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A key focus in studying lithium-ion batteries (LiBs) is the estimation of their actual capacity. To this end, many algorithms rely on the relationship between the open-circuit voltage (OCV) and state of charge (SOC) or absolute state of discharge, q . This relationship can be influenced by factors such as temperature (in a reversible way) and battery degradation (in an irreversible way). Although several studies investigated variations in the OCV-SOC or OCV- q relationship due to temperature or cycle aging using lookup tables or analytical expressions with adjustment factors, a comprehensive analytical model that simultaneously incorporates both factors and defines its parameters remains absent. To address this gap, the present work extends an existing analytical OCV- q model to capture variations in the OCV- q relationship as a function of both battery temperature and cycling level. To this aim, a comprehensive experimental campaign was conducted on a LiB, characterizing its OCV curve across various temperatures and cycling levels. Finally, simulations validated the accuracy of the proposed OCV- q model, yielding a mean relative OCV error below 0.8 % across all tests. Furthermore, the model demonstrated the ability to estimate the actual battery capacity with an estimation error of less than 2.5 % in all cases. • Cycle aging and temperature effects on lithium-ion batteries. • Open-circuit voltage model using a double exponential function. • Battery capacity estimation under varying cycling and temperatures. • Achieving a mean relative open circuit voltage error below 0.6 %. • Estimating the actual battery capacity with an estimation error of less than 2 %.
Barcellona et al. (Tue,) studied this question.