Analytical Sensitivity Analysis for Battery Electrochemical Parameters

Recognizing the important role of data in estimation, data optimization for offline and online battery state and parameter estimation has been receiving increasing attention recently. The main idea is to design/select data that are most sensitive to the target variables under estimation. However, due to the lack of efficient ways to compute sensitivity and heavy reliance on simulation, data optimization is often intractable because of the computational complexity. This issue is especially prominent for the states and parameters of the electrochemical battery models. In this work, we study the methodology for analytically deriving the sensitivity of battery electrochemical parameters. The derivation is based on a single particle model, and the results have been verified by comparing to the numerical simulation of a full order pseudo-2D electrochemical model. The obtained analytic results provide theoretic insight on the dynamic nature of the parameter sensitivity. The derived analytic expressions could enable fast sensitivity computation to serve the data optimization for offline system identification and online data selection/mining for real-time estimation.