Equivalent circuit model of supercapacitor for self-discharge analysis — A comparative study

Saha, Pankaj; Khanra, Munmun

doi:10.1109/scopes.2016.7955667

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…This observation time interval was chosen for this study, since within it all the significant effects related to the redistribution of the charge inside the SC's electrodes can be considered concluded, leaving from then on only the contributions related to charge loss (charge leakage, parasitic reactions, etc. ), which decreases with the time, as described in [43]. As mentioned above, the self-discharge phenomenon can be described in terms of the equivalent R SD resistance by using the relations (3) and (4), on the basis of the SC open-circuit voltage values (V start and V f inal refer to different time intervals) measured during the self-discharge phase, as detailed in the following section.…”

Section: Methodsmentioning

confidence: 99%

Limitations and Characterization of Energy Storage Devices for Harvesting Applications

et al. 2020

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This paper aims to study the limitations and performances of the main energy storage devices commonly used in energy harvesting applications, namely super-capacitors (SC) and lithium polymer (LiPo) batteries. The self-discharge phenomenon is the main limitation to the employment of SCs to store energy for a long time, thus reducing efficiency and autonomy of the energy harvesting system. Therefore, the analysis of self-discharge trends was carried out for three different models of commercial SCs, describing the phenomenon in terms of self-discharge rate and internal resistance. In addition, physical interpretations concerning the self-discharge mechanism based on the experimental data are provided, thus explaining the two super-imposed phenomena featured by distinct time constants. Afterwards, the dependence of self-discharge phenomenon from the charging time duration (namely, SCs charged at 5 V and then kept under charge for one or five hours) was analyzed; by comparing the voltage drop during the self-discharge process, a self-discharge reduction for longer charging durations was obtained and the physical interpretation provided (at best −6.8% after 24 h and −13.4% after 120 h). Finally, self-discharge trends of two commercial 380 mAh LiPo batteries (model LW 752035) were acquired and analyzed; the obtained results show an open circuit voltage reduction of only 0.59% in the first 24 h and just 1.43% after 124 h.

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Section: Methodsmentioning

confidence: 99%

Limitations and Characterization of Energy Storage Devices for Harvesting Applications

et al. 2020

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“…They have very pronounced characteristics: self-discharge, dielectric absorption, and voltage dependent capacitance. There are numerous SC schemes by which the SC properties can be modeled [2,7,[10][11][12][13][14][15][16]. They can be arbitrarily divided into two categories.…”

Section: The Properties Of Scs and Its Modelsmentioning

confidence: 99%

“…Resistance of the electrode and electrolyte material is taken into account by resistances 0 R and f R [17,18]. Self-discharge [2,7,11] is modeled by a leakage resistance L R . Dependence of SC capacitance upon the applied voltage is linear, but its variation from a state when SC is uncharged and fully charged at rated voltage can be up to 50% [7][8], and even higher [9].…”

Section: The Properties Of Scs and Its Modelsmentioning

confidence: 99%

An analytical approximation of the transient response of a voltage dependent supercapacitor model

Barić¹,

Glavaš²,

Kljajić³

2019

Eng. rev. (Online)

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Supercapacitors are well known for their voltage dependent capacity. Due to this, it is not possible to obtain the exact analytical solution of the nonlinear differential equation which describes the transient charging and discharging. For this reason, approximations of differential equations must be carried out in order to obtain an approximate analytical solution. The focus of this paper is on a different approach. Instead of approximating the differential equation and obtaining analytical expressions for such approximations, an intuitive approach is chosen. This approach is based on the separation of the initial response from the rest of the transient phenomenon. Both parts of the transient phenomenon are described with adequate functions. Using appropriate weight functions, both functions are combined into a single function that describes the whole transient phenomenon. As shown in the paper, such an approach gives an excellent description of the whole transient. Also, it provides simpler expressions compared to those obtained by approximation of the nonlinear differential equation. With respect to their accuracy, these expressions do not lag behind the aforementioned approach. The validity of the presented analytical expressions was confirmed by comparing their results with those obtained by numerically solving the nonlinear differential equation.

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“…Thus, as with standard capacitors like tantalum or electrolytic [15], it could use an equivalent electrical model of distributed components, Figure 2c, based on the physic morphology of the supercapacitor [16,17].…”

Section: Equivalent Electrical Modelmentioning

confidence: 99%

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

et al. 2018

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Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions.

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Equivalent circuit model of supercapacitor for self-discharge analysis — A comparative study

Cited by 20 publications

References 13 publications

Limitations and Characterization of Energy Storage Devices for Harvesting Applications

Limitations and Characterization of Energy Storage Devices for Harvesting Applications

An analytical approximation of the transient response of a voltage dependent supercapacitor model

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

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