We present an open circuit voltage (OCV) model for lithium ion (Li-ion) cells, which can be parameterized by measurements of the OCV of positive and negative electrode half-cells and a full cell. No prior knowledge of physical parameters related to particular cell chemistries is required. The OCV of the full cell is calculated from two electrode sub-models, which are comprised of additive terms that represent the phase transitions of the active electrode materials. The model structure is flexible and can be applied to any Li-ion cell chemistry. The model can account for temperature dependence and voltage hysteresis of the OCV. Fitting the model to OCV data recorded from a Li-ion cell at 0 • C, 10 • C, 20 • C, 30 • C and 40 • C yielded high accuracies with errors (RMS) of less than 5 mV. The model can be used to maintain the accuracy of dynamic Li-ion cell models in battery management systems by accounting for the effects of capacity fade on the OCV. Moreover, the model provides a means to separate the cell's OCV into its constituent electrode potentials, which allows the electrodes' capacities to be tracked separately over time, providing an insight into prevalent degradation mechanisms acting on the individual electrodes. The open circuit voltage (OCV) of lithium ion (Li-ion) cells plays a central role in battery models used in battery management systems (BMS) for a wide range of applications from consumer electronics to automotive systems. The OCV of a battery cell is the potential difference between the positive electrode (PE) and the negative electrode (NE) when no current flows and the electrode potentials are at equilibrium. A battery undergoing charge or discharge does not exhibit this potential since it is modified by kinetic effects such as mass transport. However, the OCV of a battery over the full range of states of charge can be obtained by charging or discharging the battery utilizing a galvanostatic intermittent titration technique (GITT) and measuring the potential at the end of each relaxation period (assuming the period is long enough to reach equilibrium). All dynamic battery models rely on the knowledge of the relationship between OCV and cell capacity in order to produce accurate estimates of internal battery states such as the state of charge (SOC) and the state of health (SOH). The SOC of a cell is a measure of how much charge remains within the cell relative to its maximal capacity. The SOC is generally expressed as a percentage; SOC = 100% indicates a fully charged cell and SOC = 0% a fully discharged cell. The SOH describes the performance deterioration of a cell at any point in time compared to the cell's initial performance. Two important metrics for the SOH are capacity fade and the increase in internal resistance, which relates to power fade. Capacity fade affects the OCV of a cell, which must be accounted for in dynamic cell models in a BMS, to ensure continued accuracy.Battery models typically incorporate the OCV as a function of cell capacity or SOC by storing measured OCV values ...
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