Performance optimization of binary fluid-filled heat pipes for battery thermal management applications using RSM and ANN schemes: A comparative assessment

Abstract

In the present scenario, the development of efficient lithium-ion energy storage system-based electric vehicles has been turned into the focus as an effective alternative to conventional transportation systems. However, correspondingly associated potential risks of thermal failure confirm the need for an effective thermal management system to mitigate the adverse effects of excessive heat generated during operation. This work takes an opportunity to present a comparative performance assessment of response surface method (RSM) and artificial neural networks (ANN)-based predictive models to analyze the thermal behavior of oscillating heat pipes (OHP) filled with binary working fluids (acetone-DI water, acetone-methanol, and acetone-ethanol) with mixing ratio (1:1 ≤ MR ≤ 2:1) and volumetric filling ratio (30%≤FR ≤ 70%) as independent variables at different battery discharge rates (1C ≤ DR ≤ 2C). The thermal performance is predicted in terms of maximum cell temperature (MCT), average cell temperature (ACT), and thermal uniformity (TD). The acetone-DI water binary mixture was identified as the optimal working fluid among the fluids evaluated. The ACT, MCT, and TD were determined to be 38.3°C, 40°C, and 3.4°C, respectively, under a filling ratio (FR) of 70%, mixing ratio (MR) of 2:1, and a discharge rate (DR) of 2C. The prediction model performance is measured using correlation coefficient (R²) value and average absolute prediction error (APE) to acknowledge the best fit. The results confirm the applicability of both the prediction models with the ANN model (R²-values ≥ 0.99998; average APE ≥ 0.0162%) slightly more accurate relative to the corresponding RSM approach (R²-values ≥ 0.9814; average APE ≥ 0.2342%).