Battery Degradation Modeling
Understanding calendar and cycle degradation for energy storage assets and how operating strategies impact lifespan.
Technical Overview
Proper assessment of battery degradation modeling is critical for bankability and project finance. The OPTIMUS engine incorporates detailed physical models to evaluate the long-term impacts of operation.
Asset Health: Capacity Retention Over Time
Key Modeling Factors
- •State of Charge (SOC) resting
- •Depth of Discharge (DOD)
- •C-rate (charge/discharge speed)
- •Temperature
Understanding Battery Degradation for Energy Storage Assets
Battery degradation directly impacts the financial performance of utility-scale BESS over a 15–20 year project life. Capacity fade reduces energy throughput; resistance growth increases round-trip losses. Both calendar aging (time at elevated temperature and state-of-charge) and cycle aging (depth and frequency of charge-discharge cycles) contribute to degradation. Accurate modeling is essential for project finance, lender due diligence, and operational optimization.
The OPTIMUS platform incorporates physics-based and empirical degradation models to project state-of-health (SOH) under real-world dispatch profiles.
State of Charge (SOC) Resting and Calendar Aging
Lithium-ion cells degrade faster when held at high SOC (e.g., 90–100%) or low SOC (e.g., 0–10%) for extended periods. Calendar aging is driven by time, temperature, and SOC. For BESS that may sit idle during low-price periods, resting SOC management becomes critical.
OPTIMUS models calendar aging as a function of temperature and average resting SOC. The dispatch engine can optimize for degradation by avoiding prolonged high-SOC or low-SOC states when market conditions allow. This extends useful life and reduces augmentation costs.
Depth of Discharge (DOD) and Cycle Aging
Deep discharge cycles (high DOD) accelerate capacity fade more than shallow cycles. A battery cycled to 90% DOD daily will degrade faster than one cycled to 50% DOD. However, constraining DOD reduces revenue capture. The optimal trade-off depends on price volatility, degradation cost, and augmentation strategy.
OPTIMUS uses rainflow-counting algorithms to quantify cycle depth distribution from dispatch simulations. The platform models the nonlinear relationship between DOD and capacity fade, enabling developers to evaluate dispatch strategies that balance revenue and degradation.
C-Rate and Temperature Effects
High C-rates (fast charge/discharge) increase internal resistance and can accelerate degradation. Temperature extremes—both high and low—affect both calendar and cycle aging. Hot climates require robust cooling; cold climates may limit power capability.
OPTIMUS incorporates C-rate and temperature dependencies into its degradation model. The platform simulates auxiliary load profiles (HVAC) and their impact on net round-trip efficiency, ensuring degradation projections reflect real-world operating conditions.