Prediction and optimization of the design decisions of liquid cooling systems of battery modules using artificial neural networks

Bulut E., Albak E. İ., Sevilgen G., Öztürk F.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, cilt.46, sa.6, ss.7293-7308, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 6
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/er.7637
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENERGY RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.7293-7308
  • Anahtar Kelimeler: artificial neural network, battery thermal modeling, CFD, cooling plate, liquid cooling, optimization, serpentine channel, LITHIUM-ION BATTERY, PLATE, POWER
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Liquid cooling systems are effective for keeping the battery modules in the safe temperature range. This study focuses on decreasing the power consumption of the pump without compromising the cooling performance. Artificial neural network (ANN) models are created to predict the effects of the height and width of the cooling channel and the mass flow rate on the maximum temperature, convective heat transfer coefficient, and pressure drop. The ANN models are used as surrogate models for the design and optimization of the liquid cooling battery system. Particle swarm optimization (PSO) and genetic algorithm (GA), which are commonly utilized optimization methods in many areas, and chaos game optimization (CGO) and coot optimization algorithm (COOT) methods, which are recently presented methods, are adopted to minimize the power consumption of the pump. The results are compared in terms of computational performance and best, worst, average, and SD values. Despite all of the optimization methods used giving similar results, the CGO method comes forward due to fast converging, SD, and finding the minimum power consumption of the pump among other optimization methods. A 22.4% decrease in the power consumption of the pump is achieved with the use of the ANN-based CGO method while conserving the cooling performance. When comparing the ANN predicted and CFD results, the relative errors are less than 1%.