Uncertainty-Aware Data Selection Framework for Parameter Estimation with Application to Li-ion Battery

Fogelquist, Jackson; Lin, Xinfan

doi:10.23919/acc53348.2022.9867243

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On this front, we have been trying to leverage the insights from error analysis to select the best data segments from an existing database or online data stream for estimating the battery health-related parameters. 32,33 Additionally, a properly parameterized model can be used for prognostics and predictive control by leveraging the capability of the model (with estimated parameters) to predict future battery performance-a necessary feature for advanced battery management. On this front, we have been exploring the use of the favorable data structures identified from the error equation to formulate objectives for input excitation design, to improve the model parameterization accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…This insight is corroborated by several works that have improved estimation accuracy through strategic data selection from a larger data set. 32,33,39 2. The term…”

Section: Derivation and Analysis Of Estimation Errormentioning

confidence: 99%

“…31 We sought to address these limitations in a prior work through the derivation of a univariate estimation error equation for the least-squares objective, which directly predicts the estimation error through consideration of uncertainties in model, measurement, and parameter. 8 This error equation was subsequently leveraged to develop criteria for data optimization in univariate estimation scenarios, i.e., cost functions for input excitation design 8 and criteria for data selection, 32,33 which yielded promising results and outperformed the conventional Fisher information-based criteria.…”

mentioning

confidence: 99%

See 2 more Smart Citations

On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

Fogelquist

Lai

Lin

2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

Emerging lithium-ion battery systems require high-fidelity electrochemical models for advanced control, diagnostics, and design. Accordingly, battery parameter estimation is an active research domain where novel algorithms are being developed to calibrate complex models from input-output data. Amidst these efforts, little focus has been placed on the fundamental mechanisms governing estimation accuracy, spurring the question, why is an estimate accurate or inaccurate? In response, we derive a generalized estimation error equation under the commonly adopted least-squares objective function, which reveals that the error can be represented as a combination of system uncertainties (i.e., in model, measurement, and parameter) and uncertainty-propagating sensitivity structures in the data. We then relate the error equation to conventional error analysis criteria, such as the Fisher information matrix, Cramér-Rao bound, and parameter sensitivity, to assess the benefits and limitations of each. The error equation is validated through several uni- and bivariate estimations of lithium-ion battery electrochemical parameters using experimental data. These results are also analyzed with the error equation to study the error compositions and parameter identifiability under different data. Finally, we show that adding target parameters to the estimation without increasing the amount of data intrinsically reduces the robustness of the results to system uncertainties.

show abstract

Section: Discussionmentioning

confidence: 99%

“…This insight is corroborated by several works that have improved estimation accuracy through strategic data selection from a larger data set. 32,33,39 2. The term…”

Section: Derivation and Analysis Of Estimation Errormentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

Fogelquist

Lai

Lin

2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…[ 160 ] Through an uncertainty‐aware data selection framework, the electrochemical parameters of a lithium‐ion battery are estimated with reduced errors. [ 161 ] Apart from utilizing the complementary strategy of uncertainty awareness, the influence factors associated with real‐world scenarios and demands should be assessed while determining the preference between accuracy and interpretability. For in situ laboratory modeling, explainability could be more essential considering the feasibility of consequent synthesis or optimization.…”

Section: Challenges and Prospectsmentioning

confidence: 99%

Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Ji,

Zhu,

Yang

et al. 2024

Small Methods

View full text Add to dashboard Cite

Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data‐driven artificial intelligence systems. This review provides a unique perspective on recent progress in data‐driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high‐throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics‐informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics‐informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data‐driven characterization and prognosis are discussed toward accelerating energy device development with much‐enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next‐generation battery development.

show abstract

“…Our approach is inspired by the computation of a fictional material in magnetostatics for which the sensitivity in the magnetic permeability and electrical current is minimal [6], and aims to compute a sample-dependent weight function such that the estimation of a calibration parameter of interest be as insensitive as possible to errors in a pre-estimated set of parameters. A sensitivity minimization strategy, based on data selection, has recently been implemented in the context of Li-ion batteries calibration [7]. A complementary approach, rebuilding missing points in incomplete data set with polynomials, has also been suggested [17].…”

Section: Introductionmentioning

confidence: 99%

Minimization of Parameter Sensitivity to Pre-Estimation Errors and its Application to the Calibration of Magnetometer Arrays

Neymann

Meier

Lhachemi

et al. 2023

2023 European Control Conference (ECC)

View full text Add to dashboard Cite

We consider the problem of parameters estimation in the situation where a subset of parameters of the underlying model has already been estimated. Potential errors in the pre-estimated parameters limit the accuracy in the computation of the still unknown parameters in a way that crucially depends on the symmetries of the data set. In this article, we develop an optimization approach that minimizes the sensitivity to errors in the determination of parameters by determining optimal weights that depend on both the model and the input data in the situation where the data are incomplete. We apply the method to the problem of calibrating the sensor positions in an array of magnetometers whose scale factors and biases have been estimated beforehand.

show abstract

Uncertainty-Aware Data Selection Framework for Parameter Estimation with Application to Li-ion Battery

Cited by 4 publications

References 22 publications

On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Minimization of Parameter Sensitivity to Pre-Estimation Errors and its Application to the Calibration of Magnetometer Arrays

Contact Info

Product

Resources

About