Battery space optimization to limit heat transfer in a lithium-ion battery using adaptive elephant herding optimization
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ORIGINAL PAPER
Battery space optimization to limit heat transfer in a lithium-ion battery using adaptive elephant herding optimization Chaoyi Wan 1 Received: 25 March 2020 / Revised: 15 May 2020 / Accepted: 24 May 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Pure electric vehicles have a variety of benefits such as energy efficiency, zero environmental emissions, elimination in air pollution, and decreased carbon dioxide emissions. While it offers major benefits, it suffers from numerous battery-related issues, and, among them, heat dissipation is considered to be a major challenge, leading to significant performance degradation if not handled properly. In this present work, a battery thermal management system design is presented using ANSYS Fluent and adaptive elephant herding optimization algorithm for optimizing the battery spacing, reducing the heat dissipation, and ensuring a proper battery temperature in the lithium-ion battery pack. The adaptive elephant herding optimization algorithm provides an optimal battery spacing of (17, 23, 21, 0.23, 0.23, 0.174, 0.174), and the maximum temperature, minimum temperature, and temperature difference values observed are 298.3112 K, 292.9874 K, and 5.47 K, respectively. The research findings show that the adaptive elephant herding optimization algorithm works as an appropriate cost-effective strategy for depicting the influence of the battery spacing towards the battery temperature and results in a uniform cooling of the entire battery pack. Keywords Lithium-ion battery pack . Heat dissipation . ANSYS Fluent . Battery thermal management system . Battery space optimization . Adaptive elephant herding optimization algorithm
Nomenclature QR Reaction heat QT Total heat generated k Turbulence kinetic energy (TKE), kinetic energy K Constants C Charge/discharge test F Faraday constant, 96,485 Columb/mol−1 T Temperature, K Wv Working voltage of battery, V OV Open current voltage, V I Current, ampere (A) OIR Ohmic IR of battery PIR Polarization IR of battery DIR Direct Current IR of battery q Rate of heat generation per unit volume, Jm−3 s−1
* Chaoyi Wan [email protected]; [email protected] 1
School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
V Volt a,b,c i,j HC A CV t p v e u z S R F0, C1ɛ, C2ɛ Tm Tb OP
Volume of LI battery, m3 Voltage of LI battery Cartesian coordinate displacements Cartesian coordinate direction; individual elephant’s clan Specific heat capacity, J kg−1 K−1 Cross-sectional area, m2 Convection heat transfer coefficient, W m−2 K−1 Time, s Pressure, N/m2 Total velocity, ms−1 Total energy Velocity Quality (kg) Source terms Total resistance of the battery Empirical parameters TKE generation by mean velocity gradients TKE generation by buoyancy Describes the influence of the fluctuating dilation incompressible turbulence compared with the overall dissipation rate
Ionics
m h cit rand hmax hmin ei hti;worst CP1
Matriarch Individual elephants Centre of the clan Stochastic and
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