Characterizing degradation in lithium-ion batteries with pulsing

Abstract

Degradation in lithium-ion batteries is traditionally characterized with the pseudo open-circuit voltage (pOCV) or incremental capacity (IC) but these methods have hours-long diagnostics times and cannot easily measure impedance change. It is shown here that a pulse with amplitude 1 C-rate can perform both IC and impedance characterization in just 2 min. Pulses and C/20 pOCV from 6 lithium-ion cells at 1328 unique combinations of state of charge, state of health, and temperature are evaluated using the convolution-defined diffusion equivalent circuit model, ridge regression, and neural networks. Ridge regression of the IC extrema and the pulse harmonics predicts SoH and nominal SoC with less than 1% and 6% error, respectively. Individual contributions of the ohmic, charge transfer, and diffusion overpotentials, as well as open-circuit voltage or hysteresis, are quantified for the charge pulse. Neural networks reconstruct IC extrema from the pulse harmonics with less than 1% error. The pulse response therefore reflects internal kinetic parameters and electrode phase transitions which are best uncovered using neural networks. Our results extend the uses of pulsing and suggest novel methods for degradation diagnostics in battery management systems.