and New Features Overview xEV simulations require estimates of - PowerPoint PPT Presentation

Using Battery Design Studio for Battery Simulation and New Features

Overview xEV simulations require estimates of battery electrical and thermal behavior. BDS cell design process. – Physical cell description – Fitting model parameters Model Improvements – NTGP – RCRTable – DISTNP Conclusions 2

xEV Drive Cycles Time (s) SOC is SOC relatively constant but speed varies Speed over wide range. D. Prokhorov, Neural Networks , Vol. 21, Issues 2 – 3, March – April 2008, pp. 458 – 465 SOC and speed Speed (Km/hr) vary over wide range. SOC Blended Modes All Electric Range “The drive cycle represents a typical work-home commute which starts in a suburban area, characterized by UDDS, then continues on a highway, simulated by HWFET, and finally arrives to downtown urban area, UDDS .” M. Shams-Zahraei et al. J. Power Sources 216 (2012) 237-248 Challenge: Model electrothermal behavior of battery. 3

Selected Simulation Models in Battery Design Studio EV HEV/PHEV Cell Design Module/Pack Module/Pack RCR NTG DISTNP      J Y V V U p n           2 3 U a a DOD a DOD a DOD 0 1 2 3        2 Y a a DOD a DOD 4 5 6 (1) J. Newman and W. Tiedemann, J. Electrochem. Soc. Vol. 140, No. 7, July 1993 pp. (1) T. Fuller, M. Doyle, J. Newman, J. 1961-1968. M. Verbrugge and R. Conell, J. Electrochem. Soc . 141 (1994) 1-10 (2) H. Gu, J. Electrochem. Soc. , Vol. 130 No. 7 Electrochem. Soc . 149 (2002) A45-A53 (2) Battery Design LLC, “BDS 1983 pp. 1459-1464. Documentation” (3) U. S. Kim, Ch.B. Shin, C.-S. Kim, J. Power Src. 189 (2009) 841-846 Simple, easy to create Quick response for frequent Solves transport, kinetics, model , and best for simple charge/ discharge like equations discharging thermal analysis HEV/PHEV energy storage Page 4 4

BDS Cell Design Process Physical Cell Description • Coin, cylindrical pouch, prismatic • Gives size, weight, equilibrium voltage, capacity, bill of materials, etc. Fit Model Parameters • circuit, physics Text • Allows simulation of performance Battery Model (tbm) Use and/or Distribute Page Pa e 5 5

TBM files bridge the design process for batteries s im (tbm) tbm End Users Module/Pack tbm Cell Developers • System simulation Designers • Series/Parallel Materials cells • Model selection Developers • CFD with heat • Electrodes, incl. • Button cell tabbing transfer • Separator • Active • Spiral cylindrical, materials • Additives prismatic, stack • Electrolytes STAR-CCM CCM+ • Separator Batt ttery ery De Design ign Studi dio 6

Simple Cell – useful for coin cell simulation Materials developers typically use coin cells Stain inles less s steel el lid lid Spring Sp ring Spac acer er Key properties Lith ithiu ium m foil, il, 1.587 875 5 cm dia ia • Density Separa arator, or, 1.9 cm dia ia, 100 0 um • Voltage Cathode, hode, 1.27 7 cm dia ia • Surface area • Diffusion coeff. Stain inles less s steel el cas ase 1 M LiP iPF6/ 6/EC:DE C:DEC C (1:1 1 volum lume) e) 7

Fitting solid-phase diffusion coefficient of lithium nickel cobalt aluminum oxide Pulse experiments done in coin cells can be used to fit solid-phase diffusion coefficients of active materials. Y. Li (FMC), Star Global Conference 2012 Amsterdam 8

Electrolyte Models - AEM computed property sets from K. Gering EC EC3_E _EMC MC7_Li 7_LiFSI SI EC3 C3_EMC MC7_LiPF6 7_LiPF6 EC3 C31_PC10 C10_D _DMC MC59_LiPF6 9_LiPF6 EC2 C23_PC21 C21_D _DMC MC56_05L 6_05LiDFOB_ iDFOB_05L 5LiPF6 iPF6 EC EC32_D _DEC2 EC23_DMC24 3_DMC24_EP2 _EP22_LiPF6 _LiPF6 EC3 C38_DM DMC31 C31_E _EMC MC31 31_05L _05LiPF6_ iPF6_05L 5LiFSI iFSI EC3 C31_EMC4 EMC46_DE 6_DEC23_ C23_Li LiFSI SI EC3 C31_EMC4 EMC46_DE 6_DEC23_ C23_Li LiPF6 F6 EC3 C39_EMC3 EMC30_DMC 0_DMC30 30_LiPF6 _LiPF6 EC1 C15_EMC6 EMC64_ 4_GBL GBL21 21_L _LiFSI iFSI PC5 C58_DM DME42_ E42_Li LiTFS FSI PC PC58_D _DME42_LiCF ME42_LiCF3S 3SO3 O3 Electrolyte properties available in next release of BDS. 9

