259 research outputs found
Generalized formulation to estimate the Supercapacitor’s R-C series impedance using fractional order model
The main objective of this paper is to develop a new technique for the supercapacitor’s parameter identification that can handle the issue of initial voltage. An effective approach is proposed
using the fractional-order derivative for accurate identification of a well-known series Resistance-Capacitance (R-C) model. An expression is derived using the Caputo definition and the Haar wavelet operational matrix, which is sufficient for both charging and discharging phase data of supercapacitors. To extract impedance parameters, voltage stimulated step response is utilized and parameters are calculated despite random initial voltage stored in a supercapacitor. This
operational matrix approach transforms complex fractional derivative terms into simple algebraic expressions and reduces the overall complexity. The proposed technique shows a very good agreement with experimental data that exhibit different initial voltages and different time-frames. Investigations and experiments with various supercapacitors clearly reveal the importance of the
developed equation for a fractional R-C model
A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors
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 b attery/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
Possibility of information encoding/decoding using the memory effect in fractional-order capacitive devices
In this study, we show that the discharge voltage pattern of a supercapacitor exhibiting fractional-order behavior from the same initial steady-state voltage into a constant resistor is dependent on the past charging voltage profile. The charging voltage was designed to follow a power-law function, i.e. [Formula: see text], in which [Formula: see text] (charging time duration between zero voltage to the terminal voltage [Formula: see text]) and p ([Formula: see text]) act as two variable parameters. We used this history-dependence of the dynamic behavior of the device to uniquely retrieve information pre-coded in the charging waveform pattern. Furthermore, we provide an analytical model based on fractional calculus that explains phenomenologically the information storage mechanism. The use of this intrinsic material memory effect may lead to new types of methods for information storage and retrieval
Information Encoding/Decoding using the Memory Effect in Fractional-order Capacitive Devices
In this study, we show that the discharge voltage pattern of a
fractional-order supercapacitor from the same initial steady-state voltage into
a constant resistor is dependent on the past charging voltage profile. The
charging voltage was designed to follow a power-law function, i.e.
, in which
(charging time duration between zero voltage to the terminal voltage
) and () act as two variable parameters. We used this
history-dependence of the dynamic behavior of the device to uniquely retrieve
information pre-coded in the charging waveform pattern. Furthermore, we provide
an analytical model based on fractional calculus that explains
phenomenologically the information storage mechanism. The use of this intrinsic
material memory effect may lead to new types of methods for information storage
and retrieval.Comment: 5 pages, 3 figures, Submitted on Jan 28, 2021 to ACS Applied
Electronic Materials - Manuscript ID: el-2021-00092
Charge-based supercapacitor storage estimation for indoor sub-mW photovoltaic energy harvesting powered wireless sensor nodes
Supercapacitors offer an attractive energy storage solution for lifetime “fit and forget” photovoltaic (PV) energy harvesting powered wireless sensor nodes for internet of things (IoT) applications. Whilst their low storage capacity is not an issue for sub-mW PV applications, energy loss in the charge redistribution process is a concern. Currently there is no effective method to estimate the storage of the supercapacitor in IoT applications for optimal performance with sub-mW input. The existing energy-based method requires supercapacitor model parameters to be obtained and the initial charge state to be determined, consequently it is not suitable for practical applications. This paper defines a charge-based method, which can directly evaluate supercapacitor’s storage with straightforward calculations. Time constant analysis and experimental tests demonstrate that with the newly proposed method the manufacturer-specified tiny leakage current, although measured long after post-charge (e.g. 72 hours), can be directly used, making the storage estimation for a supercapacitor in IoT applications as simple as that for an ordinary capacitor. In addition, the demonstrated tiny leakage current at the required energy storage for a sub-mW PV powered IoT application enables a supercapacitor alone to be employed as the storage mechanism, thus achieving lifetime battery-replacementfree, self-powered IoT nodes
Various analytical models for supercapacitors: a mathematical study
Supercapacitors (SCs) are used extensively in high-power potential energy applications like renewable energy systems, electric vehicles, power electronics, and many other industrial applications. This is due to SCs containing high-power density and the ability to respond spontaneously with fast charging and discharging demands. Advancements in material and fabrication techniques have induced a scope for research to improve the application of SCs. Many researchers have studied various SC properties and their effects on energy storage and management performance. In this paper, various fractional calculus-based SC models are summarized, with emphasis on analytical studies from derived classical SC models. Study prevails such parameterized resistor- capacitor networks have simplified the representation of electrical behavior of SCs to deal with the complicated internal structure. Fractional calculus has been used to develop SC models with the aim of understanding their complicated structure. Finally, the properties of different SC models utilized by various researchers to understand the behavior of SCs are listed using an equivalent circuit
A Conformal Fractional Derivative-based Leaky Integrate-and-Fire Neuron Model
Neuron model have been extensively studied and different models have been proposed. Nobel laureate Hodgkin-Huxley model is physiologically relevant and can demonstrate different neural behaviors, but it is mathematically complex. For this reason, simplified neuron models such as integrate-and-fire model and its derivatives are more popular in the literature to study neural populations. Lapicque’s integrate-and-fire model is proposed in 1907 and its leaky integrate-and-fire version is very popular due to its simplicity. In order to improve this simple model and capture different aspects of neurons, a variety of it have been proposed. Fractional order derivative-based neuron models are one of those varieties, which can show adaptation without necessitating additional differential equations. However, fractional-order derivatives could be computationally costly. Recently, a conformal fractional derivative (CFD) is suggested in literature. It is easy to understand and implement compared to the other methods. In this study, a CFD-based leaky integrate-and-fire neuron model is proposed. The model captures the adaptation in firing rate under sustained current injection. Results suggest that it could be used to easily and efficiently implement network models as well as to model different sensory afferents
Energy Consideration of a Capacitor Modelled Using Conformal Fractional-Order Derivative .
Fractional order circuit elements have become important parts of electronic circuits to model systems including supercapacitors, filters, and many more. The conformal fractional derivative (CFD), which is a new basic fractional derivative, has been recently used to model supercapacitors successfully. It is essential to know how electronic components behave under excitation with different types of voltage and current sources. A CFD capacitor is not a well-known element and its usage in circuits is barely examined in the literature. In this research, it is examined how to calculate the stored energy of a CFD capacitor with a series resistor supplied from a DC voltage source. The solutions given in this study may be used in circuits where supercapacitors are used
Advances in Supercapacitor Technology and Applications
Energy storage is a key topic for research, industry, and business, which is gaining increasing interest. Any available energy-storage technology (batteries, fuel cells, flywheels, and so on) can cover a limited part of the power-energy plane and is characterized by some inherent drawback. Supercapacitors (also known as ultracapacitors, electrochemical capacitors, pseudocapacitors, or double-layer capacitors) feature exceptional capacitance values, creating new scenarios and opportunities in both research and industrial applications, partly because the related market is relatively recent. In practice, supercapacitors can offer a trade-off between the high specific energy of batteries and the high specific power of traditional capacitors. Developments in supercapacitor technology and supporting electronics, combined with reductions in costs, may revolutionize everything from large power systems to consumer electronics. The potential benefits of supercapacitors move from the progresses in the technological processes but can be effective by the availability of the proper tools for testing, modeling, diagnosis, sizing, management and technical-economic analyses. This book collects some of the latest developments in the field of supercapacitors, ranging from new materials to practical applications, such as energy storage, uninterruptible power supplies, smart grids, electrical vehicles, advanced transportation and renewable sources
- …