Publication: Impact of hybrid and mono nanofluids on the cooling performance of lithium-ion batteries: Experimental and machine learning insights
Date
2024
Authors
Kanti P.K.
Yang E.S.J.
Wanatasanappan V.V.
Sharma P.
Said N.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
The present study investigates the preparation and application of mono and hybrid nanofluids to enhance the cooling performance of 18,650 lithium-ion batteries. Researchers dispersed Al2O3 and CuO nanoparticles in water at a volume concentration of 0.5 % to create these advanced coolants. The experimental setup evaluated battery cooling efficiency under diverse conditions, including varying coolant types, flow rates (150, 250, and 350 ml/min), and battery discharge rates (0.5 and 1C). Al2O3-CuO hybrid nanofluids exhibited superior thermal conductivity, surpassing CuO and Al2O3 mono nanofluids by 35.26 % and 29.1 %, respectively at 60 �C. Notably, the 0.5 % of Al2O3-CuO nanofluid achieved a remarkable 54.23 % reduction in lithium-ion battery cell temperature at a flow rate of 350 ml/min, compared to water alone. These findings highlight the promising potential of hybrid nanofluids as effective working fluids in thermal management systems for lithium-ion battery cells. Following the identification of the optimal nanofluid, researchers developed prediction models using machine-learning techniques. The random forest approach was employed, with linear regression serving as a baseline for comparison. The RF-based model demonstrated exceptional predictive accuracy, achieving 98.4 % accuracy compared to the LR model's 80.82 %, while maintaining minimal prediction errors. ? 2024 Elsevier Ltd
Description
Keywords
Battery cells , Cooling performance , Discharge rates , Experimental learning , Hybrid nanofluid , Ion batteries , Lithium ions , Nanofluids , Soft-Computing , Thermal