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Journals / Journal of Artificial Intelligence and Data Science / 2024 / Cilt: 4 Sayı: 2

Optimization of Thermal Management for Cooling System of Power Electronics Modules Consisting Insulated-Gate Bipolar Transistor Using Neuro-Regression Analysis and Non-Traditional Algorithms

Melih Savran(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0001-8343-1073
Ece Nur Yüncü(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0009-0002-3195-124X
Levent Aydın(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0003-0483-0071
Pages
68–78
Publication Date
2024-12-27
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate a 12 % reduction in the minimum IGBT temperature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Thermal management and extreme temperatures critically influence the performance of power electronics systems, especially those utilizing IGBT (Insulated-Gate Bipolar Transistor) and diode components.
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Key Findings & Data:To achieve temperature minimization, optimum values of design variables: coolant flow rate (L/min), distance from the vortex generator (mm), height (μmm), and width of the first pin-fin (μmm), and distance of the vortex generator from the surface (μmm) were determined.
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Conclusion & Impact:The results indicate a 12 % reduction in the minimum IGBT temperature.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:neuro-regression analysis · thermal management · Insulated-Gate Bipolar Transistor

Abstract

Thermal management and extreme temperatures critically influence the performance of power electronics systems, especially those utilizing IGBT (Insulated-Gate Bipolar Transistor) and diode components. Various parameters govern the cooling efficiency of these systems. In this study, the IGBT temperature was selected as the objective function. To achieve temperature minimization, optimum values of design variables: coolant flow rate (L/min), distance from the vortex generator (mm), height (μmm), and width of the first pin-fin (μmm), and distance of the vortex generator from the surface (μmm) were determined. The mathematical modeling process employed Neuro-Regression analysis. The prediction performance of proposed 14 different regression models were evaluated using R2Training, R2Testing, R2Validation indexes and boundedness check criteria. The Differential Evolution, Nelder Mead, Simulated Annealing, and Random Search algorithms were applied to minimize IGBT temperature. The FOLN (First Order Logarithmic Nonlineer) model emerged as the most successful, achieving a minimum temperature lower than the experimental dataset given in literature. The results indicate a 12 % reduction in the minimum IGBT temperature.

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Cite This Article

Savran, M., Yüncü, E. N., Aydın, L. (2024). Optimization of Thermal Management for Cooling System of Power Electronics Modules Consisting Insulated-Gate Bipolar Transistor Using Neuro-Regression Analysis and Non-Traditional Algorithms. Journal of Artificial Intelligence and Data Science, Cilt: 4 Sayı: 2, 68–78. https://ajindex.com/articles/1618305

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References (21)

