Impact of hybrid and mono nanofluids on the cooling performance of lithium-ion batteries: Experimental and machine learning insights

• Main Authors: Kanti P.K., Yang E.S.J., Wanatasanappan V.V., Sharma P., Said N.M.
• Other Authors: 57216493630
• Format: Article
• Published: Elsevier Ltd 2025
• Subjects: Battery cells; Cooling performance; Discharge rates; Experimental learning; Hybrid nanofluid; Ion batteries; Lithium ions; Nanofluids; Soft-Computing; Thermal

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