摘要
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths,form factors,and electrochemical testing protocols.Existing models typically translate poorly across different electrode,electrolyte,and additive materials,mostly require a fixed number of cycles,and are limited to a single discharge protocol.Here,an attention-based recurrent algorithm for neural analysis(ARCANA)architecture is developed and trained on an ultralarge,proprietary dataset from BASF and a large Li-ion dataset gathered from literature across the globe.ARCANA generalizes well across this diverse set of chemistries,electrolyte formulations,battery designs,and cycling protocols and thus allows for an extraction of data-driven knowledge of the degradation mechanisms.The model’s adaptability is further demonstrated through fine-tuning on Na-ion batteries.ARCANA advances the frontier of large-scale time series models in analytical chemistry beyond textual data and holds the potential to significantly accelerate discovery-oriented battery research endeavors.
基金
This work contributes to the research performed at CELEST(Center for Electrochemical Energy Storage Ulm-Karlsruhe)and was partly funded by the German Research Foundation(DFG)under Project ID 390874152(POLiS Cluster of Excellence)
This project also received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No.957189(BIG-MAP)
funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.957213
HSS acknowledges funding from the German Research Foundation(DFG)under Project ID 390776260(eConversion Cluster of Excellence).
