Multi-scale Analysis of Ionic Transport and Performance Promotion for Lithium-ion/ Lithium-air Batteries

• Main Authors: San, Cheng-Hung, 三政鴻
• Other Authors: Hong, Che-Wun
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/23136216476252002188

博士 === 國立清華大學 === 動力機械工程學系 === 100 === Lithium batteries are one of the most popular energy storage devices presently. From portable electronics to electric vehicles, lithium batteries play the key role as the major power source. Because of the fundamentally low electric capacity limit in lithium-ion batteries (LIBs), brand new lithium-air batteries (LABs) with embryonic forms are proposed as a substitute for future batteries. This research intends to set up the general model to study the ionic conductivity, effective diffusion coefficient, stress-strain analysis and to predict the overall performance of lithium batteries. First of all, the quantum scale analysis was conducted to study the oxygen reduction reaction (ORR) mechanism at the air cathode of LABs. The results indicate that both Pt-adsorbed carbon nanotubes (CNTs) and Pt-doped CNTs are more effective in lowering the band gap than the pristine CNT. The phenomena are contributive to the electric conduction and the electron cloud distribution at the air cathode. For this reason, an N-doped CNT is proposed as a substitute for the Pt-doped CNT to lower down the band gap and also the production cost significantly. The simulation results reveal that the N-doped CNT obviously lowers the energy gap than the pure CNT, but still shows a slight poor performance than the Pt-doped CNT. However, the N-doped CNT shows a better specific thermal capacity than the Pt-doped CNT to reinforce the thermal effect during the charge-discharge processes. Secondly, from the molecular aspect, this research focuses on reinforcement of the poly electrolyte to reduce the dendrite phenomena during the charge-discharge processes of lithium ion batteries. Molecular dynamics simulations are employed to design the optimal ratio between the polyethylene oxide (PEO), which provides the ionic conductivity, and the polystyrene (PS), which provides mechanical strength in the polymeric electrolyte. Four variations of PS weight ratios (0%, 30%, 50%, 70%) are studied to predict the ionic conductivities and their Young’s modulus. The results indicate that the ionic conductivities are enhanced with the decrease of the PS weight ratio, resulting from the weakening oxygen transport effect in the internal electrolyte. On the other hand, the Young’s modulus increases with the rise of PS weight ratio, resulting from enhancing the internal Van der Waal’s force and the inter-molecular potential. For this reason, a clear prediction of an optimal PEO-PS weight ratio can be determined to design the optimal electrolyte for the LIBs. The result indicates that if the PS weight ratio is chosen between range of 40% and 50%, the electrolytic material can avoid dendrite defects without compromising the ionic conductivity significantly for lithium-ion batteries. Finally, a macro-scale performance study was conducted to predict the overall performance for the lithium-ion batteries. The anode, the electrolyte, and the cathode material effects are taken into consideration. This part combines the microscopic molecular dynamics (MD) simulation with the traditional macroscopic computational mass transfer (CMT) to predict the performance of various lithium battery designs. Molecular simulations are employed to predict the diffusion coefficients and ionic conductivities of Li ions in the porous anode, cathode and electrolytes under different salt concentrations and operation temperatures. The MD results are input to the CMT code, which is based on the balance equations of current, charge and materials, with the Bulter-Volmer equation to predict the battery performance. Two kinds of cathode materials, LiMn2O4 and LiFePO4, three kinds of electrolytes, e.g., PEO, PS-PEO and EMI-TFSI ionic liquid, and three discharging conditions: 0.5C, 1C, and 2C, are chosen to carry out parametric studies. The results show that the performance can be optimized by trading-off the above parameters using this multi-scale simulation technique.