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

Öz

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.

Anahtar Kelimeler

• Batarya termal yönetimi
• sıvı soğutma
• minimal yüzey alanı
• gözenekli yapı
• makine öğrenmesi

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

Öz

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.

Anahtar Kelimeler

• Battery thermal management
• liquid cooling
• minimal surface area
• porous structure
• machine learning

Kaynakça

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