UC Davis

Estimation of model parameters is a critical topic in battery modeling research, as the accuracy of parameters determines the efficacy of the widely used model-based battery engineering. The measurement-based approaches aim at measuring the battery physical parameters using advanced experiment and instrumentation techniques, but the associated complexity, time, and cost make it undesirable in many cases. Therefore, the data-based identification approach, which only uses easily available input and output measurement data, has been widely adopted due to its convenience and noninvasiveness. As the quality of data has significant impact on the estimation accuracy, data optimization, or optimal experiment design, is often utilized to improve and guarantee the accuracy of estimation. The common practice of data optimization aims at designing input excitation by maximizing a certain conventional criterion, e.g. Fisher information which measures the information content of the data and relates to the variance of the estimation error. However, such approach suffers from fundamental limitations, including inability to explicitly address estimation bias and system uncertainties in measurement, model, and parameter, which severely restrict the applicability and effectiveness of the method in practice.

To overcome the existing limitations, new criteria and a novel framework are proposed in this research for estimation error quantification and data optimization. A generic formula is first derived for quantifying the estimation error subject to sensor, model, and parameter uncertainties for the commonly used least-squares algorithm. Based on the formula, data structures, represented in terms of parameter sensitivity, which could minimize the estimation errors caused by each type of uncertainty are identified. These data structures are then employed as new criteria to supplement the Fisher information and formulate a novel data optimization framework. In order to facilitate the solution of the formulated data optimization problem, this research also explores new methods for efficient computation of parameter sensitivity, which is a key for representing the data structures and enabling data optimization. Efforts have been made to derive the analytic expressions of the sensitivity of battery electrochemical parameters by leveraging reasonable assumptions and model reformulation and simplification techniques. The proposed methodology is applied to estimating the electrochemical parameters of a single particle lithium-ion battery model in simulation and experiments, showing excellent estimation and voltage prediction accuracy compared with the traditional approach.