Determining half-cell materials in lithium-ion batteries is usually expensive, time-consuming, and destructive. Traditional methods require opening cells to extract active materials for analysis. This software enables non-destructive identification of active materials and stoichiometric properties, providing a free and accessible tool for researchers and engineers.
The approach uses a database of half-cell measurements from literature and self-conducted experiments, covering 36 anodes and 17 cathodes. By fitting possible half-cell combinations to a full-cell open-circuit voltage curve and comparing errors, the most probable match is identified. This method integrates directly into electrochemical models and allows database expansion for new materials.
Validation with known half-cell data and EDX measurements from Droese et al. confirmed high accuracy across various cell chemistries, including LTO-NMC, Graphite-NMC811, and Hard-Carbon-Sodium. The significant error difference between correct and incorrect identifications ensures reliable material determination. The algorithm also provides half-cell curves, stoichiometries, and capacities.
The software aids ageing analysis by identifying degradation mechanisms like loss of lithium inventory (LLI) and loss of active material (LAM). It also supports electrochemical model parameterisation and enhances recycling and second-life applications by enabling fast, non-destructive material identification.
Freely available, this tool can be expanded with additional half-cell data to improve accuracy and compatibility with more cell types. Future enhancements may include additional diagnostic methods, making the software an essential tool for advancing battery research and sustainability.