Electrochemical Model and Sigma Point Kalman Filter Based Online Oriented Battery Model

Miguel, Eduardo de; Plett, Gregory L.; Trimboli, M. Scott; Lopetegi, Iker; Oca, Laura; Iraola, Unai; Bekaert, Émilie

doi:10.1109/access.2021.3095620

Cited by 22 publications

(10 citation statements)

References 32 publications

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“…ROMs from multiple cells can be combined to predict multi-cell battery-pack behaviors. 79 The ROMs can also be used for real-time electrochemical state estimation, 80 fast charge, 81 and state-of-power estimation. 82 to compute the cell impedance response.…”

Section: Discussionmentioning

confidence: 99%

Nondestructive EIS Testing to Estimate a Subset of Physics-based-model Parameter Values for Lithium-ion Cells

Lü¹,

Trimboli²,

Fan³

et al. 2022

J. Electrochem. Soc.

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This paper is the final installment in a series of articles that collectively shows how to estimate parameter values for lumped-parameter physics-based models of lithium-ion cells without requiring cell teardown. In this paper, we leverage electrochemical impedance spectroscopy (EIS) to find estimates of all as-yet-unresolved parameter values. The characterization process regresses the measured cell impedance spectrum to exact analytic closed-form expressions of the frequency response of an extended Doyle–Fuller–Newman model to identify thirteen lumped parameters plus multiple reaction-rate constants. A nonlinear optimization algorithm performs the regression, and so it is important to provide reasonable initial parameter estimates and constraints, which we also discuss. As part of this process, the generalized distribution of realization times technique is used to isolate time constants from the two electrodes as well as to calibrate the laboratory EIS-test data. The overall methodology is studied on a virtual cell and on a laboratory cell (both having graphite//NMC chemistries). Parameter estimates found in the simulation study are highly accurate, leading us to have confidence in the values estimated for the physical cell as well.

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Section: Discussionmentioning

confidence: 99%

Nondestructive EIS Testing to Estimate a Subset of Physics-based-model Parameter Values for Lithium-ion Cells

Lü¹,

Trimboli²,

Fan³

et al. 2022

J. Electrochem. Soc.

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“…The first step in estimating health status is creating a model. The model parameters, operating current, and voltage are inserted into the extended Karman filter's recursive equation, and the model parameters are then updated in real time based on the value of the improved battery SOH [64].…”

Section: Battery State Of Health (Soh) Estimation: Model-based Methodsmentioning

confidence: 99%

“…It is used to process the dynamic behavior of the battery's physical properties. In the subsequent papers, which demonstrate the simultaneous evaluation of the SOC and SOH, the sigma-point Kalman filter adaptive filtering approach paired with an electrochemical model is utilized for this purpose [64].…”

Section: Standing Statementioning

confidence: 99%

State of Health Estimation Methods for Lithium-Ion Batteries

Nuroldayeva

Serik

Adair

et al. 2023

International Journal of Energy Research

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Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

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“…In operation, a BMS needs to estimate SOC in real-time, and hence, it is essential to use a battery model that can be computed sufficiently fast. A variety of PBMs have been used for battery physical state estimation, the most popular ones in the literature are based on the pseudo-two-dimensional (P2D) model [7][8][9]11,21 and the single-particle model (SPM). 10,13,[15][16][17][18]22 The P2D model describes the concentrations and potentials of the electrolyte and solid phase with four partial differential equations (PDE) and the reaction current density with an algebraic equation.…”

Section: Physics-based Modelmentioning

confidence: 99%

“…6 However, in recent years, physics-based models (PBMs) have been considered and investigated for these applications. [7][8][9][10][11] Due to the information that PBMs can give about the internal physical states of the battery, these models can offer superior performance when attempting to mitigate cell degradation. 2,3 Furthermore, they can provide more information on battery aging than just an SOH estimate, providing information about different degradation mechanisms and modes.…”

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confidence: 99%

A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part I. SOC and Internal Variable Estimation

Lopetegi,

Plett,

Trimboli

et al. 2024

J. Electrochem. Soc.

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Battery management systems (BMSs) are required to estimate many non-measurable values that describe the actual operating condition of batteries; such as the state of charge (SOC) or the state of health (SOH). In order to improve this evaluation, many physical states and parameters can be estimated using physics-based models (PBMs). These estimates could be used to improve the control and prognosis of batteries. In this series of papers we propose a new method to estimate the internal physical states, the SOC, the SOH, and the electrode-specific state of health (eSOH) parameters of a lithium-ion battery, using interconnected sigma-point Kalman filters (SPKFs) and a single-particle model with electrolyte dynamics (SPMe). This first paper focuses on state estimation for non-aged cells. To begin, we describe and validate our electrochemical model against a high-fidelity P2D model. After, the interconnected SPKF algorithm is described and the observability of our system is analyzed, showing that the interconnected estimator approach improves an observability measure of the system. Finally, the results of the estimator are discussed, comparing the estimated variables with the truth values under initialization, measurement and modeling uncertainties. The results show that the algorithm can estimate the internal battery states with high accuracy.

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Electrochemical Model and Sigma Point Kalman Filter Based Online Oriented Battery Model

Abstract: This work has been partially funded by a predoctoral grant of the Basque Government (PRE.2019.2.0195).

Cited by 22 publications

References 32 publications

Nondestructive EIS Testing to Estimate a Subset of Physics-based-model Parameter Values for Lithium-ion Cells

Nondestructive EIS Testing to Estimate a Subset of Physics-based-model Parameter Values for Lithium-ion Cells

State of Health Estimation Methods for Lithium-Ion Batteries

A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part I. SOC and Internal Variable Estimation

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