A Life Model for Supercapacitors

Williard, Nicholas; Baek, Donkyu; Park, Jong Won; Choi, Byung-Oh; Osterman, Michael; Pecht, Michael

doi:10.1109/tdmr.2015.2479466

Cited by 19 publications

(19 citation statements)

References 16 publications

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“…Several efforts have been made to model capacitor degradation under both temperature and voltage conditions [12], [14], [17], [18], [19]; however, most rely on simplified exponential models that do not translate well from one manufacturer to another. Given the variability in manufactured components and application-specific direct and indirect loading, this is a limiting factor in the applicability of the current state-of-the-art models.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we deploy a machine learning algorithm to predict degradation trends for electrochemical doublelayer capacitors beyond the knee-point onset when cycled at high temperature in an oil and gas drilling environment. Operation at high temperature accelerates EC degradation as shown in [17], we therefore investigate the worst-case scenario for the mentioned application. This paper considers samples from two manufacturers with different ratings, as opposed to previous methods [18] - [19], which concentrate on a single manufacturer.…”

Section: Introductionmentioning

confidence: 99%

“…We use capacitance rather than ESR as a proxy for degradation. The choice of capacitance as a state of health indicator is motivated by capacitor cells reaching the end of life (EoL) criteria-measured as a 30% decrease in capacitance-first, as opposed to ESR-based criteria of a 100% increase in resistance [17]. The modelling stage employs a Gaussian process algorithm that is fitted on two covariates (temperature and end of charge voltage) as well as two measurands (nominal capacitance value and cycle number) to predict the capacitance degradation as a function of cycle number.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

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Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturerspecified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

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“…To confirm these phenomena and to predict degradation mechanism, chemical and electrochemical aging test were performed using original electrode under acetonitrile (AN)-based electrolyte, propylene carbonate (PC)-based electrolyte [1][2][3][4][5][6][7], and ionic liquid as electrolyte [8]. It was also reported that aging model was calculated and aging diagnosis was built up using manufactured EDLCs under overvoltage and high-temperature condition [9][10][11][12][13][14][15][16][17][18][19][20]. For this chapter, we performed a voltage hold test, which is a type of accelerated degradation test for EDLCs using the application of overvoltage, on cylindrical EDLCs, which are used in actual applications [21].…”

Section: Introductionmentioning

confidence: 99%

Deterioration Factors of Electric Double-Layer Capacitors Obtained from Voltage Hold Test

Tashima¹

2019

Science, Technology and Advanced Application of Supercapacitors

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A chemical analysis was carried out before and after a constant voltage hold test (applied at 2.5, 2.8, 3.0, and 3.2 V individually for 1 week) that was an acceleration deterioration examination to clarify the deterioration factors of electric doublelayer capacitors. The results showed that the stress test slightly raised the internal resistances and decreased the capacitances. It was also confirmed that a range of fluorochemicals was formed on the electrode surface for approximately 10-13 nm in depth using electron spectroscopy for chemical analysis. From a chemical analysis of the electrolyte using an inductively coupling plasma-optical emission spectrometer (ICP-OES), it was confirmed that the electrolyte included Si, which is an ingredient element of an electrode, and that the increase in the holding voltage during the stress test decreased the Si density in the electrolyte.

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“…Although some studies are titled with SOC prediction, they are in reality the estimation of SOC at the next moment [21]. Besides, some related studies about forecast are primarily remaining capacitance prediction, remaining energy and power prediction [19], as well as life prediction [22–24]. Apparently, applying these methods directly to SOC prediction is not appropriate, because the definition of SOC is different from these concepts.…”

Section: Introductionmentioning

confidence: 99%

State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network

Wang

Zhou

2018

IET Electric Power Applications

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Supercapacitors are increasingly applied to the field of electric vehicles. Although the state of charge (SOC) directly shows the remaining capacity of supercapacitors in an energy management system, vehicle drivers also require the changes of supercapacitors in the future for driving reference. How to predict SOC of supercapacitors has become a pressing problem. In order to solve the above problem, a method combining backpropagation (BP) neural network with the Kalman filtering algorithm is proposed to predict SOC in the future. The BP neural network inputs SOC estimated by the Kalman filtering algorithm as the training data to train network, and thereby being able to forecast the SOC in the future period. The algorithm is verified in simulations and experiments under the two conditions: NewYorkBus and NYCC, with the consideration of the influence of the length of training data and temperature. The results show the max absolute error during prediction at different lengths is under 6% in simulations and experiments. In addition, temperature has almost no effect on the prediction accuracy. The current research implies that this method can be applied to predict supercapacitors' SOC in the future.

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A Life Model for Supercapacitors

Cited by 19 publications

References 16 publications

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

Deterioration Factors of Electric Double-Layer Capacitors Obtained from Voltage Hold Test

State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network

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