Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Mukhopadhyay, Shayok; Dhaouadi, Rached; Takrouri, Mohannad; Raveendhra, Dogga

doi:10.1109/ojies.2020.3008339

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…It is quite fascinating to compare the results obtained with those of the conventional N-phase IDDB converter [3]. With consideration of similar assumptions for the symmetry between phases, (31) gives the N-phase interleaved boost converter reduced-order model, with the state variables being selected as the output voltage and the inductor current in one phase:…”

Section: Modelling Of the N-phase Convertermentioning

confidence: 99%

“…New challenges in energy conversion technology are being faced due to the increased use of renewable energy sources. One such challenge is that several types of devices that store or produce electrical energy, such as ultra-capacitors, solar panels, batteries, and fuel cells, are manufactured using low-voltage cells, which must be series-connected to attain reasonable voltages [1][2][3]. In such cases, the complexity of the system is increased due to the series connection of a large number of cells, which reduces the performance due to the differences among the cells, such as fabrication variations and other various working conditions.…”

Section: Introductionmentioning

confidence: 99%

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A High-Gain Multiphase Interleaved Differential Capacitor Clamped Boost Converter

Raveendhra¹,

Poojitha

Kumar

et al. 2022

Electronics

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A step-up for a non-isolated interleaved differential capacitor clamped boost (IDCCB) DC–DC converter is proposed in this manuscript. Because of its ability to produce high voltage gains, it is used in high-power applications. This converter’s modelling and control design are applicable to any number of phases. A six-phase interleaved differential capacitor clamped boost prototype is tested in this work, with an input voltage of 60 V, an output voltage of 360 V, and a nominal output power of 2.2 kW. The components of the converter are placed and controlled in such a way that the output voltage is the sum of the two capacitor voltages and the input voltage, which is two times higher than the supply voltage when compared to a conventional interleaved differential dual-boost converter. This converter reduces the stress on the capacitor with reference to the conventional interleaved differential boost converter for the same conversion gain. This prototype is considered and the developed approach is applied, after which the experimental results are obtained. This converter has potential for application in areas such as renewable energy conversion and electric vehicles.

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Section: Modelling Of the N-phase Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Gain Multiphase Interleaved Differential Capacitor Clamped Boost Converter

Raveendhra¹,

Poojitha

Kumar

et al. 2022

Electronics

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“…It is also worth noting that there exists work on parameters estimation which does not explicitly impose the PE condition [37], and achieves reliable parameter estimates. And work related to using the UAS based approach for parameters estimation exists, and has been rigorously verified in [2], [25], [38], [39].…”

Section: B Persistence Of Excitationmentioning

confidence: 99%

“…Iterative methods using a combination of optimization along with UAS-based approaches are available in the literature, to tune the upper, lower bounds, respective confidence levels, and initial values [4], [38]. This enables the setting of initial guesses for the upper, lower bounds, respective confidence levels, and initial values; based on which an optimization routine determines the updated values of the upper, lower bounds, respective confidence levels, and initial values for the next iteration of parameters estimation.…”

Section: ) Initial Guessesmentioning

confidence: 99%

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

Mukhopadhyay

Usman

Rehman

2022

IEEE Open J. Control. Syst.

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This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables update of battery parameters during run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering an induction motor driven prototype electric vehicle (EV) traction system.

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“…The method is conducted under a significantly low battery current. Unlike battery model identification, DPE was employed to estimate the model parameters of a fuel cell, permanent magnet DC motor, and super-capacitor in [34], [35], and [36], respectively.…”

Section: A Deterministic Parameter Estimationmentioning

confidence: 99%

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

Tameemi

2020

IEEE Access

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In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort. INDEX TERMS universal adaptive stabilizer (UAS), particle swarm optimization (PSO), teachinglearning-based optimization (TLBO), unscented Kalman filter (UKF), and maximum likelihood estimation (MLE).

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Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Cited by 15 publications

References 35 publications

A High-Gain Multiphase Interleaved Differential Capacitor Clamped Boost Converter

A High-Gain Multiphase Interleaved Differential Capacitor Clamped Boost Converter

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

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