Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium-ion Batteries, Part-1: Performance Measures

Open-circuit voltage (OCV) modeling is a foundational component of battery management systems (BMS), serving as the basis for state-of-charge (SOC) estimation and overall system reliability. While empirical OCV–SOC curves are widely used in practice, their accuracy is often assumed rather than scrutinized. This paper revisits the OCV–SOC characterization process to systematically identify and quantify sources of uncertainty that propagate into SOC estimation error. We analyze how assumptions typically treated as trivial, such as, voltage cutoffs, discharge rates, temperature conditions, and capacity definitions, introduce variability into the empirical OCV–SOC model. Six major sources of uncertainty are modeled as SOC-dependent error terms: cell-to-cell variation, temperature dependence, aging effects, high C-rate distortion, curve-fitting residuals, and voltage measurement noise. We then derive an analytical framework that links these uncertainties to SOC estimation error via a unified OCV uncertainty coefficient. This formulation enables the derivation of SOC confidence intervals and provides practical guidance for selecting characterization parameters to minimize downstream estimation error. Unlike prior OCV–SOC studies that mainly focus on fitting accuracy, this work treats the empirical OCV–SOC curve as an uncertain quantity and derives how its uncertainty propagates into SOC estimation error. This work lays the theoretical foundation for uncertainty-aware OCV modeling, which is experimentally validated in Part II [1] and Part III [2] of this three-part study.