Models for Battery Reliability and Lifetime Applications in Design - PowerPoint PPT Presentation

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 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.

Better life prediction methods, models and management are essential to accelerate commercial deployment of Li- ion 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 *Source: Marc Isaacson, Lockheed Martin End User Goals: • Understand reliability and economics of new technologies (e.g., electric-drive vehicles vs. conventional vehicles) • Manage assets for maximum utilization (e.g. route scheduling, charge control to optimize EV fleet life and cost) 2 NATIONAL RENEWABLE ENERGY LABORATORY

NREL Research & Development Addressing Battery Lifetime Life scenario analysis Aging Life predictive modeling and battery model Capacity Relative system tradeoff studies (%) Liquid cooling Air cooling, low resistance ce Air cooling Computer-aided engineering of No cooling 3D Multi- Phoenix, AZ ambient conditions batteries (CAEBAT program) physics simulation Online & offline health Battery health estimation & management tracking of real-world applications (Laboratory-Directed R&D program) Battery prognostic and electrochemical control (ARPA-E AMPED program) Advanced battery management R&D with industry & university partners 3 NATIONAL RENEWABLE ENERGY LABORATORY

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 4 NATIONAL RENEWABLE ENERGY LABORATORY

NREL Life Predictive Model NCA Relative Capacity (%) Calendar fade Cycling fade r 2 = 0.942 • SEI growth (possibly • Active material structure coupled with cycling) degradation and • Loss of cyclable lithium mechanical fracture • a 1 , b 1 = f( ∆ DOD,T,V) • a 2 , c 2 = f( ∆ DOD,T,V) Time (years) R = a 1 t 1/2 + a 2 N Relative Li-ion NCA chemistry Arrhenius-Tafel-Wohler model Resistance describing a 2 ( ∆ DOD,T, V) Relative Growth (m Ω ) Resistance Q = min ( Q Li , Q sites ) Capacity Q Li = b 0 + b 1 t 1/2 + b 2 N Q Li = b 0 + b 1 t 1/2 + b 2 N Q sites = c 0 + c 2 N Q sites = c 0 + c 2 N •Data shown above: J.C. Hall, IECEC, 2006. 5 NATIONAL RENEWABLE ENERGY LABORATORY

Life model framework: Graphite/NCA example Regression Data A. Resistance growth during storage Broussely (Saft), 2007: 1. Fit local model(s) • T = 20°C, 40°C, 60°C Relat Capa city ive (%) • SOC = 50%, 100% 2. Visualize rate-dependence on B. Resistance growth during cycling operating condition Hall (Boeing), 2005-2006: • DoD = 20%, 40%, 60%, 80% 3. Hypothesize rate-law(s) • End-of-charge voltage = 3.9, 4.0, 4.1 V • Cycles/day = 1, 4 C. Capacity fade during storage             exp  V E 1 1 F V ( t )  Smart (NASA-JPL), 2009 DoD             ref exp  a   oc          T V  • T = 0°C, 10°C, 23°C, 40°C, 55°C DoD R T ( t ) T R T ( t ) T      DoD           ref ref ref Broussely (Saft), 2001 • V = 3.6V, 4.1V 4. Fit rate-laws(s) D. Capacity fade during cycling Hall (Boeing), 2005-2006: (see above) 5. Fit global model(s) NCA Predictive model Select model with best statistics NCA PHEV10 Phoenix 6 NATIONAL RENEWABLE ENERGY LABORATORY

Knee in curve important for predicting end of life (Hypothesis based on observations from data) Example simulation: 1 cycle/day at 25°C 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 Life over-predicted by 25% without “knee” 7 NATIONAL RENEWABLE ENERGY LABORATORY

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) Capacity fade with “knee” region highlighted FeP A123 ANR-26650-M1 • Li x C 6 /Li y FePO 4 • 2.3 Ah, 3.3V nominal 8 NATIONAL RENEWABLE ENERGY LABORATORY

Active site loss controlled mainly by mechanical-driven cycling fade 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) 9 NATIONAL RENEWABLE ENERGY LABORATORY

Hypothesized Active Site Loss Model q  min( q Li q , ). sites  0     z q sites c c N q b b t b N 2 Li 0 1 2                  binder  intercal. t  E     E  C c c exp 1 1 m DOD m T m exp 1 1   .  pulse  a a rate 2 2 , ref 1 2 3   R T T R T T C t   ref ref rate , ref pulse , ref bulk bulk accelerated binder intercalation gradient strain, accelerated intercalation thermal failure at high T by low temperature strain strain FeP Blue symbols are site- loss rates for each Site loss/cycle, log( c 2 ) individual aging condition Purple symbols are global rate-law model across all aging conditions DOD NATIONAL RENEWABLE ENERGY LABORATORY 10

FeP model comparison with knee data Global model compared with 13 aging conditions from 0°C to 60°C FeP Active site loss ( at room temperature, 1C charge/discharge, 100% DOD reference conditions ) • 83% due to bulk volumetric expansion/contraction of the active material* • 13% due to particle fracture owing to intercalation stress at high C-rates • 4% due to temperature swings encountered by the cell * This dominant aging term correlates with Amp-hour throughput, often used as a proxy for aging 11 NATIONAL RENEWABLE ENERGY LABORATORY

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 12 NATIONAL RENEWABLE ENERGY LABORATORY

Automotive Analyses: Battery Ownership Model Objective: Identify cost- effective pathways to reduce petroleum use and carbon footprint via optimal use of vehicular energy storage systems Approach: – Trip-by-trip simulation of hundreds of real-world, year-long, vehicle-specific drive patterns in real climates – Model driver behavior, road loads, auxiliary loads, vehicle cabin thermal Life response, and battery electrical, thermal, and life Model response 13 NATIONAL RENEWABLE ENERGY LABORATORY

Automotive Analyses: Battery Ownership Model Phoenix, AZ Phoenix, AZ Los Angeles, CA A recent study of climate, trip history, and driver aggression shows Los Angeles, CA Minneapolis, MN how these factor affect Minneapolis, MN battery state of health after 10 years in a BEV75 - 317 different real-world trip histories Los Angeles, CA Phoenix, AZ Phoenix, AZ - 3 different driver aggression levels Minneapolis, MN - 3 different climates Los Angeles, CA - Findings: Climate has Minneapolis, MN the largest effect on battery wear, followed by trip history 14 NATIONAL RENEWABLE ENERGY LABORATORY

Battery Second-Use Analyses Life Model Battery state of health is critical to determining the 100% Fraction of Drive Patterns BEV75 technical capability and 80% PHEV35 performance of a second- 60% use battery 40% 20% 0% Our second-use analyses 0% 9% 18% 27% 34% 41% incorporate the life model (k H ) Second Use Health Factor to calculate a health factor that becomes a major determinant in second-use Second-Use feasibility Battery Selling Price = k U k H c N 15 NATIONAL RENEWABLE ENERGY LABORATORY

Grid Analyses: Community Energy Storage Analyzed the long-term effects of two different community energy storage system configurations in a Capacity (%) real-world climate – “Tomb” configuration : insulated from ambient temperature and solar irradiation, strong connection to soil temperature. – “Greenhouse” configuration: Strong connection to ambient temperature, large effect of irradiation. Resistance (%) – Duty Cycle: Daily 60% DOD peak- shaving event – Climate: Los Angeles, CA – Findings: The difference in long-term wear between the two system configurations is small for this Time (days) combination of climate and duty cycle 16 NATIONAL RENEWABLE ENERGY LABORATORY