Advanced Energy Technologies
Mohammad Zarei-Jelyani; Shaghayegh Baktashian; Mohsen Babaiee; Rahim Eqra
Abstract
In recent years, many studies have focused on the active materials of anodes to improve the performance of LIBs, while limited attention has been given to polymer binders, which act as inactive ingredients. However, polymer binders have amazing influence on the electrochemical performance of anodes. ...
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In recent years, many studies have focused on the active materials of anodes to improve the performance of LIBs, while limited attention has been given to polymer binders, which act as inactive ingredients. However, polymer binders have amazing influence on the electrochemical performance of anodes. Herein, to investigate the binding performance between MCMB artificial graphite and the copper current collector, three binders such as PVDF, MSBR, and CMC+SBR were used to prepare the anode electrodes. The mechanical and electrochemical tests were conducted for different MCMB electrodes. The results show that the water-based binders (CMC+SBR and MSBR) made better adhesion properties for the coating on the current collector in comparison with the organic solvent-based binder (PVDF). MCMB anode fabricated with CMC+SBR binder shows the highest discharge capacity and the best rate performance at various C-rates of 0.2C, 0.5C, and 1C that result in the brilliant electrochemical performance. Therefore, artificial graphite anode materials using cheap aqueous CMC+SBR binder instead of toxic solvent like NMP and expensive PVDF improve electrochemical property and reduce the cost of LIBs.
Advanced Energy Technologies
Mohammad Zarei-Jelyani; Mohammad Sarshar; Mohsen Babaiee; Nima Tashakor
Abstract
Accurate lifetime prediction of lithium-ion batteries is a great challenge for the researchers and engineers involved in battery applications in electric vehicles and satellites. In this study, a semi-empirical model is introduced to predict the capacity loss of lithium-ion batteries as a function ...
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Accurate lifetime prediction of lithium-ion batteries is a great challenge for the researchers and engineers involved in battery applications in electric vehicles and satellites. In this study, a semi-empirical model is introduced to predict the capacity loss of lithium-ion batteries as a function of charge and discharge cycles, operational time, and temperature. The model parameters are obtained by minimizing the prediction errors of experimental capacity loss for each charge/discharge cycle at 25 oC, 35 oC, and 45 oC.The optimum values of the model parameters are obtained using genetic algorithm, one of the optimization tools in Matlab software. The model accurately predicts the capacity loss of lithium-ion battery for more charge and discharge cycles at 25 °C with an average error of 4 %. The mentioned cycles are used only to validate the prediction.
