**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

Publication# On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Abstract

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model different frequency regions of the Li-ion cell’s impedance spectrum. We derive a noise covariance matrix from the measurement model and use it to assign weights for the fitting technique. The paper presents two formulations of the parameters identification problem. Using the properties of the ECM EIS spectra, we propose a method to initialize ECM parameters for the Complex Non-linear Least Squares (CNLS) technique. The paper proposes a novel algorithm for designing the EIS experiments by applying the theory on Cramér-Rao Lower Bound (CRLB) and Fisher Information Matrix (FIM) to the identification problem. We show that contributions to the FIM elements strongly depend on the frequencies at which EIS is performed. Hence, the algorithm aims to adjust frequencies such that the most information about parameters is collected. This is done by minimizing the highest variance of ECM parameters defined by CRLB. Results of a numerical experiment show that the estimator is efficient, and frequency adjustment leads to more accurate ECM parameters’ identification.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (32)

Related MOOCs (9)

Related publications (60)

Ontological neighbourhood

Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements.

Parameter

A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when identifying the system, or when evaluating its performance, status, condition, etc. Parameter has more specific meanings within various disciplines, including mathematics, computer programming, engineering, statistics, logic, linguistics, and electronic musical composition.

Fisher information

In mathematical statistics, the Fisher information (sometimes simply called information) is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of the score, or the expected value of the observed information. The role of the Fisher information in the asymptotic theory of maximum-likelihood estimation was emphasized by the statistician Ronald Fisher (following some initial results by Francis Ysidro Edgeworth).

Electrical Engineering I

Découvrez les circuits électriques linéaires. Apprenez à les maîtriser et à les résoudre, dans un premier temps en régime continu puis en régime alternatif.

Electrical Engineering I

Découvrez les circuits électriques linéaires. Apprenez à les maîtriser et à les résoudre, dans un premier temps en régime continu puis en régime alternatif.

Digital Signal Processing I

Basic signal processing concepts, Fourier analysis and filters. This module can
be used as a starting point or a basic refresher in elementary DSP

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

Many engineering fields rely on frequency-domain dynamical systems for the mathematical modeling of physical (electrical/mechanical/etc.) structures. With the growing need for more accurate and reliable results, the computational burden incurred by frequen ...

, , ,

Since the 70s, there has been a growing interest in activity-based modelling. This approach models the need to travel as a result of performing daily activities (Bowman and Ben-Akiva, 2001). Nevertheless, the activities need to be scheduled which involves ...

2022