SavedAdd your journal

Journals / Politeknik Dergisi / 2026 / Cilt: 29 Sayı: 5

Machine Learning-Based Modeling and Performance Analysis of a Novel Battery Thermal Management System with a Ternary Periodic Minimal Surface Porous Structured Liquid Cooling Channel

Üçlü Periyodik Minimal Yüzey Gözenekli Yapılı Sıvı Soğutma Kanalına Sahip Yenilikçi Bir Batarya Termal Yönetim Sisteminin Makine Öğrenmesi Tabanlı Modellenmesi ve Performans Analizi

Andaç Batur Çolak(Niğde Ömer Halisdemir University)0000-0001-9297-8134
Cuma Kılınç(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0002-6258-617X
Pages
1–12
Publication Date
2026-09-16
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The novelty of the study lies in its dual experimental machine learning approach, which provides a foundation for improved predictive control in battery temperature management systems with high accuracy and minimal resource requirements.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article examines the fundamental requirements for effective thermal management of batteries, particularly in high-energy-density applications such as electric vehicles and energy storage systems.
📊
Key Findings & Data:Experimental validation and optimization via artificial neural networks are used to evaluate and improve thermal performance, focusing on critical metrics such as maximum temperature and temperature difference within the battery system.
💡
Conclusion & Impact:The novelty of the study lies in its dual experimental machine learning approach, which provides a foundation for improved predictive control in battery temperature management systems with high accuracy and minimal resource requirements.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Batarya termal yönetimi · sıvı soğutma · minimal yüzey alanı

Abstract

This article examines the fundamental requirements for effective thermal management of batteries, particularly in high-energy-density applications such as electric vehicles and energy storage systems. A unique method is introduced that utilizes a triple-periodical minimal surface pore structure in liquid cooling channels to improve heat dissipation and temperature homogeneity in battery packs. Experimental validation and optimization via artificial neural networks are used to evaluate and improve thermal performance, focusing on critical metrics such as maximum temperature and temperature difference within the battery system. Artificial neural network models demonstrated model reliability, exhibiting ideal prediction accuracy with low mean square error values of 1.29E-05 and 9.87E-06, and a correlation coefficient of 0.99999. This work builds upon previous attempts by combining an innovative structural design with machine learning, aiming to provide a robust and data-driven solution that aligns cooling efficiency with structural integrity. The novelty of the study lies in its dual experimental machine learning approach, which provides a foundation for improved predictive control in battery temperature management systems with high accuracy and minimal resource requirements.

Özet

Bu makale, özellikle elektrikli araçlar ve enerji depolama sistemleri gibi yüksek enerji yoğunluklu uygulamalarda, bataryaların etkili termal yönetiminin temel gereksinimlerini incelemektedir. Batarya paketlerinde ısı dağılımını ve sıcaklık homojenliğini iyileştirmek için sıvı soğutma kanallarında üçlü periyodik minimal yüzey gözenekli yapı kullanan benzersiz bir yöntem tanıtılmıştır. Deneysel doğrulama ve yapay sinir ağları aracılığıyla optimizasyon, batarya sistemi içindeki maksimum sıcaklık ve sıcaklık farkı gibi kritik ölçütlere odaklanarak termal performansı değerlendirmek ve geliştirmek için kullanılmıştır. Yapay sinir ağı modelleri, 1.29E-05 ve 9.87E-06 gibi düşük ortalama kare hata değerleri ve 0.99999'luk korelasyon katsayısı değeri ile ideal bir tahmin doğruluğu sergileyerek modelin güvenilirliğini göstermiştir. Bu çalışma, yenilikçi bir yapısal tasarımı makine öğrenimiyle birleştirerek önceki girişimleri geliştirmekte ve soğutma verimliliğini yapısal bütünlükle uyumlu hale getiren, dayanıklı ve veriye dayalı bir çözüm sunmayı hedeflemektedir. Çalışmanın yeniliği, yüksek doğruluk ve minimum kaynak talebiyle pil sıcaklık yönetim sistemlerinde iyileştirilmiş tahmine dayalı kontrol için bir temel oluşturan çift deneysel makine öğrenimi yaklaşımında yatmaktadır.

