001016981 001__ 1016981
001016981 005__ 20240709081918.0
001016981 037__ $$aFZJ-2023-03888
001016981 041__ $$aEnglish
001016981 1001_ $$0P:(DE-Juel1)190784$$aAli, Haider Adel$$b0$$eCorresponding author
001016981 1112_ $$a244th ECS Meeting$$cGothenburg$$d2023-10-08 - 2023-10-12$$wSweden
001016981 245__ $$aA Hybrid Electrochemical Multi-Particle Model for Li-ion Batteries
001016981 260__ $$c2023
001016981 3367_ $$033$$2EndNote$$aConference Paper
001016981 3367_ $$2BibTeX$$aINPROCEEDINGS
001016981 3367_ $$2DRIVER$$aconferenceObject
001016981 3367_ $$2ORCID$$aCONFERENCE_POSTER
001016981 3367_ $$2DataCite$$aOutput Types/Conference Poster
001016981 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1698232429_9042$$xAfter Call
001016981 520__ $$aPhysics-based models have proven to be effective tools for understanding the behavior of Li-ion batteries, which is essential for improving their design and performance. Among the various physics-based models, the Doyle-Fuller-Newman (DFN) model has emerged as the most widely used due to its accurate simulation of battery behavior. To address certain limitations, the Multiple-Particle DFN (MP-DFN) model was introduced. The MP-DFN model employs multiple electrode particle sizes to account for internal concentration heterogeneities and accurately capture slow diffusion processes. However, it is worth noting that the MP-DFN model comes with a relatively high computational cost. To overcome these challenges, this study has developed a Hybrid-Multiple-Particle DFN (HMP-DFN) model.
001016981 536__ $$0G:(DE-HGF)POF4-1223$$a1223 - Batteries in Application (POF4-122)$$cPOF4-122$$fPOF IV$$x0
001016981 536__ $$0G:(DE-Juel1)BMBF-03SF0628$$aLLEC::VxG - Integration von "Vehicle-to-grid" (BMBF-03SF0628)$$cBMBF-03SF0628$$x1
001016981 7001_ $$0P:(DE-Juel1)176196$$aRaijmakers, Luc$$b1
001016981 7001_ $$0P:(DE-Juel1)186070$$aChayambuka, Kudakwashe$$b2
001016981 7001_ $$0P:(DE-Juel1)173719$$aDanilov, Dmitri$$b3
001016981 7001_ $$0P:(DE-Juel1)165918$$aNotten, Peter H. L.$$b4
001016981 7001_ $$0P:(DE-Juel1)156123$$aEichel, Rüdiger-A.$$b5
001016981 909CO $$ooai:juser.fz-juelich.de:1016981$$pVDB
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190784$$aForschungszentrum Jülich$$b0$$kFZJ
001016981 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)190784$$aRWTH Aachen$$b0$$kRWTH
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176196$$aForschungszentrum Jülich$$b1$$kFZJ
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186070$$aForschungszentrum Jülich$$b2$$kFZJ
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173719$$aForschungszentrum Jülich$$b3$$kFZJ
001016981 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)173719$$a Eindhoven University of Technology$$b3
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165918$$aForschungszentrum Jülich$$b4$$kFZJ
001016981 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)165918$$a Eindhoven University of Technology$$b4
001016981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156123$$aForschungszentrum Jülich$$b5$$kFZJ
001016981 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)156123$$aRWTH Aachen$$b5$$kRWTH
001016981 9131_ $$0G:(DE-HGF)POF4-122$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1223$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vElektrochemische Energiespeicherung$$x0
001016981 9141_ $$y2023
001016981 920__ $$lyes
001016981 9201_ $$0I:(DE-Juel1)IEK-9-20110218$$kIEK-9$$lGrundlagen der Elektrochemie$$x0
001016981 980__ $$aposter
001016981 980__ $$aVDB
001016981 980__ $$aI:(DE-Juel1)IEK-9-20110218
001016981 980__ $$aUNRESTRICTED
001016981 981__ $$aI:(DE-Juel1)IET-1-20110218