Advanced non-destructive methods for state-of-charge diagnostics of the industrial and pilot lithium coin cells

It was established one-, two and three-factor regression dependencies among discharge capacity С, short-circuit current I (SC), open circuit voltage U (OCV), internal resistance (Ri) and mass (m) for polynomials of a degree n(n = 1–4) in all sampling. The discharge capacity of ``fresh'' cell's forecasts a little bit better than after their long storage and magnitude of standard deviations for ``fresh'' CR2325 (2.5%) differ from ``old'' cells approximately twice (5.5%). For the predictive estimation of a discharge capacity an optimum variant is the three-dimensional regression on SC (Ri), OCV and weight of Li-cell. The optimum polynomial for the long-term storage cells with the expressed passivation phenomena looks as the function of relations of predicted capacity from SC, OCV and weight – C(I,m,U) = a0 + a1·I + a2·m + a3·U The offered method can be applied for deriving dependence of discharge capacity from an external load and for creation of accelerated tests technique of, including also the development of methods for nondestructive diagnostic of each made coin Li-cell's quality.


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