50% DOD: Graceful fade (controlled by lithium loss) 80% DOD: Graceful fade transitions to sudden fade ~2300 cycles (transition from lithium loss to site loss)
NCA
PHEV10 Phoenix
Select model with best statistics
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6
Knee in curve important for predicting end of life
(Hypothesis based on observations from data) Example simulation: 1 cycle/day at 25°C
Relative Capacity (%)
Life predictive modeling and battery system tradeoff studies
Aging model
Liquid cooling Air cooling
Air cooling, low resistanngineering of batteries (CAEBAT program)
V exp
F V (t ) Vref oc T (t ) T R ref

DoD
DoD DoD ref


4. Fit rate-laws(s) 5. Fit global model(s)
Predictive model
Better life prediction methods, models and management are essential to accelerate commercial deployment of Liion batteries in large-scale high-investment applications
Time-to-market vs acceptable risk for satellite battery industry*
OEM Goals: • Optimize designs
(size, cost, life)
• •
Minimize business & warranty risk Reduce time to market
Cycling fade
• Active material structure degradation and mechanical fracture • a2, c2 = f(∆DOD,T,V)
Relative Capacity (%)
NCA
r2 = 0.942
Relative Resistance Relative Capacity
Time (years)
R = a1 t1/2 + a2 N Q = min ( QLi , Qsites )
Qsites = c0 + c2 N
Resistance Growth (mΩ)
Li-ion NCA chemistry Arrhenius-Tafel-Wohler model describing a2(∆DOD,T, V)
Hypothesis for active site loss dependence on operating parameters: • • • • C-rate (intercalation gradient strains) DOD (bulk intercalation strains) Low T (exacerbates Li intercalation-gradients) High T (exacerbates binder loss of adhesion) ∆T (thermal strains)
Battery prognostic and electrochemical control (ARPA-E AMPED program)
Advanced battery management R&D with industry & university partners
3
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NCA
Life over-predicted by 25% without “knee”
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7
Iron-phosphate (FeP) Life Model
Estimated $2M data collection effort of other labs has been leveraged for this analysis (DOE, NASA-JPL, HRL & GM, Delacourt, CMU, IFP)
Relat ive Capa city (%)
Data
Regression 1. Fit local model(s) 2. Visualize rate-dependence on operating condition 3. Hypothesize rate-law(s)
T exp
E 1 1 a R T ( t ) T ref
No cooling
3D Multiphysics simulation
Phoenix, AZ ambient conditions
Battery health estimation & management (Laboratory-Directed R&D program)
Online & offline health tracking of real-world applications
Capacity fade with “knee” region highlighted
A123 ANR-26650-M1
• • LixC6/LiyFePO4 2.3 Ah, 3.3Vnominal
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FeP
8
Active site loss controlled mainly by mechanical-driven cycling fade
•
Manage assets for maximum utilization
(e.g. route scheduling, charge control to optimize EV fleet life and cost)
2
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NREL Research & Development Addressing Life scenario Battery Lifetime analysis
2

binder Ea R

1 T
1 Tref
m DOD m T
1
m3 exp

intercal. Ea 1 R T

1 Tref

C rate C rate ,ref

t pulse t pulse ,ref

.
accelerated binder failure at high T
NREL/PR-5400-58550
Battery Congress • April 15-16, 2013 • Ann Arbor, Michigan
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.
•
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9
Hypothesized Active Site Loss Model
q min(q Li , q sites ).
q Li b0 b1t z b2 N
c2 c2,ref exp
q sites c0 c2 N
QLi = b0 + b1 t1/2 + b2 N
•Data shown above: J.C. Hall, IECEC, 2006.
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5
Life model framework: Graphite/NCA example
A. Resistance growth during storage Broussely (Saft), 2007: • T = 20°C, 40°C, 60°C • SOC = 50%, 100% B. Resistance growth during cycling Hall (Boeing), 2005-2006: • DoD = 20%, 40%, 60%, 80% • End-of-charge voltage = 3.9, 4.0, 4.1 V • Cycles/day = 1, 4 C. Capacity fade during storage Smart (NASA-JPL), 2009 • T = 0°C, 10°C, 23°C, 40°C, 55°C Broussely (Saft), 2001 • V = 3.6V, 4.1V D. Capacity fade during cycling Hall (Boeing), 2005-2006: (see above) NCA