Block-oriented system identification for nonlinear modeling of all-solid-state Li-ion battery technology

Firouz, Yousef; Goutam, Shovon; Soult, Marta Cazorla; Mohammadi, Amir; Mierlo, Joeri Van; Bossche, Peter Van den

doi:10.1016/j.est.2019.101184

Cited by 12 publications

(5 citation statements)

References 57 publications

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“…From the perspective of frequency domain system identification theory, the non-linearity of the battery has to be captured and quantized in advance, and then, according to the characterised level of the nonlinearity, it can be accounted via an appropriate non-linear function (e.g. sigmoid, neural nets or polynomials) and added into equivalent circuit model (ECM) to improve model accuracy (such examples at a full cell level can be found in [24] and [40]). Meanwhile, the model will have the advantage to separate the voltage response of each electrodes from a three-electrode cell configuration.…”

Section: Discussionmentioning

confidence: 99%

Frequency domain non-linear characterization and analysis of lithium-ion battery electrodes

Fan

O’Regan

et al. 2021

Journal of Energy Storage

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Section: Discussionmentioning

confidence: 99%

Frequency domain non-linear characterization and analysis of lithium-ion battery electrodes

Fan

O’Regan

et al. 2021

Journal of Energy Storage

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“…Unlike the previous literature Ref. [24,30] only focusing on the extreme low frequency range (≤ 1 Hz), the bandwidth in this work was extended to 10 Hz to comprise characteristic frequency ranges of both diffusion process and charge transfer kinetics. A rapid decrease in the impedance magnitude and a peak in the phase at low frequency range are shown in Fig.…”

Section: Nedc Testmentioning

confidence: 99%

“…Authors in [30] developed and parametrized a Hammerstein-Wiener structure non-linear model for all-solid-state batteries. However, only the low frequency range ( f < 1 Hz) for capturing the diffusion dynamic was considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Data-driven identification of lithium-ion batteries: A nonlinear equivalent circuit model with diffusion dynamics

Fan

O’Regan

et al. 2022

Applied Energy

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“…Assumption (III) displays that the linear system orders are known. These assumptions can be found in [ 29 , 30 ].…”

Section: Sandwich System and Identification Modelmentioning

confidence: 99%

Parameter estimation for nonlinear sandwich system using instantaneous performance principle

Wang

2022

PLoS ONE

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The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of nonlinear sandwich system is studied by using the predefined constraint technology and high-effective filter. To achieve the above purpose, the estimation error information reflecting the transient performance of parameter estimation is procured using the developed some intermediate variables. Then, a predefined constraint function is used to prescribe the error convergence boundary, in which the convergence rate is lifted. An error equivalent conversion technique is then employed to obtain the transformed error data for establishing an parameter adaptive update law, in which the estimation error convergence and the predefined domain can be achieved. In comparison with the available estimation schemes, the good instantaneous performance is obtained on the basis of the numerical example and practical process results.

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Block-oriented system identification for nonlinear modeling of all-solid-state Li-ion battery technology

Cited by 12 publications

References 57 publications

Frequency domain non-linear characterization and analysis of lithium-ion battery electrodes

Frequency domain non-linear characterization and analysis of lithium-ion battery electrodes

Data-driven identification of lithium-ion batteries: A nonlinear equivalent circuit model with diffusion dynamics

Parameter estimation for nonlinear sandwich system using instantaneous performance principle

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