Supercapacitors and lithium-ion batteries for power electronic applications

Buller, S.; Thele, Marc; Doncker, Rik W. De; Karden, Eckhard

doi:10.1109/mia.2005.1405828

Cited by 26 publications

(21 citation statements)

References 3 publications

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“…5b, consisting of an inductance, a serial resistance and two ZARC-elements, was used to describe the impedance spectra. The ZARC-element consists of a resistance and a constant phase element in parallel, described by the parameters R, A and 4 [21,22]:…”

Section: Calendar and Cycle Aging Resultsmentioning

confidence: 99%

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Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

Ecker

Gerschler

Vogel

et al. 2012

Journal of Power Sources

500

294

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Section: Calendar and Cycle Aging Resultsmentioning

confidence: 99%

“…The thermal, as well as the dynamic electrical behavior can be simulated accurately. A similar approach was also used in [21,26]. As aging depends strongly on temperature and voltage, the lifetime model receives temperature and voltage calculated by the thermo-electrical model in each time step.…”

Section: Development Of Lifetime Modelmentioning

confidence: 99%

Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

Ecker

Gerschler

Vogel

et al. 2012

Journal of Power Sources

500

294

View full text Add to dashboard Cite

“…1, the EC has the resistance element, R elect , representing the resistance of the bulk electrolyte, and an R-CPE parallel circuit [45][46][47][48][49][50]58,59] for the passive film with resistance R film and constant phase element, CPE film . The final component is an R-CPE parallel [61] element that represents the electric doublelayer capacitance as a constant-phase element, CPE dl , and the charge transfer resistance, R ct , representing the site where the electrochemical reaction occurs. Each CPE contains the pseudocapacitance and exponential parameter, as expressed in Eq.…”

Section: Implementation Of Equivalent Circuit Modelmentioning

confidence: 99%

Passive film formation on a graphite electrode: Effect of siloxane structures

Nakahara¹,

Yoon²,

Nutt³

2006

Journal of Power Sources

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“…This frequency range depends on the battery chemistry [32,34,35]. This measurement method cannot be used in a realtime application due to hardware complexity and high cost.…”

Section: Internal Impedance Measurementsmentioning

confidence: 99%

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Kalawoun

Biletska

Suard

et al. 2015

Journal of Power Sources

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. From a novel classification of the battery state of charge estimators toward a conception of an ideal one. Journal of Power Sources, Elsevier, 2015, 279, pp.694 -706. 10 b s t r a c tAn efficient estimation of the State of Charge (SoC) of an electrical battery in a real-time context is essential for the development of an intelligent management of the battery energy. The main performance limitations of a SoC estimator originate in limited Battery Management System hardware resources as well as in the battery behavior cross-dependence on the battery chemistry and its cycling conditions. This paper presents a review of methods and models used for SoC estimation and discusses their concept, adaptability and performances in real-time applications. It introduces a novel classification of SoC estimation methods to facilitate the identification of aspects to be improved to create an ideal SoC model. An ideal model is defined as the model that provides a reliable SoC for any battery type and cycling condition, online. The benefits of the machine learning methods in providing an online adaptive SoC estimator are thoroughly detailed. Remaining challenges are specified, through which the characteristics of an ideal model can emerge.

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Supercapacitors and lithium-ion batteries for power electronic applications

Cited by 26 publications

References 3 publications

Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

Passive film formation on a graphite electrode: Effect of siloxane structures

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

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