Phase change material based passive battery thermal management system to predict delay effect

• Published in: Journal of energy storage Vol. 44; p. 103482
• Main Authors: Talele, Virendra, Thorat, Pranav, Gokhale, Yashodhan Pramod, VK, Mathew
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-12-2021
• Subjects: Battery pack; Battery thermal management; Data prediction; Electric vehicle; Linear regression model; Neural network; PCM; CFD; ANN; BC; UGV; Linear regression model; CO2; LPM; Electric vehicle; PDE; R; LFP; DOD; MSE; Battery pack; Battery thermal management; EF; LIB; SOC; BTMS; Data prediction; Neural network; VOF; FEM; EV; HEV; RT-18; LF; PCM; TMS; FVM
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2021.103482

•PCM based battery thermal management with delay effect methodology is studied.•A multi-objective strategy is suggested between selection of PCM.•Battery effectiveness is predicted.•By ANN approach strong correlation has been concluded between with and without thermal management system to predict performance of pcm and delay effect.•Fixed temperature-based methodology is proposed.
In the modern scenario of the automobile industry there is a translation from conventional fuel vehicles to electric vehicles as a set to reduce carbon percentage. High power Lithium-ion batteries are the main power source of this vehicle to drive the powertrain, but due to the natural influence of various hot environmental conditions, there is a significant reduction in mileage of vehicle which is not desirable because a lithium-ion battery pack covers 50 to 60% whole cost of vehicle. To tackle this problem from recent years there are several types of research have been performed over active and passive thermal management techniques in which passive thermal management system is widely growth from recent years due to its numerous working advantages with the low-cost application. In the present study, we performed PCM based passive thermal management study over 60 Cell 18,650 Lithium-ion battery packs which are considered to be working for automobile applications. The high-power energy density battery is submerged with a layer of PCM to investigate the delay effect caused by the battery pack to resists the set limit of the threshold temperature range. The output field results from numerical investigation are predicted using neural network approach in which a multi-objective optimization strategy is proposed between battery pack delay effect for selected paraffin wax and RT-18 PCM against its given C-rate. furthermore, liquid fraction rate is monitored for the fixed temperature limit in varying C-rate condition, for optimization strategy Fixed temperature is taken as a function for time delay by PCM addition with liquid fraction rate of PCM over the varying C-rate condition. Correlations suggest paraffin wax is the best choice for a set temperature limit below 60 °C because paraffin wax is found to be melted faster and acts as a resistance to increase the temperature of the cell. The best fit line from the linear regression model using neural network approach suggest best fit line as the time delay is the function of liquid fraction rate over the selected PCM configuration. The highest time delay is occurred in case of paraffin wax PCM as 11,900 s for 1C rate and lowest time delay is occurred as 10,000 s for 1C rate in case of RT-18.