  1. F. Wang, J. Cao, Z. Ling, Z. Zhang, and X. Fang, "Experimental and simulative investigations on a phase change material nano-emulsion-based liquid cooling thermal management system for a lithium-ion battery pack," Energy, vol. 207, p. 118215, 2020.
  2. R. Sabbah, R. Kizilel, J. R. Selman, and S. Al-Hallaj, "Active (air-cooled) vs. passive (phase change material) thermal management of high power lithium-ion packs: Limitation of temperature rise and uniformity of temperature distribution," Journal of Power Sources, vol. 182, no. 2, pp. 630-638, 2008.
  3. Y. Song and B. Wang, "Survey on reliability of power electronic systems," IEEE Transactions on Power Electronics, vol. 28, no. 1, pp. 591-604, 2013.
  4. H. Zou, W. Wang, G. Zhang, F. Qin, C. Tian, and Y. Yan, "Experimental investigation on an integrated thermal management system with heat pipe heat exchanger for electric vehicle," Energy Conversion and Management, vol. 118, pp. 88-95, 2016.
  5. H. Lambate, S. Nakanekar, and S. Tonapi, "Thermal characterization of the IGBT modules used in hybrid electric vehicles," in Fourteenth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2014, pp. 1086-1091.
  6. J. He, V. Mehrotra, and M. C. Shaw, "Thermal design and measurements of IGBT power modules: Transient and steady state," in Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Fourth IAS Annual Meeting, vol. 2, 1999, pp. 1440-1444.
  7. Z. Huang, T. An, F. Qin, Y. Gong, Y. Dai, and P. Chen, "Effect of the thermal contact resistance on the heat dissipation performance of the press-pack IGBT module," in 2022 23rd International Conference on Electronic Packaging Technology (ICEPT), 2022, pp. 1-4.
  8. A. S. Bahman, K. Ma, and F. Blaabjerg, "A lumped thermal model including thermal coupling and thermal boundary conditions for high-power IGBT modules," IEEE Transactions on Power Electronics, vol. 33, no. 3, pp. 2518-2530, 2018.
  9. F. Dong, Y. Feng, Z. Wang, and J. Ni, "Effects on thermal performance enhancement of pin-fin structures for insulated gate bipolar transistor (IGBT) cooling in high voltage heater system," International Journal of Thermal Sciences, vol. 146, p. 106106, 2019.
  10. R. V. Rao and V. K. Patel, "Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm," International Journal of Thermal Sciences, vol. 49, no. 9, pp. 1712-1721, 2010.
  11. S. M. Lee and K. Y. Kim, "Multi-objective optimization of arc-shaped ribs in the channels of a printed circuit heat exchanger," International Journal of Thermal Sciences, vol. 94, pp. 1-8, 2015.
  12. M. Mishra, P. K. Das, and S. Sarangi, "Optimum design of crossflow plate-fin heat exchangers through genetic algorithm," International Journal of Heat Exchangers, vol. 5, no. 2, pp. 379-402, 2004.
  13. M. Mishra and P. K. Das, "Thermoeconomic design-optimisation of crossflow plate-fin heat exchanger using genetic algorithm," International Journal of Exergy, vol. 6, no. 6, pp. 237-252, 2009.
  14. W. T. Han, L. H. Tang, and G. N. Xie, "Performance comparison of particle swarm optimization and genetic algorithm in rolling fin-tube heat exchanger optimization design," in Proceedings of the ASME Summer Heat Transfer Conference, 2008, pp. 5-10.
  15. F. Pourfattah and M. Sabzpooshani, "On the thermal management of a power electronics system: Optimization of the cooling system using genetic algorithm and response surface method," Energy, vol. 232, p. 120951, 2021.
  16. İ. Polatoğlu, L. Aydın, B. Ç. Nevruz, and S. Özer, "A novel approach for the optimal design of a biosensor," Analytical Letters, vol. 53, no. 9, pp. 1428-1445, 2020.
  17. R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, 2nd ed., John Wiley & Sons, 2004.
  18. M. Bilal, M. Pant, H. Zaheer, L. Garcia-Hernandez, and A. Abraham, "Differential Evolution: A review of more than two decades of research," Engineering Applications of Artificial Intelligence, vol. 90, p. 103479, 2020.
  19. S. S. Fan, Y. C. Liang, and E. Zahara, "A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search," Computers & Industrial Engineering, vol. 50, no. 4, pp. 401-425, 2006.
  20. M. Savran and L. Aydın, "Natural frequency and buckling optimization considering weight saving for hybrid graphite/epoxy-sitka spruce and graphite-flax/epoxy laminated composite plates using stochastic methods," Mech Adv Mater Struct, vol. 30, no. 13, pp. 2637-2650, 2023. https://doi.org/10.1080/15376494.2021.1875390.
  21. M. Savran, L. Aydın, A. Ayaz, and T. Uslu, "A new strategy for manufacturing, modeling, and optimization of 3D printed polylactide based on multiple nonlinear neuro regression analysis and stochastic optimization methods," Proc Inst Mech Eng Part E J Process Mech Eng, 2024. https://doi.org/10.1177/09544089241272909.