Optimization of Current Excitation for Identification of Battery Electrochemical Parameters based on Analytic Sensitivity Expression

The quality of data plays an important role in determining the accuracy of battery parameter identification/estimation, and hence data optimization for estimation has received increasing attention lately. The main idea is to design input excitation that is most sensitive to the target parameter(s) under estimation. However, most existing studies are hampered by the complexity of sensitivity computation/optimization, and need to impose heuristic patterns on the input to facilitate the optimization. Consequently, they are not capable of finding the ultimate optimal input pattern, and explaining the underlying mechanism. This is especially the case for the parameters of the first-principle electrochemical battery model. This paper aims at performing direct optimization of input excitation with no imposed pattern to find the ultimate optimal profile for estimating battery electrochemical parameters. The practice is enabled by the analytic expressions of sensitivity derived in our previous work. Based on the optimization results, we will explore the features/patterns of optimal profiles for different parameters by correlating to the analytic sensitivity expressions.