Capacity Forecasting of Lithium-Ion Batteries Using Empirical Models: Toward Efficient SOH Estimation with Limited Cycle Data

Accurate prediction of lithium-ion battery capacity degradation is essential for reliable state of health (SOH) estimation and long-term performance assessment in battery management systems. This study presents an empirical modeling approach for capacity degradation using experimental data collected from four lithium iron phosphate (LFP) battery packs over 75 to 100 charge–discharge cycles. Several mathematical models, including linear, quadratic, single-exponential, and double-exponential functions, were evaluated for their predictive accuracy. The linear and single-exponential models demonstrated superior robustness for early-cycle prediction, while a novel modified-linear model, incorporating an exponentially decaying slope, was proposed to capture nonlinear degradation trends beyond the initial aging phase. The study further investigates the minimum number of data points required for reliable long-term prediction and finds that approximately 30 to 40 cycles are sufficient to achieve high accuracy, with mean absolute error reduced by over 80% compared to early-cycle prediction alone. The modified-linear model outperforms traditional approaches in forecasting SOH and end-of-life (EOL), particularly under extended cycling conditions, providing a practical balance between computational simplicity and predictive fidelity.