From molecular models to system analysis for lithium battery electrolytes

From molecular models to system analysis for lithium battery electrolytes

TitleFrom molecular models to system analysis for lithium battery electrolytes
Publication TypeJournal Article
Year of Publication2002
AuthorsJohn B Kerr, Steven E Sloop, Gao Liu, Yongbong Han, Jun Hou, Shanger Wang
JournalJournal of Power Sources
Volume110
Pagination389-400
Keywordsion solvation, lithium battery, mobility, molecular models, polymer electrolytes, rheological models, sei layer, system models
Abstract

The behavior of polymer electrolytes in lithium batteries is reviewed in the context of molecular scale models as well as on the system scale. It is shown how the molecular structure of the electrolyte strongly influences ion transport through the polymer as well as across the interfaces and determines the values of a number of parameters needed for system models that can predict the performance of the battery (e.g. [kappa], D, t0+ and i0). The interaction of the electrolyte with the electrodes not only leads to transfer of the lithium ion across the interface but also to side reactions that profoundly influence the calendar and life cycle of the battery. Typically these electrochemically induced side reactions generate the SEI layer, but inherent instability of the bulk electrolyte may also play a role in the formation of surface layers. These various reactions can lead to changes in the mechanical properties of the separator and electrode structure that promote life-limiting phenomena such as dendrite growth, passivation and morphology changes. The rheological model of Eisenberg is drawn upon to show how the interactions of the electrolyte with surfaces can lead to distinct changes in mechanical and transport properties that may limit the battery performance and lead to diminished performance with time. The molecular level models may be combined with the rheological models to provide workable models of the interfaces and bulk electrolyte dynamics that in turn can be used to provide a more accurate level of performance prediction from the system models. This connects molecular structure with battery performance and guides the design and synthesis of new and better materials.

DOI10.1016/S0378-7753(02)00202-1