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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 ...

Abstract-Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3 % RMSE.

Post-mortem MRI of the brain is increasingly applied in neuroscience for a better understanding of the contrast mechanisms of disease induced tissue changes. However, the influence of chemical processes caused by formalin fixation and differences in temperature may hamper the comparability with results from in vivo MRI. In this study we investigated how formalin fixation and temperature affect T1, T2 and T2* relaxation times of brain tissue. Fixation effects were examined with respect to changes in water content and crosslinking. Relaxometry was performed in brain slices from five deceased subjects at different temperatures. All measurements were repeated after 190 days of formaldehyde immersion. The water content of unfixed and fixed tissue was determined using the wet-to-dry ratio following drying. Protein weight was determined with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Fixation caused a strong decrease of all relaxation times, the strongest effect being seen on T1, with a reduction of up to 76%. The temperature coefficient of T1 was lower in the fixed than unfixed tissue, which was in contrast to T2, where an increase of the temperature coefficient was observed following fixation. The reduction of the water content after fixation was in the range of 1-6% and thus not sufficient to explain the changes in relaxation time. Results from SDS-PAGE indicated a strong increase of the protein size above 260 kDa in all brain structures examined. Our results suggest that crosslinking induced changes of the macromolecular matrix are responsible for T1 shortening and a decreased temperature dependency. The relaxation times provided in this work should allow optimization of post-mortem MRI protocols for the brain.