====================================================================== LFP vs. NMC Degradation Curve Database (CSV) Downloadable Resource (CSV) ====================================================================== DEFINITION ---------------------------------------- The LFP vs. NMC Degradation Curve Database (CSV) is a structured, experimentally validated dataset containing time- and cycle-resolved capacity retention, resistance growth, and voltage fade metrics for lithium iron phosphate (LFP) and nickel-manganese-cobalt oxide (NMC) lithium-ion battery chemistries under standardized aging conditions. It enables quantitative comparison of degradation kinetics, mechanisms, and lifetime performance between the two chemistries. The CSV format ensures interoperability with data analysis, battery modeling, and BMS calibration tools. OVERVIEW ---------------------------------------- This database consolidates accelerated aging test results—typically spanning thousands of cycles and multiple temperature/stress conditions—from peer-reviewed studies and industry validation campaigns (e.g., USABC, CALCE, and EU Battery 2030+ initiatives). Each row represents a unique aging condition (e.g., 45°C, 1C charge/discharge, 80% DoD) and includes time-series or endpoint metrics such as normalized capacity (Ah), DC internal resistance (mΩ), voltage hysteresis, and differential voltage (dV/dQ) features. Degradation curves are derived from galvanostatic cycling data fitted to empirical or physics-informed models (e.g., power-law, exponential, or SEI-growth-based equations), allowing interpolation and extrapolation of lifetime behavior. The database supports techno-economic analysis by linking degradation rates to calendar/cycle life, energy throughput (MWh/ton), and end-of-life (EoL) definitions (e.g., 80% capacity retention). Crucially, it highlights fundamental differences: LFP exhibits slower capacity fade but higher resistance growth at low SoC extremes, while NMC shows steeper capacity loss above 4.2 V and pronounced transition-metal dissolution—both reflected in curve shape, inflection points, and stress-dependence. KEY COMPONENTS ---------------------------------------- 1. Chemistry-specific degradation trajectories (capacity retention % vs. cycles/time) 2. Aging condition metadata (temperature, C-rate, DoD, voltage window, rest periods) 3. Electrochemical signature metrics (IR increase, dQ/dV peak shifts, coulombic efficiency decay) APPLICATIONS ---------------------------------------- - Battery energy storage system (BESS) sizing and warranty period estimation - Physics-based battery model parameterization (e.g., PyBaMM, COMSOL) - BMS algorithm development for state-of-health (SoH) estimation and adaptive charging control KEY FORMULAS ---------------------------------------- Capacity Fade Rate α = (1 − Q(t)/Q₀) / t -> Average fractional capacity loss per unit time (e.g., %/month), where Q(t) is capacity at time t and Q₀ is initial capacity. Cycle-Life Power Law Model Q(N) = Q₀ · (1 − k · N^m) -> Empirical fit for capacity vs. cycle count N; k is fade coefficient, m is degradation exponent (typically 0.5–1.0 for LFP, 0.7–1.2 for NMC). Arrhenius Aging Acceleration k(T) = k₀ · exp(−Eₐ/(R·T)) -> Temperature-dependent fade rate coefficient k(T), where Eₐ is activation energy (kJ/mol), R is gas constant, and T is absolute temperature (K). RELATED CONCEPTS ---------------------------------------- - State of Health (SoH) estimation - SEI growth kinetics - Transition metal dissolution (NMC) - Olivine structure stability (LFP) - Accelerated aging protocols REFERENCES ---------------------------------------- - CALCE Battery Degradation Database https://www.calce.umd.edu/battery/battery-database.htm - EU Battery 2030+ Degradation Benchmarking Framework https://battery2030.eu/wp-content/uploads/2022/09/Battery2030_Development_Framework_v2.pdf - USABC Electric Vehicle Battery Test Procedures Manual (Rev 5) https://www.uscar.org/files/USCAR/USCAR%20Documents/USABC%20EV%20Battery%20Test%20Procedures%20Manual%20Rev%205.pdf TAGS ---------------------------------------- battery-degradation, LFP, NMC, BESS-design, SoH-data