Abstract
Mechanical stress during the cycling process notably impacts the performance of lithium-ion batteries (LIBs), making it crucial to accurately monitor stress generation and propagation during battery operation. Traditional electrochemical-mechanical models are limited to the particle and electrode scales, and their parameter identification relies solely on voltage. Here, a multi-scale electrochemical-mechanical-thermal modelling framework with non-destructive parameter identification capabilities is proposed. This numerical model couples electrochemical reactions with thermal effects and links particle-scale strain to electrode-scale displacement. Diffusion-induced stress (DIS) is selected as a key indicator, combined with voltage, to analyze the sensitivity of 23 parameters. A voltage-strain multi-objective parameter identification strategy based on the Pareto front is employed to determine the key parameters. The framework demonstrates high fidelity, with the mean absolute percentage error for voltage and strain predictions below 1% and 3.6%, respectively. This work enables high-fidelity simulation of multi-physics behavior, provides an effective method for calibrating key parameters, and holds potential for establishing a reliable digital twin of LIBs.