Electrothermal Analysis of a Multichip SiC Power Module

A Physical RC Network Model for Electrothermal Analysis of a Multichip SiC Power Module

Review of Power Semiconductor Device Reliability for Power Converters

Adaptive control and signal processing literature survey (No. 20)

Contact Info

Product

Resources

About