TY - JOUR
T1 - Optimisation of PCM passive cooling efficiency on lithium-ion batteries based on coupled CFD and ANN techniques
AU - Li, Weiheng
AU - Li, Ao
AU - Yin Yuen, Anthony Chun
AU - Chen, Qian
AU - Yuan Chen, Timothy Bo
AU - De Cachinho Cordeiro, Ivan Miguel
AU - Lin, Peng
PY - 2025/1/15
Y1 - 2025/1/15
N2 - Ever since the lithium-ion batteries (LIBs) outbreak, there has been an exponential bloom of application over the last decade, especially for electric vehicles, automobiles and other transportation systems. Nonetheless, as the first-generation LIBs eventually aged and became increasingly thermally unstable, the utilisation of thermal management cooling systems is essential to maintain the safe operation of LIB packs in the long term. Compared to active cooling methods, passive cooling often offers a cost-effective, easy-to-install and energy-saving solution without significant changes to the design complexity. This article focuses on the thermal management of prismatic battery packs and proposes a coupling passive cooling method that combines phase change material (PCM) cooling and immersion cooling, which proves to be cost-effective and efficient. Furthermore, the study incorporates an artificial neural network (ANN) model into computational fluid dynamics (CFD) simulations to optimize a specific battery cooling system. This optimization takes into account the PCM package method and the properties of PCM and immersion liquid. The results demonstrate that the immersion liquid exhibits different behaviours under various PCM conditions than natural convection. Overall, this modelling framework presents an innovative approach by utilizing high-fidelity CFD numerical results as inputs for establishing a numerical dataset. Through ANN optimisation, eleven input parameters are considered, and the optimised scenario confirmed that PCM material with a density of 760 kg/m3, thermal conductivity 32 W/(m K), specific heat 1691 (J/kg K), latent heat 80,160 (J/kg), liquidus temperature 302.93 K, solidus temperature 315.15 K and direct liquid density 1.4 (g/ml), thermal conductivity 0.4 (W/m K), specific heat 1220 (J/kg K) with side thickness 5 (mm) and mid thickness 2.5 (mm). With this combination, the optimised performance demonstrated considerable decreases in the maximum temperature and the temperature difference by 4.26 % and 10.8 %, respectively. This approach has the potential to enhance the state-of-the-art thermal management of LIB systems, reducing the risks of thermal runaway and fire outbreaks. © 2024 Elsevier Ltd
AB - Ever since the lithium-ion batteries (LIBs) outbreak, there has been an exponential bloom of application over the last decade, especially for electric vehicles, automobiles and other transportation systems. Nonetheless, as the first-generation LIBs eventually aged and became increasingly thermally unstable, the utilisation of thermal management cooling systems is essential to maintain the safe operation of LIB packs in the long term. Compared to active cooling methods, passive cooling often offers a cost-effective, easy-to-install and energy-saving solution without significant changes to the design complexity. This article focuses on the thermal management of prismatic battery packs and proposes a coupling passive cooling method that combines phase change material (PCM) cooling and immersion cooling, which proves to be cost-effective and efficient. Furthermore, the study incorporates an artificial neural network (ANN) model into computational fluid dynamics (CFD) simulations to optimize a specific battery cooling system. This optimization takes into account the PCM package method and the properties of PCM and immersion liquid. The results demonstrate that the immersion liquid exhibits different behaviours under various PCM conditions than natural convection. Overall, this modelling framework presents an innovative approach by utilizing high-fidelity CFD numerical results as inputs for establishing a numerical dataset. Through ANN optimisation, eleven input parameters are considered, and the optimised scenario confirmed that PCM material with a density of 760 kg/m3, thermal conductivity 32 W/(m K), specific heat 1691 (J/kg K), latent heat 80,160 (J/kg), liquidus temperature 302.93 K, solidus temperature 315.15 K and direct liquid density 1.4 (g/ml), thermal conductivity 0.4 (W/m K), specific heat 1220 (J/kg K) with side thickness 5 (mm) and mid thickness 2.5 (mm). With this combination, the optimised performance demonstrated considerable decreases in the maximum temperature and the temperature difference by 4.26 % and 10.8 %, respectively. This approach has the potential to enhance the state-of-the-art thermal management of LIB systems, reducing the risks of thermal runaway and fire outbreaks. © 2024 Elsevier Ltd
KW - Artificial Neural Network
KW - Battery Thermal management
KW - Computational Fluid Dynamics
KW - Immersion Cooling
KW - Lithium-ion battery
KW - Phase Change Materials
UR - http://www.scopus.com/inward/record.url?scp=85208560876&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85208560876&origin=recordpage
U2 - 10.1016/j.applthermaleng.2024.124874
DO - 10.1016/j.applthermaleng.2024.124874
M3 - RGC 21 - Publication in refereed journal
SN - 1359-4311
VL - 259
JO - Applied Thermal Engineering
JF - Applied Thermal Engineering
M1 - 124874
ER -
