Predictive Models of Li-ion Battery Lifetime

Transcription

1 Predictive Models of Li-ion Battery Lifetime Kandler Smith, Ph.D. Eric Wood, Shriram Santhanagopalan, Gi-Heon Kim, Ying Shi, Ahmad Pesaran National Renewable Energy Laboratory Golden, Colorado IEEE Conference on Reliability Science for Advanced Materials and Devices Colorado School of Mines Golden, Colorado September 7-9, 2014 NREL/PR NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

2 Key Messages Semi-empirical battery lifetime models are generally suitable for system design & control o Long-term validation still needed o Standardization would benefit industry o Characterization requires expensive cell aging experiments Physics lifetime models are needed to reduce test time as well as guide future cell design o Open questions remain how best to model electrochemo-thermomechanical processes across length- and time-scales Liu et al., J. Echem Soc. (2010) NREL Life & MSMD Models 2

3 NREL Electrochemical/Thermal/Life Models Multi-Scale Multi-Domain (MSMD) model Inter-domain coupling of field variables, source terms Efficient, flexible framework for physics expansion Leading approach for large-cell computer-aided engineering models Electrochemical Current Collector (Cu) Negative Electrode p Separator Positive Electrode Current Collector (Al) Thermal Kim et al. (2011) Multi-Domain Modeling of Lithium-Ion Batteries Encompassing Multi-Physics in Varied Length Scales, J. of Electrochemistry, Vol. 158, No. 8, pp. A955 A969 3

4 NREL Electrochemical/Thermal/Life Models Life-predictive model Physics-based surrogate models tuned to aging test data Implemented in system design studies & real-time control Regression to NCA, FeP, NMC chemistries NCA = Nickel-Cobalt-Aluminum FeP = Iron Phosphate NMC = Nickel-Manganese-Cobalt Illustration by Josh Bauer, NREL Minneapolis Houston 10 C Air cooling Liquid cooling, chilled fluid Air cooling, low resistance cell Phoenix 15 C No cooling Li-ion graphite/nickelate life: PHEV20, 1 cycle/day 54% DoD 30 C 25 C 20 C Phoenix, AZ ambient conditions 33 miles/day driving, 2 trips/day 4

5 Outline Background Li-ion Batteries o Working principles o Electrochemical window o Degradation mechanisms Life Predictive Modeling Automotive Life Studies & Control 5

6 Working Principles Neg. Electrode Graphite Hard carbon Silicon Titanate Li foil V V Li + Pos. Electrode LiXO 2, X = NiMnCo Co NiCoAl LiMn 2 O 4, LiFeP0 4 6

7 Electrochemical Operating Window charge 0% SOC 100% SOC discharge Potential vs. Li (V) Potential measured at cell terminals SOC (x in Li x C 6 or y in Li 1-0.6y CoO 2 ) Figure credit: Ilan Gur (ARPA-E) & Venkat Srinivasan (LBNL),

8 Electrochemical Window Degradation Figure credit: Ilan Gur (ARPA-E) & Venkat Srinivasan (LBNL) 2007 Potential vs. Li (V) SOC (x in Li x C 6 or y in Li 1-0.6y Co)O 2 ) 8

9 Negative Electrode Degradation (Graphite) 1) Manufacturing environment 2) Application environment i) graceful fade (time at high T,SOC) Graphene layer SEI Li+ Graphite exfoliation, cracking (gas formation, solvent co-intercalation) Electrolyte decomposition and SEI formation Donor solvent SEI conversion, stabilization and growth SEI dissolution, precipitation Positive/negative interactions Negative/ electrolyte interactions ii) sudden fade/ damage Lithium plating and subsequent corrosion (cycling at low T) Figure credit: Vetter et al., Journal Power Sources,

10 Positive Electrode Degradation (Metal Oxide) Positive/electrolyte interactions Positive/negative interactions Figure credit: Vetter et al., Journal Power Sources, 2005 X. Xiao et al., Echem Comm., 32 (2013)

11 Mechanical Coupled Stress & Degradation Least understood amongst ECTM physics Figure credit: Santhanagopalan, Smith, et al., Artech House (in press) 11

12 Mechanical Coupled Stress & Degradation Examples Cannarella, J. Power Sources (2014) Diercks, Packard, Smith et al., J. Echem Soc (2014) Figure credit: Santhanagopalan, Smith, et al., Artech House (in press) 12

13 Outline Background Li-ion Batteries Life Predictive Modeling o Physics-based o Semi-empirical Automotive Life Studies & Control 13

14 Degradation Mechanism vs. Length Scale Chemistry SEI growth Li plating Electrolyte decomposition Gas generation Particle scale SEI μ-cracking Fracture, damage of transport paths Phase evolution, voltage droop Electrode scale Electrode creep, delamination, isolation Separator pore closure Pore clogging boundary conditions Cell scale 3D elec, thermal, mech. inhomogeneities Tab effects Stack/wind Module scale Thermal & mechanical

15 Macro-scale Stress Model Stress/strain due to thermal and electrode bulk concentration changes Coupled echem & thermal Behrou, Maute, Smith, ECS Mtg. (2014)

