锂离子电池的可靠性和循环寿命模型

Outline
Part 1: Battery Life Modeling • Life Model Framework • NCA Model • FeP Model Part 2: Life Model Application • Life-Cycle Analyses • Real-Time Health Management
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)
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
QLi = b0 + b1 t1/2 + b2 N
•Data shown above: J.C. Hall, IECEC, 2006.
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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
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)
NCA
PHEV10 Phoenix
Select model with best statistics
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Knee in curve important for predicting end of life
(Hypothesis based on observations from data) Example simulation: 1 cycle/day at 25°C
Battery prognostic and electrochemical control (ARPA-E AMPED program)
Advanced battery management R&D with industry & university partners
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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
*Source: Marc Isaacson, Lockheed Martin
End User Goals: • Understand reliability and economics of new technologies
(e.g., electric-drive vehicles vs. conventional vehicles)
Models for Battery Reliability and Lifetime
Applications in Design and Health Management
Kandler Smith Jeremy Neubauer Eric Wood Myungsoo Jun Ahmad Pesaran Center for Transportation Technologies and Systems National Renewable Energy Laboratory
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
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)
•
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Hypothesized Active Site Loss Model
q min(q Li , q sites Biblioteka Baidu.
q Li b0 b1t z b2 N
c2 c2,ref exp
q sites c0 c2 N
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NREL Life Predictive Model
Calendar fade
• SEI growth (possibly coupled with cycling) • Loss of cyclable lithium • a1, b1 = f(∆DOD,T,V)
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
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.
Relative Capacity (%)
Life predictive modeling and battery system tradeoff studies
Aging model
Liquid cooling Air cooling
Air cooling, low resistance ce
Computer-aided engineering of batteries (CAEBAT program)
Capacity fade with “knee” region highlighted
A123 ANR-26650-M1
• • LixC6/LiyFePO4 2.3 Ah, 3.3Vnominal
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FeP
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Active site loss controlled mainly by mechanical-driven cycling fade
NCA
Life over-predicted by 25% without “knee”
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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)
•
Manage assets for maximum utilization
(e.g. route scheduling, charge control to optimize EV fleet life and cost)
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NREL Research & Development Addressing Life scenario Battery Lifetime analysis
Better life prediction methods, models and management are essential to accelerate commercial deployment of Liion batteries in large-scale high-investment applications
bulk intercalation strain
bulk thermal strain
intercalation gradient strain, accelerated by low temperature
Purple symbols are global rate-law model across all aging conditions