17 resultados para lithium-metal battery
Resumo:
The methane solubility in five pure electrolyte solvents and one binary solvent mixture for lithium ion batteries – such as ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC) and the (50:50 wt%) mixture of EC:DMC was studied experimentally at pressures close to atmospheric and as a function of temperature between (280 and 343) K by using an isochoric saturation technique. The effect of the selected anions of a lithium salt LiX (X = hexafluorophosphate,
<img height="16" border="0" style="vertical-align:bottom" width="27" alt="View the MathML source" title="View the MathML source" src="http://origin-ars.els-cdn.com/content/image/1-s2.0-S0021961414002146-si1.gif">PF6-; tris(pentafluoroethane)trifluorurophosphate, FAP−; bis(trifluoromethylsulfonyl)imide, TFSI−) on the methane solubility in electrolytes for lithium ion batteries was then investigated using a model electrolyte based on the binary mixture of EC:DMC (50:50 wt%) + 1 mol · dm−3 of lithium salt in the same temperature and pressure ranges. Based on experimental solubility data, the Henry’s law constant of the methane in these solutions were then deduced and compared together and with those predicted by using COSMO-RS methodology within COSMOthermX software. From this study, it appears that the methane solubility in each pure solvent decreases with the temperature and increases in the following order: EC < PC < EC:EMC (50:50 wt%) < DMC < EMC < DEC, showing that this increases with the van der Walls force in solution. Additionally, in all investigated EC:DMC (50:50 wt%) + 1 mol · dm−3 of lithium salt electrolytes, the methane solubility decreases also with the temperature and the methane solubility is higher in the electrolyte containing the LiFAP salt, followed by that based on the LiTFSI one. From the variation of the Henry’s law constants with the temperature, the partial molar thermodynamic functions of solvation, such as the standard Gibbs free energy, the enthalpy, and the entropy where then calculated, as well as the mixing enthalpy of the solvent with methane in its hypothetical liquid state. Finally, the effect of the gas structure on their solubility in selected solutions was discussed by comparing methane solubility data reported in the present work with carbon dioxide solubility data available in the same solvents or mixtures to discern the more harmful gas generated during the degradation of the electrolyte, which limits the battery lifetime.An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries
Resumo:
Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.
