Modeling and State-of-Charge Estimation of Supercapacitor Considering Leakage Effect

Saha, Pankaj; Dey, Satadru; Khanra, Munmun

doi:10.1109/tie.2019.2897506

Cited by 49 publications

(19 citation statements)

References 39 publications

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“…A disturbance due to a leaking current through the external connection, as reported e.g. in case of supercapacitors 41 has to be excluded. This possibility could be ruled out because of the high internal resistance of the Maccor channel (>10 MΩ) as well as and by experiments without external electrical connection and Li counter electrode.…”

Section: Resultsmentioning

confidence: 99%

Mechanistic Details of the Spontaneous Intercalation of Li Metal into Graphite Electrodes

Hogrefe

Hein

Waldmann

et al. 2020

J. Electrochem. Soc.

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The mechanism of the spontaneous intercalation of Li metal into graphite electrodes is highly relevant for aging mechanisms and pre-lithiation of Li-ion cells. In the present work, we introduce a method to investigate this mechanism via measuring the opencircuit-potential (OCP). Experiments without electrolyte, with organic solutions without and with LiPF 6 reveal details on the reaction mechanism at 29 °C. The electrodes are investigated by Raman spectroscopy and glow-discharge optical emission spectroscopy (GD-OES) depth profiling to reveal the spatial distribution of the lithiated phases. The analytical information is enriched by simulations with the Battery and Electrochemistry Simulation Tool (BEST). The combination of tools gives interesting insights into the behavior of negative electrodes regarding re-intercalation of deposited Li into graphite and its kinetics, development of inhomogeneities during aging, as well as pre-lithiation and post-mortem analysis methodology.

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Section: Resultsmentioning

confidence: 99%

Mechanistic Details of the Spontaneous Intercalation of Li Metal into Graphite Electrodes

Hogrefe

Hein

Waldmann

et al. 2020

J. Electrochem. Soc.

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“…In addition, determining the energy stored in a supercapacitor with

b (t) = \frac{1}{2} C V {false(t false)}^{2}

is a simple approximation, but more elaborate methods such as the one presented in Ref. [46] are necessary to accurately determine the state of charge of a supercapacitor. The supercapacitor's capacitance is also specified with a relatively high tolerance.…”

Section: Experimental Validation: Indoor Harvesting Scenariomentioning

confidence: 99%

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Stricker

Forno

Thiele

2022

IET Computers & Digital Tech

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Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non‐deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting‐based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non‐robust system's performance by up to 13.8 times in a real‐world setting.

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“…One-step forward SOC prediction can only predict the SOC the next time, and algorithms have to be executed recursively. Scholars presented many methods to predict one-step forward SOC, for example, Kalman filtering [6][7][8], Particle filter [9], and Luenberger-style algorithm [10]. However, the range of a single prediction step is limited, which plays a limited role in solving the range anxiety.…”

Section: Introductionmentioning

confidence: 99%

A Simplified Historical-Information-Based SOC Prediction Method for Supercapacitors

Wang

Zhou

et al. 2022

IEEE Trans. Ind. Electron.

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Modeling and State-of-Charge Estimation of Supercapacitor Considering Leakage Effect

Cited by 49 publications

References 39 publications

Mechanistic Details of the Spontaneous Intercalation of Li Metal into Graphite Electrodes

Mechanistic Details of the Spontaneous Intercalation of Li Metal into Graphite Electrodes

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

A Simplified Historical-Information-Based SOC Prediction Method for Supercapacitors

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