Parameter sensitivity analysis of a physico-chemical lithium-ion battery model with combined discharge voltage and electrochemical impedance data
By Wimarshana, Buddhi; Bin-Mat-Arishad, Izzuan; Fly, Ashley
Published in Journal of Power Sources
2022
Abstract
Physico-chemical battery models are capable of better simulation of lithium-ion cell behaviour due to their more physically descriptive modelling approach using many physical parameters. Accuracy of simulations highly depend on the correct identification of parameters. However, large parameter estimation uncertainties and model complexities increase the model parameterization difficulties. Therefore, pre-analysis of parameter sensitivities to model outputs can greatly help determining the best conditions and techniques for identifying many parameters with better accuracy. This work demonstrates a next step in improving the parameter identification accuracy by combined parameter sensitivity analysis of both discharge voltage and EIS data. A commercially available NMC-graphite based lithium-ion cell is modelled and 26 physical parameters are analysed for their sensitivity. For discharge voltage, capacity-related and non-capacity-related parameters clearly show different sensitivity patterns at different C-rates and depth of discharge regions. Moreover, non-dimensional EIS spectra states are introduced for correct identification of impedance-based parameter sensitivities and accurate parameter sensitivities are observed matching the frequency bands truly affected by the parameter kinetics. The ranked analysis of the parameter sensitivities clearly demonstrate the ability to identify many discharge voltage insensitive parameters using impedance measurements, which would greatly increase the number of identifiable parameters of a physico-chemical battery model.