Sensitivity Analysis and Joint Estimation of Parameters and States for All-Solid-State Batteries

All-solid-state batteries (ASSBs) are considered to be the next generation of lithium-ion batteries. Physics-based models (PBMs) can effectively simulate the internal electrochemical reactions and provide critical internal states for battery management. In order to promote the onboard applications of PBMs for ASSBs, in this article, the parameter sensitivity of a typical PBM is analyzed, and a joint estimation method for states and parameters based on sigma-point Kalman filtering (SPKF) is proposed. First, to obtain accurate sensitivity analysis results, approaches from different principles, including local sensitivity, elementary effect test, and variance-based methods, are applied. Then, for the battery model based on partial differential equations, a nonlinear state-space model is constructed by using the finite-difference discretization method. Finally, the SPKF algorithm is employed to conduct the joint estimation of model parameters and lithium-ion concentrations. The results from constant current and dynamic cycles show that two parameters, namely maximum lithium-ion concentration and minimum lithium-ion concentration, have the most influence on the model results. The joint estimation method is validated in three different cases, and the mean absolute errors of the estimated voltage and state of charge (SOC) are below 2.1 mV and 1.5%, respectively.

Automated summary

All-solid-state batteries are considered the next generation of lithium-ion batteries, and physics-based models can effectively simulate internal electrochemical reactions for battery management. A joint estimation method based on sigma-point Kalman filtering is proposed to analyze parameter sensitivity, showing that two parameters have the most influence on model results. Validation of the method in three different cases resulted in mean absolute errors below 2.1 mV and 1.5% for estimated voltage and state of charge.