Search

In this article, analysis of average Nusselt number (Nu avg), which indicates the heat removal from the battery pack cooled by flowing fluid is carried out considering coupled heat transfer condition at the pack and coolant interface. Five categories of coolant, mainly gases, common oils, thermal oils, nanofluids, and liquid metals, are selected. In each coolant category, five fluids (having different Prandtl number Pr) are selected and passed over the Li-ion battery pack. The analysis is made for different conductivity ratio (Cr), heat generation term (Q gen), Reynolds number (Re), and Pr. Pr varying in the range 0.0208–511.5 (25 coolants) and Cr for each category of coolant having its own upper and lower limit are used to analyze the heat removed from the battery pack. Using single feed-forward network and integrating two feed-forward networks having multi-layers with back-propagation is employed for artificial neural network (ANN) modelling. In this modelling, the concept of the main network and space network is devised for multiple back propagation (MBP). The numerical analysis revealed that the temperature distribution in battery and fluid is greatly affected by increasing Cr. The maximum temperature located close to the upper edge of battery is found to get reduced significantly with the increase of Cr , but upto a certain limit above which reduction is marginal. The analysis carried out reveals that Cr and Q gen have no role in improving Nu avg while Pr and Re vary it significantly in each step. Moreover, Nu avg is found to increase with Re continuously irrespective of any Cr and Q gen. While, for oils with an increase in Pr and Re , Nu avg was found to reduce significantly. Nanofluids are found to be more effective in improving heat transfer from the battery pack when cooled by flowing nano-coolants over it. The MBP networks proposed are successfully trained, and hence they can be used for prediction of Nu avg. • Conjugate numerical analysis of battery cells is carried for several coolants. • Multiple back propagation modelling of Nusselt number is carried. • Nanofluids provide maximum heat transfer. • Multiple feed forward with back propagation predicts Nusselt number properly. [ABSTRACT FROM AUTHOR]

Heat transfer and phase change flow inside a Li-ion battery enclosure filled with a copper metal foam embedded in paraffin wax phase change material (PCM) are studied numerically. For simplicity and due to the axisymmetric of the enclosure geometry, half of the battery cell is considered as the heat source on the right wall. The vertical walls are considered symmetric, while the bottom wall is insulated, and the top wall is subjected to the convective heat transfer. The governing equations of the flow and heat transfer are presented as partial differential equations and then transformed into non-dimensional forms and solved by employing the finite element method. The local thermal equilibrium model was used, which limits the model to metal foams with high pore densities. For validation, results are compared with some of the well-known studies in the literature. The isotherms and streamlines are presented for three levels of heat pulse intensity. Both melting and solidification occur as a result of the unsteady heat pulses induced by the thermal source. The vertical location of the battery inside the enclosure can affect the streamlines and isotherm contours. Results show that for higher heat pulse powers, the melt volume fraction (MVF) increases, and heatsink will have higher efficiency. For a comparatively strong heat pulse, the efficiency was increased about seven times. [ABSTRACT FROM AUTHOR]