LAUSR

Abstract The pseudo-2-dimensional (P2D) model parameters of Li-ion batteries are important indicators of their properties, characteristics, and conditions. For safe operation, it is essential to discover the effective P2D model parameters closely related to the degradation state of the battery. This paper proposes a practical method for identifying and selecting effective P2D model parameters that significantly change with battery aging. As per the proposed procedure, the aging parameters distinctly correlated with the battery degradation states are selected, and effectively and reliably identified through experiments with multiple profiles, such as constant current–constant voltage (CCCV) chirp sequences, hybrid pulse power characterization (HPPC) cycles, and driving cycles. To identify the optimal parameters, the obtained experimental data are fitted to the P2D model using the genetic algorithm. The mean error between the experimental data and the output voltages of the P2D model with the identified parameters is 18.79 mV, showing high accuracy. Certain parameters that converge to distinctly different values at the beginning-of-life (BOL) and end-of-life (EOL) of a battery are selected as the aging parameters. Consequently, the cathode particle surface area, stoichiometry limits, and porosities were selected as aging parameters, and it is demonstrated that those parameters shift with aging.