Global Parameter Sensitivity Analysis of Electrochemical Model for Lithium-Ion Batteries Considering Aging

Accurately identifying the aging-related parameters of a lithium-ion electrochemical model is crucial for the advanced battery management systems over the cells' service life. However, the multiparametric and highly nonlinear mathematical structures of the physical model heighten the difficulty for parameterization. Thus, analyzing the influence of degraded parameters on model output holds the key to efficient identification. In this article, a statistics-based global sensitivity analysis of overall 16 aging parameters in the pseudo-2-D model of lithium-ion batteries is investigated under both the charge process and dynamic driving cycles at 10 degrees, 25 degrees, and 45 degrees. First, massive samples of the parameters are generated synchronously with the Latin hypercubes method. Then the model is simulated with the Monte Carlo technique. Finally, the sensitivity of each parameter is ranked with the partial correlation coefficient which quantifies the relation between the parameter variations and the voltage residuals. The results turn out that the sensitivity of each parameter varies at different operating conditions. Specifically, the resistance-related parameters are the most sensitive than capacity and diffusion-related parameters. To validate the effectiveness of the proposed approach, 16 aging parameters are clustered for identification. The identified model achieves low voltage root-mean-square errors across 170 cycles at three temperatures.

Automated summary

The study found that the sensitivity of each parameter varies at different operating conditions, with resistance-related parameters being more sensitive than capacity and diffusion-related parameters. By clustering the 16 aging parameters, the identified model achieved low voltage root-mean-square errors.