🔖

Cite This Article

Çolak, A. B., Kılınç, C. (2026). Machine Learning-Based Modeling and Performance Analysis of a Novel Battery Thermal Management System with a Ternary Periodic Minimal Surface Porous Structured Liquid Cooling Channel. Politeknik Dergisi, Cilt: 29 Sayı: 5, 1–12. https://doi.org/10.2339/politeknik.1947626

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (28)

  1. Yuan, J., Gu, Z., Bao, J., Yang, T., Li, H., Wang, Y., Pei, L., Jiang, H., Chen, L. and Yuan, C., “Structure optimization design and performance analysis of liquid cooling plate for power battery”, Journal of Energy Storage, 87: 111517, (2024).
  2. Luo, Y., Qiu, X., Ren, J. and Wang, S., “A channel with hybrid twisted tapes for immersion cooling battery thermal management system”, Journal of Energy Storage, 95: 112588, (2024).
  3. Luo, D., Wu, Z., Yan, Y., Sun, Z., Yang, L. and Cao, B., “Performance analysis of a battery thermal management system combining thermoelectric, composite phase change material, and liquid cooling under extreme operating conditions”, Journal of Energy Storage, 95: 112679, (2024).
  4. Xiong, S., Wang, Z., Bao, R., Yang, H., Zhang, B. and Du, X., “Performance study and optimization of hybrid battery thermal management system based on triply periodic minimal surface coupled phase change material”, Journal of Energy Storage, 100: 113529, (2024).
  5. Song, C., Kong, B., Zeng, Z., Yuan, N. and Shi, H., “Innovative coupled cooling strategy for enhanced battery thermal management: Synergistic optimization of jet impingement and immersion cooling”, International Journal of Heat and Mass Transfer, 232: 125963, (2024).
  6. Hussain, M., Khan, M.K. and Pathak, M., “Thermal management of high-energy lithium titanate oxide batteries using an effective channeled dielectric fluid immersion cooling system”, Energy Conversion and Management, 313: 118644, (2024).
  7. Abdulqader, A.A. and Jaffal, H.M., “Comparative investigation on heat transfer augmentation in a liquid cooling plate for rectangular Li-ion battery thermal management”, Results in Engineering 24: 102913, (2024).
  8. Sutheesh, P.M., Atul, A.P. and Rohinikumar, B., “Comparative study of fiber materials for enhanced performance of hybrid cooling in battery thermal management systems”, Applied Energy, 371: 123671, (2024).
  9. Choi, H., Jun, Y., Chun, H. and Lee, H., “Comprehensive feasibility study on metal foam use in single-phase immersion cooling for battery thermal management system”, Applied Energy 375: 124083, (2024).
  10. Kumar, K., Sarkar, J. and Mondal, S.S., “Evaluating nanofluid-cooled hybrid Lithium-ion battery thermal management system under abnormal operating scenarios”, Journal of Power Sources, 623: 235495, (2024).
  11. You, H., Lee, S., Baek, C., Han, C. and Kim, Y., “Enhancement of heat transfer in cooling channels for electronic devices using multi-directional graded triply periodic minimal surfaces (TPMS)”, International Communications in Heat and Mass Transfer, 159: 108278, (2024).
  12. Jiang, G., Wen H. and Peng, C., “A novel battery thermal management based on composite phase change material with liquid-assisted cooling for different ambient temperatures, Applied Thermal Engineering 252: 123686, (2024).
  13. Chen, P., Gao, L., Li, W., Zhao, J., Garg, A. and Panda, B., “A topology optimization-based-novel design and comprehensive thermal analysis of a cylindrical battery liquid cooling plate”, Applied Thermal Engineering, 252: 123747, (2024).
