Parameter Estimation of Fractional Systems: Application to the Modeling of a Lead-Acid Battery

Lin, Jun; Poinot, Thierry; Trigeassou, Jean‐Claude; Ouvrard, Régis

doi:10.1016/s1474-6670(17)39881-6

Cited by 30 publications

(12 citation statements)

References 3 publications

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“…Further developments will confirm the interest of this benchmark model to perform a good approximation of the interface dynamics at different time scales [28] and with different kind of systems [17][18][19].…”

Section: Approximate Modeling Of a Diffusive Interfacementioning

confidence: 84%

Identification of Fractional Systems Using an Output-Error Technique

2004

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An original method for modeling, simulation and identification of fractional systems in the time domain is presented in this article. The basic idea is to model the fractional system by a state-space representation, where conventional integration is replaced by a fractional one with the help of a non-integer integrator. This operator is itself approximated by a N -dimensional system composed of an integrator and of a phase-lead filter. An output-error technique is used in order to estimate the parameters of the model, including the fractional order n. Simulations exhibit the properties of the identification algorithm. Finally, this methodology is applied to the modeling of the dynamics of a real heat transfer system.

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Section: Approximate Modeling Of a Diffusive Interfacementioning

confidence: 84%

Identification of Fractional Systems Using an Output-Error Technique

2004

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“…Garcia et al [79] presented a diffusive model to delineate the dynamic behaviors of lead-acid batteries using a fractional-order operator. Following this, Lin et al [80] proposed a framework of fractional-order modeling for diffuse processes, which was then used to simulate the dynamics of lead-acid batteries. The output-error technique was employed by them to derive the model parameters based on classical input/output data of test cells.…”

Section: Lead-acid Battery Modelsmentioning

confidence: 99%

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Zou

Zhang

et al. 2018

Journal of Power Sources

430

155

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Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15-30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced battery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.

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“…Non-integer order systems, also known as fractional order systems, have been introduced long ago in various fields of science for the modeling of diffusion processes such as electrochemical phenomena encountered in batteries (Lin et al (2000); Sabatier et al (2006)), the modeling of frequency effects (skin effect) in the squired-cages of induction machine and heat transfer. Such systems are characterized by long range memory transients (Gabano and Poinot (2009)).…”

Section: Introductionmentioning

confidence: 99%

“…Previous works concerning diffusion interface modeling using the fractional approach have already shown the ability of such models to estimate with good accuracy the frequency behavior from time data acquisition (Lin et al (2000); Wang et al (2002)). By analyzing the diffusion interface Bode plots in the case of the heat transfer through a finite thickness sphere, we outline the fractional behavior at high frequencies.…”

Section: Introductionmentioning

confidence: 99%

Relation between fractional order models and diffusion in the body

Copot

Ionescu

Keyser

2014

IFAC Proceedings Volumes

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Robain.DeKeyser} @ UGent.be).Abstract: This paper presents the mathematical roots in fractional calculus that are present in the modeling of diffusion in the human body. Respiratory diffusion, as well as drug diffusion, are discussed and presented with their common mathematical framework. The links and discussion presented in this paper suggests that emerging tools from fractional calculus are natural solutions for modeling complex phenomena in biological tissue.

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Parameter Estimation of Fractional Systems: Application to the Modeling of a Lead-Acid Battery

Cited by 30 publications

References 3 publications

Identification of Fractional Systems Using an Output-Error Technique

Identification of Fractional Systems Using an Output-Error Technique

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Relation between fractional order models and diffusion in the body

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