Electro-thermal models commonly rely on experimentally identified parameters to predict temperatures in power electronic systems. However, even with a physically reasonable model structure, arbitrarily selected experimental cases may leave some parameters unidentifiable and degrade prediction capability. This paper proposes an experimental design framework with an information-sufficiency criterion linking prescribed parameter variations to measurable temperature responses. Singular value decomposition (SVD) of the parameter-sensitivity matrix identifies weak parameter directions, whose corresponding temperature responses are compared with a practical detectability threshold. The framework is demonstrated using a nonlinear electro-thermal model with thermal coupling and validated on four independently power-controlled MOSFETs representing multiple devices. Unguided and frameworkguided selections yield fitting errors of 2.161 °C and 1.413 °C, but prediction errors of 28.236 °C and 0.462 °C at the same validation case. A broader analysis over experimental case selections also shows an increase in prediction error as temperature response falls below the proposed criterion. These results show that the proposed framework can guide experimental case selection to improve electro-thermal model prediction reliability.
- Open Access
- Article
Efficient Experimental Design Framework for Reliable Parameter Identification of Electro-Thermal Models
Author Information
Received: 24 Sep 2026 | Revised: 06 Oct 2026 | Accepted: 08 Oct 2026 | Published: 13 Oct 2026
Abstract
Keywords
electro-thermal modelling | thermal coupling | nonlinear thermal model | singular value decomposition | experimental design | power electronic devices
References
- 1.
Ramamurthi, S.; Ramasamy, P. Reliability evaluation of non-isolated high gain interleaved DC–DC converter. Bull. Electr. Eng. Inform. 2024, 13, 794–799. https://doi.org/10.11591/eei.v13i2.6585.
- 2.
U.S. Department of Defense. Reliability Prediction of Electronic Equipment; U.S. Department of Defense: Washington, DC, USA, 1991.
- 3.
Zhao, Y.; Wang, Z.; Zhang, H.; et al. Temperature Evaluation of Power Devices in DC–DC Converters Based on Multitimescale Electrothermal Coupling Model. IEEE Trans. Ind. Electron. 2025, 72, 10016–10027. https://doi.org/10.1109/tie.2025.3552191.
- 4.
Chen, H.; Yang, J.; Xu, S. Electrothermal-Based Junction Temperature Estimation Model for Converter of Switched Reluctance Motor Drive System. IEEE Trans. Ind. Electron. 2020, 67, 874–883. https://doi.org/10.1109/tie.2019.2898600.
- 5.
Jørgensen, A.B.; Munk-Nielsen, S.; Uhrenfeldt, C. Overview of Digital Design and Finite-Element Analysis in Modern Power Electronic Packaging. IEEE Trans. Power Electron. 2020, 35, 10892–10905. https://doi.org/10.1109/tpel.2020.2978584.
- 6.
Peyghami, S.; Davari, P.; Blaabjerg, F. System-Level Reliability-Oriented Power Sharing Strategy for DC Power Systems. IEEE Trans. Ind. Appl. 2019, 55, 4865–4875. https://doi.org/10.1109/tia.2019.2918049.
- 7.
Li, X.; Wang, Y.; Chang, G.; et al. Thermal Imbalance Among Paralleling Chips in Power Modules and the Impact from Traction Inverter System View. IEEE Trans. Ind. Electron. 2023, 70, 2231–2240. https://doi.org/10.1109/tie.2022.3159920.
- 8.
Bernardoni, M.; Delmonte, N.; Chiozzi, D.; et al. Non-linear thermal simulation at system level: Compact modelling and experimental validation. Microelectron. Reliab. 2018, 80, 223–229. https://doi.org/10.1016/j.microrel.2017.12.005.
- 9.
De Mey, G.; Torzewicz, T.; Kawka, P.; et al. Analysis of nonlinear heat exchange phenomena in natural convection cooled electronic systems. Microelectron. Reliab. 2016, 67, 15–20. https://doi.org/10.1016/j.microrel.2016.11.003.
- 10.
García-Sánchez, S.; Íñiguez-de-la-Torre, I.; Pérez, S.; et al. Non-linear thermal resistance model for the simulation of high power GaN-based devices. Semicond. Sci. Technol. 2021, 36, 055002. https://doi.org/10.1088/1361-6641/abeb83.
- 11.
Marek, J.; Chvála, A.; Donoval, D.; et al. Compact model of power MOSFET with temperature dependent Cauer RC network for more accurate thermal simulations. Solid-State Electron. 2014, 94, 44–50. https://doi.org/10.1016/j.sse.2014.02.003.
- 12.
Codecasa, L.; d’Alessandro, V.; Magnani, A.; et al. Circuit-Based Electrothermal Simulation of Power Devices by an Ultrafast Nonlinear MOR Approach. IEEE Trans. Power Electron. 2016, 31, 5906–5916. https://doi.org/10.1109/tpel.2015.2494500.
- 13.
Wang, B.; Dong, S.; Liu, F.; et al. A Simple Equivalent Temperature Rise Test Method Suitable for Safely Evaluating Thermal Limits of Motors and Power Devices without Model Dependence. IEEE Trans. Ind. Electron. 2020, 67, 8755–8766. https://doi.org/10.1109/tie.2019.2952785.
- 14.
Ji, L.; Hu, X. Analog circuit soft-fault diagnosis based on sensitivity analysis with minimum fault number rule. Analog Integr. Circuits Signal Process. 2018, 95, 163–171. https://doi.org/10.1007/s10470-018-1111-y.
- 15.
Tadeusiewicz, M.; Hałgas, S. A Systematic Method for Arranging Diagnostic Tests in Linear Analog DC and AC Circuits. J. Electron. Test. 2017, 33, 147–156. https://doi.org/10.1007/s10836-017-5650-4.
- 16.
Pozzi, A.; Ciaramella, G.; Volkwein, S.; et al. Optimal Design of Experiments for a Lithium-Ion Cell: Parameters Identification of an Isothermal Single Particle Model with Electrolyte Dynamics. Ind. Eng. Chem. Res. 2019, 58, 1286–1299. https://doi.org/10.1021/acs.iecr.8b04580.
- 17.
Sovljanski, V.; Paolone, M.; Tant, S.; et al. Optimizing Experiments for Accurate Battery Circuit Parameters Estimation: Reduction and Adjustment of Frequency Set Used in Electrochemical Impedance Spectroscopy. In Proceedings of the 2024 IEEE Energy Conversion Congress and Exposition (ECCE), Phoenix, AZ, USA, 20–24 October 2024; pp. 1935–1942. https://doi.org/10.1109/ecce55643.2024.10861495.
- 18.
Baumann, M.S.; Kemmetmüller, W.; Kugi, A.; et al. Optimal Design of Experiments for Identification of PMSM Thermal Models. IEEE Open J. Ind. Appl. 2026, 7, 995–1009. https://doi.org/10.1109/ojia.2026.3719835.
- 19.
Yoko, M.; Juniper, M.P. Optimal experiment design with adjoint-accelerated Bayesian inference. Data-Centric Eng. 2024, 5, e17. https://doi.org/10.1017/dce.2024.16.
- 20.
Sun, H.; Pomeroy, J.W.; Simon, R.B.; et al. Temperature-Dependent Thermal Resistance of GaN-on-Diamond HEMT Wafers. IEEE Electron Device Lett. 2016, 37, 621–624. https://doi.org/10.1109/led.2016.2537835.
- 21.
Górecki, P. Compact Thermal Modeling of Power Semiconductor Devices with the Influence of Atmospheric Pressure. Energies 2022, 15, 3565. https://doi.org/10.3390/en15103565.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com