ABSTRACT Electric Automobiles (EAs) and Hybrid Electric Automobiles (HEAs) provide feasible alternatives for reducing environmental pollution and fossil fuel dependence. The necessity to optimize thermal performance in Battery Thermal Management Systems (BTMS) for improving battery economy and safety drives this research. The present study primarily investigates the thermal performance of three BTMS designs: conventional (D1), wavy‐walled (D2), and grooved‐walled (D3). The performance assessment parameters including maximum temperature (T max ), temperature difference (ΔT), and pumping power characteristics (W p ) of the battery pack were numerically investigated in COMSOL Multiphysics software. Both designs, D2 and D3, significantly decrease T max and ΔT inside the battery pack. The smallest T max value (≈303 K) is evident in designs D2 and D3 at a Reynolds Number of 16 000. Design D3 outperformed design D1 and D2 as far as the distribution of temperatures and hotspots in the battery pack are concerned. In the case of designs D2 and D3, with an increase in the height of grooves, the normalized pumping power ratios change from 3.5 to 6.5 and 1 to 2.5 respectively. An Artificial Neural Network (ANN) model is also developed to predict T max inside the battery pack for different BTMS configurations and flow distributions. An overall R value of 0.99505 is obtained for the neural network indicating that the ANN model developed is very capable of predicting the T max inside the battery pack with respect to various inputs.
Sofi et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: