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The present study aims to evaluate the feasibility of a novel dual-evaporator loop heat pipe (DE-LHP) in battery thermal management systems (BTMS). A 3S4P (3-series and 4-parallel) Li-ion battery module with a 12.6 V and 10 Ah capacity is made and tested under various C rates for different environmental conditions. The battery generates an average heat of 11.28 W@1C and 24.44 W@1.5C with 100% discharge and 43.36 W@2C with 50% discharge at an ambient temperature of 30 °C. An increase in temperature from 30 to 40 °C results in a decline in the depth of discharge. The proposed DE-LHP is evaluated using deionized water with filling volumes of 12, 16, 20, and 24 mL. The DE-LHP started working above 5 W regardless of the filling volume, and before the evaporator reached dry-out, the DE-LHP transferred heat loads of 10, 30, 35, and 55 W with filling volumes of 12, 16, 20, and 24 mL, respectively. At a heat load of 30 W, the evaporator section of the 20-mL filled LHP demonstrated a minimum thermal resistance of 0.274 KW-1, while the condenser section exhibited a minimum thermal resistance of 0.372 KW-1 at a heat load of 35 W. The DE-LHP is found to be effective in transferring heat of 35 W to keep the module temperature below 60 °C. Based on these initial findings, it is suggested that incorporating the multi-evaporator LHP-based BTMS could be a feasible and efficient solution for the thermal management of battery modules/packs for electric vehicles. [ABSTRACT FROM AUTHOR]

Pulsating heat pipe (PHP) is one of the prominent research areas in the family of heat pipes. Heat transfer and fluid flow mechanism associated with PHP are quite involved. The analytical models are simple in nature and limited in scope and applicability. The regression models and Artificial Neural Network (ANN) are also limited to a number of input parameters, their ranges and accuracy. The present paper discusses the thermal performance prediction models of a PHP based on ANN and RCA approach. Totally 1652 experimental data are collected from the literature (2003-2017). Nine major influencing input variables are considered for the first time to develop the prediction models. Feed-forward back-propagation neural network is developed and verified. Backward regression analysis is used in RCA-based regression model. Linear and power-law regression correlations are developed for input heat flux in terms of dimensionless Kutateladze (Ku) number, which is a function of Jakob number (Ja), Morton number (Mo), Bond number (Bo), Prandtl number (Pr) and geometry of a PHP. The prediction accuracy of present regression models (R2 = 0.95) is observed to be better as compared with literature-based correlations. [ABSTRACT FROM AUTHOR]