  14. Ye, L., Li, C., Wang, C., Zheng, J., Zhong, K. and Wu, T., “A multi-objective optimization approach for battery thermal management system based on the combination of BP neural network prediction and NSGA-II algorithm”, Journal of Energy Storage, 99: 113212, (2024).
  15. Mesgarpour, M., Mir, M., Alizadeh, R., Abad, J.M.N. and Borj, E.P., “An evaluation of the thermal behaviour of a lithium-ion battery pack with a combination of pattern-based artificial neural networks (PBANN) and numerical simulation”, Journal of Energy Storage, 47: 103920, (2022).
  16. Mehmandoosti, M.M. and Kowsary, F., “Artificial neural network-based multi-objective optimization of cooling of lithium-ion batteries used in electric vehicles utilizing pulsating coolant flow”, Applied Thermal Engineering, 219: 119385, (2023).
  17. Yuan, L., Li, W., Deng, W., Sun, W., Huang, M. and Liu, Z., “Cell temperature prediction in the refrigerant direct cooling thermal management system using artificial neural network”, Applied Thermal Engineering, 254: 123852, (2024).
  18. Zheng, A., Gao, H., Jia, X., Cai, Y., Yang, X., Zhu, Q. and Jiang, H., “Deep learning-assisted design for battery liquid cooling plate with bionic leaf structure considering non-uniform heat generation”, Applied Energy, 373: 123898, (2024).
  19. Ahmad, S., Liu, Y., Khan, S.A., Shah, S.W.A. and Huang, X., “Modeling liquid immersion-cooling battery thermal management system and optimization via machine learning”, International Communications in Heat and Mass Transfer, 158: 107835, (2024).
  20. Tang, Z., Xiang, Y., Li, M., Cheng, J. and Q. Wang, “Multi-objective optimization of liquid-cooled battery thermal management system with biomimetic fractal channels using artificial neural networks and response surface methodology”, International Journal of Thermal Sciences, 206: 109304, (2024).
  21. Oyewola, O.M. and Idowu, E.T., “Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system”, International Journal of Thermofluids, 24: 100897, (2024).
  22. He, L., Tan, L., Liu, Z., Zhang, Y., Wu, L., Feng, Y. and Tong, B., “Optimization of thermal management performance of direct-cooled power battery based on backpropagation neural network and deep reinforcement learning”, Applied Thermal Engineering, 258: 124661, (2025).
  23. Xu, Y., Zhao, J., Chen, J., Zhang, H., Feng, Z. and Yuan, J., “Performance analyses on the air cooling battery thermal management based on artificial neural networks”, Applied Thermal Engineering, 252: 123567, (2024).
  24. Du, X., Wang, Z., Gao, Q., Yang, H., Bao, R. and Xiong, S., “Study on novel battery thermal management using triply periodic minimal surface porous structures liquid cooling channel”, Applied Thermal Engineering, 257: 124384, (2024).
  25. Pan, D., Guo, H., Tang, S., Li, X., Wang, Z., Peng, W., Wang J. and Yan, G., “Evaluating the accuracy of electro-thermal coupling model in lithium-ion battery via altering internal resistance acquisition methods”, Journal of Power Sources, 463: 228174, (2020).
  26. Çolak, A.B. and Bayrak, M., “A Novel Machine Learning Study: Maximizing the Efficiency of Parabolic Trough Solar Collectors with Engine Oil-Based Copper and Silver Nanofluids”, Heat Transfer Research, 55(16): 51–65, (2024).
  27. Willmott, C. J., Robeson, S. M. and Matsuura, K., “A refined index of model performance”, International Journal of Climatology, 32: 2088 – 2094, (2021).
  28. Yuan, L., Li, W., Deng, W., Sun, W., Huang, M. and Liu, Z., “Cell temperature prediction in the refrigerant direct cooling thermal management system using artificial neural network”, Applied Thermal Engineering, 254: 123852, (2024).