A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical-thermal battery aging model

High-fidelity physics-based models are required to comprehend battery behavior at various operating condi-tions. This paper proposes an uncertainty quantification analysis on a coupled electrochemical-thermal aging model to improve the reliability of a battery model, while also investigating the impact of parametric model uncertainties on battery voltage, temperature, and aging. The coupled model's high computing cost, however, is a significant barrier to perform uncertainty quantification (UQ) and sensitivity analysis (SA). To address this problem, a surrogate model - i.e, by simulating the outcome of a quantity of interest that cannot be easily computed or measured - based on the Gaussian process regression (GPR) theory and principle component analysis (PCA) is built, using a small collection of finite element simulation results as synthetic training data. In total, 43 variable electrochemical-thermal parameters as well as 13 variable aging parameters are studied and estimated. Moreover, the trained surrogate model is also used in the parameterization of the electrochemical and thermal models. The results show that the uncertainties in the input parameters significantly affect the estimations of battery voltage, temperature, and aging. Based on this sensitivity analysis, the most influential parameters affecting the above mentioned battery outputs are reported. This approach is thereby helpful for developing robust and reliable high-fidelity battery aging models with potential applications in digital twins as well as for synthetic data generation.

Automated summary

This paper proposes an uncertainty quantification analysis on a coupled electrochemical-thermal aging model to improve the reliability of a battery model, while also investigating the impact of parametric model uncertainties on battery voltage, temperature, and aging. A surrogate model based on Gaussian process regression (GPR) and principle component analysis (PCA) is built to address the high computing cost of the coupled model. The results show that the uncertainties in the input parameters significantly affect the estimations of battery voltage, temperature, and aging, and this approach is helpful for developing robust and reliable high-fidelity battery aging models.