Peak-tracking method to quantify degradation modes in lithium-ion batteries via differential voltage and incremental capacity

Incremental capacity (IC) and differential voltage (DV) analyses are effective for monitoring battery health, however, the diagnosis often requires considerable parameterisation efforts and a low scan rate. In this work, a simple-to-parameterise quantitative diagnostic approach is presented, which differentiates between loss of lithium inventory and loss of active materials in the anode and cathode. With an open-circuit voltage model and a genetic algorithm optimisation routine, peak signatures in voltage and capacity differentials are used to quantify degradation modes as opposed to traditional approaches of matching the whole voltage and capacity spectra. The outputs are validated with synthetic IC-DV spectra and achieve a low root-mean-square error of ± 2.0 %. A similar level of accuracy is achieved when heterogeneity is introduced in the synthetic degradation data and also with partial discharge data. Experiments from pouch cells under 5 C discharge and 0.3 C charge cycling at 25 °C and 45 °C, together with post-mortem measurements, confirm the accuracy of this approach with diagnosis scan taken at 0.3 C. The IC-DV peak-tracking quantitative diagnostic code demonstrates a reliable and easy-to-implement means of extracting deeper insights into battery degradation and is shared alongside this manuscript to help academia and industry develop better lifetime predictions.