16 Micro-scale Stress/Degradation Model Damage evolution Performance impact An, Barai, Smith, Mukherjee, JES

17 NREL Life Predictive Model Calendar fade SEI growth Loss of cyclable lithium Coupled with cycling a 1, b 1 = f( DOD,T,V oc, ) NCA Cycling fade Active material structure degradation and mechanical fracture a 2, c 2 = f( DOD,T,V oc, ) Relative Capacity (%) r 2 = NCA Relative Resistance Relative Capacity R = a 1 t z + a 2 N Q = min ( Q Li, Q sites ) Time (years) Arrhenius-Tafel-Wohler model describing a 2 ( DOD,T, V) Li-ion NCA chemistry Q Li = b 0 + b 1 t z + Q sites = c 0 + c 2 N + Data: J.C. Hall, IECEC, Correct separation of calendar vs. cycling degradation for extrapolation of ½ year testing to 10+ year life Extensible to untested drive cycles, environments (state form) 17 17

18 Knee in Fade Critical for Predicting End of Life Example simulation: 1 cycle/day at 25 C 50% DOD: Graceful fade controlled by Li loss ~ t 1/2 NCA 80% DOD: Transitions to electrode site loss, N ~ 2300 cycles Time (Days) Life over-predicted by 25% without accounting for transition from Li loss (~chemical) to site loss (~mechanical) 18

19 Electrode Site Loss Cell Aging Data Graphite/iron-phosphate meta-dataset from multiple labs FeP 19

20 Electrode Site Loss Cell Aging Data Graphite/iron-phosphate meta-dataset from multiple labs 13 of 50+ test conditions show apparent knee in capacity fade curve FeP 20

21 c Electrode Site Loss Model (graphite/iron phosphate) 2 = c q 2, ref exp ( binder intercal. Ea 1 1 Ea 1 1 Crate ( ))[ ] + 3 exp( ( ))( ) t pulse m DOD m T m. R q = min( q Li, q T T ref sites z = b + b t b N = c c N Li accelerated polymer failure at high T bulk intercalation strain ). q sites bulk thermal strain R T T ref C rate, ref t pulse, ref intercalation gradient strain, accelerated by low temperature Model successfully describes 13 aging conditions from 0 C to 60 C FeP 21

22 Outline Background Li-ion Batteries Life Predictive Modeling Automotive Life Studies & Control o Temperature (xev) o Charge control (xev / grid) o Prognostic/duty-cycle control (xev) 22

23 Ambient Effects on Battery Average Temperature Temperature (C) BEV75 Passive cooling Temperature (C) PHEV35 Chilled liquid cooling Phoenix, AZ Los Angeles, Minneapolis, Average Ambient CA MN w/ solar effects w/ driving effects 0 Phoenix, AZ Los Angeles, Minneapolis, CA MN Average Ambient w/ solar effects w/ driving + active TMS Ambient conditions dominate Thermal connection with passenger cabin, parking in shaded structures strongly influence battery life Battery temperature and lifetime weakly coupled to ambient conditions 23

24 Optimized Charging Strategies Reduce time spent at high SOC (delay charging) Avoid high C-rates to lower peak temperatures Cost function CU-Boulder/Hoke (2014) 24

25 Optimized Charging Strategies A) Constant energy cost B) Variable energy cost Begin charge End charge Begin charge End charge Delayed charging best Response to price signals No V2G energy exported until electricity price $0.50/kWh 25

26 NREL ARPA-E AMPED Projects in Battery Control AMPED = Advanced Management and Protection of Energy Storage Devices Eaton Corporation Utah State/Ford Washington Univ. Project: Downsized HEV pack by 50% through enabling battery prognostic & supervisory control while maintaining same HEV performance & life NREL: Life testing/modeling of Eaton cells; controls validation on Eaton HEV packs Project: 20% reduction in PHEV pack energy content via power shuttling system and control of disparate cells to homogenous endof-life NREL: Requirements analysis; life model of Ford/Panasonic cell; controls validation of Ford PHEV packs Project: Improve available energy at the cell level by 20% based on real-time predictive modeling & adaptive techniques NREL: Physics-based celllevel models for MPC; implement WU reformulated models on BMS; validate at cell & module level 26

27 NREL ARPA-E AMPED Projects in Battery Control Utah State/Ford 1C Energy (Wh) Teardown analysis of automotive pack aged to 70% remaining energy shows ±5.5% variation at EOL ±5.5% variation at EOL Project: 20% reduction in PHEV pack energy content via power shuttling system and control of disparate cells to homogenous endof-life NREL: Requirements analysis; life model of Ford/Panasonic cell; controls validation of Ford PHEV packs Active balancing most benefits energy applications with large cellto-cell disparity Key question: How much does cell-to-cell disparity grow with age? Extreme driving conditions, large pack ΔT cause abnormally large cell capacity imbalance growth 27

28 Summary Main factors controlling battery lifetime o Time at high T & SOC (weak coupling with DOD & C-rate) o Cycling at high DOD & C-rate; Low/high T & SOC Semi-empirical battery lifetime models are generally suitable for system design & control o NREL models describe various commercial chemistries o Life extensions of 20% to 50% may be possible Physics lifetime models to provide design feedback o Electrochemical/thermal processes well understood o Mechanics coupling underway at various length scales 28

29 Acknowledgements Funding US Dept. of Energy Vehicle Technologies Office: Brian Cunningham, David Howell US Dept. of Energy Advanced Research Projects Agency: Ilan Gur, Patrick McGrath, Russel Ross US Army Tank Automotive Research, Development & Engineering Center: Yi Ding, Sonya Zanardelli, Matt Castanier Collaborators Texas A&M University: Partha Mukherjee, Pallab Barai, Kai An University of Colorado at Boulder: Kurt Maute, Reza Behrou, Dragan Maksimovic, Andrew Hoke Colorado School of Mines: Corinne Packard, David Diercks, Brian Gorman Eaton Corporation: Chinmaya Patil Utah State: Regan Zane Ford Motor Company: Dyche Anderson 29

30 Thank you 30