Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles
Seojoung Park, Hyunjun Lee, Zoe K. Scott-Nevros, Dongjun Lim, Dong‐Hwa Seo, Yunseok Choi, Hankwon Lim, Donghyuk Kim
Abstract
. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.
Topics & Concepts
Battery (electricity)State of healthCathodeComputer scienceMean squared errorLithium (medication)IonMaterials scienceLithium-ion batteryDeep learningElectrochemistryArtificial intelligenceElectrodePower (physics)Electrical engineeringChemistryEngineeringStatisticsMathematicsThermodynamicsPhysicsPhysical chemistryMedicineEndocrinologyOrganic chemistryAdvanced Battery Technologies ResearchAdvancements in Battery MaterialsElectric Vehicles and Infrastructure