Comparison of Gering’s computed properties with literature values EC31_ EC 1_PC PC10_DM 0_DMC59 C59_LiPF6 _LiPF6 152 (5 L. Valøen en and d J. Reimer imers, , J. Ele lectroch rochem em. . Soc., ., 152 (5) ) A882- A891 1 (2005 005) 10

Electrolyte Models - Tabular Input Experimental values for electrolyte properties can be directly entered in tabular form. Available in BDS 8.02.010 11

Physical Cell Description: Stacked Plate Cells 12

Physical Cell Description: Spirally-Wound Cells - Tabbed electrode 13

Physical Cell Description: Spirally-Wound Cells - Multi-tabbed electrode 14

Physical Cell Description: Spirally-Wound Cells - Edge Collector with clamp Available in next release of BDS. 15

NTG Model – Parameter Fitting with Polynomials Polynomial fits introduce significant error. 16

NTG Model – Parameter Fitting with Bezier Curves Bezier curves conform to parameter values exactly. Parameter input is simplified since only values need to be provided. 17

NTGP Model: Peukert Effect Compa mparis rison n of data a and d fitt itted Compa mparis rison n of data a and d fitt itted (NTG TG model) del) voltage ltage behav havior ior for (NTGP TGP mode del) l) voltage ltage behav havior ior for r cell ll with ith 100 0 micr icron on thic ick k catho hode de cell ll with ith 100 0 micr icron on thic ick k catho hode de coat ating. ing. coat ating. ing. NTGP model improves fit to data when discharge or charge capacity depends on rate. 18

RCRTable – an enhanced RCR model RC RCRT RTable Model RC RCR R Model C=  /R p C= C=  /R p C= OCV OCV R s R s R p,1 ,1 R W R p Enhan ancemen cements s provided ovided by M. Verbrugge and R. Conell, J. Electrochem. Soc . 149 (2002) A45-A53 RCRTa RTable ble RCR R model del is is u useful eful for r sim imula ulati ting ng • Enter er mode del l par arame ameters ers at perform rforman ance ce of H HEV batteri eries es specific ecific SOC values lues dir irectly ectly, no Para rame meters ers are e a funct ction ion of • need ed to fit it to polynomials lynomials SO SOC repr pres esented ented by polyno lynomials mials • Use e lin linear ar in interp rpola lati tion on for Para rame meters ers depend pend on • tempera perature ure depen penden dence ce tempera peratures ures via ia Arrh rhen enius ius • Pola lari riza zation ion res esist istan ance ce can n relat lation ion depend pend on curre rrent nt (rat rate e or Tafel el effec ect) • War arburg burg im impeda dance nce  Capabilit pability to fin ine tune e fits its to to data 19

RCRTable: Rate Dependent Resistance       1    R R   p , 1 p , 0 i i      exp       i i   1 0 R p,1 = Polari larisat ation ion res esis istan ance R p,0 = Polari larisat ation ion res esis istan ance at zero ero curren urrent i 1 , i 0 = Adjus ustable able para ramet eter ers Current, Resistance, Cell resistance decreases with A mOhms increasing current 50 2.122 100 1.473 20

RCRTable Model: Warburg Effect C=  /R p C=   R R R R p,1 = Polari larisat ation ion res esis istanc nce p p , 1 W OCV R W = = Warbu rburg rg imped pedanc ance t A d & & B d are re coef oefficien icients R s  R A V oc oc is related lated to the he OCV curv rve W d  R p,1 B V ,1 R W e e d oc Cell resistance increases with time. 21

DISTNP Model: SEI Growth • Sei growth by solvent reduction is one dominant aging mechanism in Li-ion batteries: • increase in cell resistance • irreversible consumption of available Lithium • deterioration in: • capacity M. Inaba, Y. Iriyama, T. Abe, Z. Ogumi, Electrochim. • rate capability Acta 47 (2002) 1975-1982 • experimental investigations have shown a sqrt(t) dependency • a new SEI growth feature was implemented in BDS using a diffusion based transport model after Ploehn et al. (J. Echem. Soc. 151(3) A456 (2004)) • feature available for Li_Pouch_dist1D in release 8.02 but will be available for other Dist models (in STAR CCM+ as Source: arXiv:1210.3672 [physics.chem-ph]a well) 22

New SEI growth parameters in the particle menu 60  C nm nm 30  C 15  C 60  C W. cm cm 2 30  C 15  C 23

Notes on the required input data Model on or off Initial SEI thickness specified by user Values taken from Ploehn paper Running with the ‘long report’ or ‘solid - phase profiles’ option produces a very large *.pr2 file (circa 2GB in this case) 24

Run time is considerably effected by frequency of report in .prg file Changed to 1 day, which expedites the aging run time and file size 25

After simulation is run, ‘aged’ tbm file can be found in the BDS Results directory Thickness is increased compare to original and this tbm file could then be used in STAR-CCM+ BSM to represent an aged cell Capacity Fresh Aged Run a capacity test on both new and aged tbm files to see effect 26