A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring

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Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter (2017, methodology): “Polynomial OCV(x) = p1x + p0 [15] OCV(x) = pnx + pn−1x + · · ·+ p1x + p0 [34,39] Logarithmic OCV(x) = p0 + p1 ln( x 1−x )− p2x [35] Exponential OCV(x) = p1e1 + p0 [40] OCV(x) = p0 + p1x + p2(1 − e−∝1x) + p3(1 − e −∝2 1−x ) [41] Combined OCV(x) = p0 + p1 1 1+e∝1(x−β1) + p2 1 1+e∝2(x−β2) + p3 1 1+e∝3(”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “The distance between two peaks (DBTP) [57] is also extracted and used for SOH estimation.”
A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery (2016, methodology): “Reference [17] proposed an OCV model structure in which simplified hyperbolic and exponential functions are used to represent phenomenological characteristics associated with the lithium-ion intercalation/deintercalation process.”