2018
DOI: 10.1016/j.apenergy.2018.06.143
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Compact liquid cooling strategy with phase change materials for Li-ion batteries optimized using response surface methodology

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Cited by 155 publications
(30 citation statements)
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“…Compared with basic active BTMS, PCM adds extra weight to the whole structure. Considering the heavier weight and larger volume of hybrid BTMS, Ling et al [70] analyzed the influence of the composition of PCM, the set of the battery module, and the active cooling configuration on the heat-transfer capacity to minimize the mass of PCM used in BTMS. They found that the optimized design of this hybrid BTMS helps to save up to 94.1% in mass and 55.6% in volume of PCM.…”
Section: Pcm Coupled With Air or Liquid Active Cooling Methodsmentioning
confidence: 99%
“…Compared with basic active BTMS, PCM adds extra weight to the whole structure. Considering the heavier weight and larger volume of hybrid BTMS, Ling et al [70] analyzed the influence of the composition of PCM, the set of the battery module, and the active cooling configuration on the heat-transfer capacity to minimize the mass of PCM used in BTMS. They found that the optimized design of this hybrid BTMS helps to save up to 94.1% in mass and 55.6% in volume of PCM.…”
Section: Pcm Coupled With Air or Liquid Active Cooling Methodsmentioning
confidence: 99%
“…Their results demonstrated that both the battery temperature ramp-up rate and the battery steady-state temperature were significantly reduced by the conjugated cooling, in comparison with single PCM or liquid cooling condition. Ling et al [267] investigated an optimization method, the response surface methodology (RSM) with PCM and liquid cooling integrated for LIB, which helped retain the PCM mass by up to 94.1% and the volume by up to 55.6%. Compared with the conventional LCS, the hybrid system is not only highly efficient but lightweight and flexible for the batteries with arbitrary shapes.…”
Section: Appl Sci 2019 9 X 27 Of 46mentioning
confidence: 99%
“…It can define the influence of independent variables individually or in combination on a response or a set of responses of a process, and simultaneously optimize the levels of experimental variables to attain the optimal performance of the system. Some examples of the RSM applications of optimization of chemical or biomedical process are enzymatic processing [21,22], heat transfer with nano-fluid [23,24], micro-extraction for pre-concentration [25], ultrasonicated adsorption and extraction process [26][27][28], cooling strategy for Li-ion batteries [29] and fibre manufacturing [30][31][32][33]. RSM is used to approximate the functional relationship between the independent variables and the process response based on experimental data, and it is valuable in obtaining insight of variable contributions through the coefficients in the developed model.…”
Section: Introductionmentioning
confidence: 99%