State-of-charge estimation for supercapacitors: A Kalman filtering formulation

Nadeau, Andrew; Sharma, Gaurav; Soyata, Tolga

doi:10.1109/icassp.2014.6853988

Cited by 44 publications

(19 citation statements)

References 14 publications

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“…The charge takes significantly different amounts of time to migrate to, or from the different regions of the electrode areas through the porous surfaces. As a result, charge redistribution side effects are encountered such as open circuit voltage decay, capacitance loss at high frequency, and voltammetric distortions at high scan rates [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Mukhopadhyay

Dhaouadi

Takrouri

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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This paper presents a simplified supercapacitor model and a universal adaptive stabilization, optimization (UAS+O) based parameter identification technique. Analytic solutions for the description of supercapacitors current, voltage, subject to cyclic voltage and current sources of varying amplitudes and frequency, consistent with electric vehicle driving cycles, are developed. Supercapacitor I-V relationships show hysteresis, indicating simultaneous energy storage and dissipation mechanisms. A reduced equivalent circuit model is proposed to accurately represent hysteresis I-V characteristics. The proposed UAS+O based technique for estimating model parameters, is supported by mathematical proofs, simulation, and experimental results.

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Section: Introductionmentioning

confidence: 99%

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Mukhopadhyay

Dhaouadi

Takrouri

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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“…For example, the ampere-hour method [9,10] can be used to estimate the current SOC, but cannot predict the future SOC since future current is unknown. Furthermore, although the Kalman filtering method [11][12][13][14][15][16] is widely applied to predict SOC, this method can only estimate the SOC on the next moment. Besides, the artificial neural network is mainly applied to estimate SOC [17] or predict capacity [18,19] rather than predict SOC in the future.…”

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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“…The extended Kalman filter becomes a standard technique used in a number of nonlinear dynamic systems for state estimation, Nadeau et al [15] present a supercapacitor stateof-charge estimation for solar application using Kalman filter. Three-branch supercapacitor equivalent circuit has been chosen to model the supercapacitor.…”

Section: Introductionmentioning

confidence: 99%

Online Supercapacitor Diagnosis for Electric Vehicle Applications

Mejdoubi

Oukaour²,

Chaoui

et al. 2016

IEEE Trans. Veh. Technol.

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This paper presents an online diagnosis method for supercapacitors' aging problem. State-of-Health (SoH) estimation is an important feature since aging introduces degradation in supercapacitors' performance, which might eventually lead to their failure. SoH is usually measured by Electrochemical Impedance Spectroscopy (EIS). But, this has to be performed offline and requires interruption of the system's operation. Unlike this method, this paper presents an online diagnosis technique for supercapacitors using an extended Kalman observer as a well-known tool for its particularities and performances to study nonlinear parameters estimation. The main objective of this paper is the online State-of-Health diagnosis based on the supercapacitors aging indicators estimation. Moreover, the proposed online estimation strategy requires only voltage and current measurements, which reduces the number of sensors with respect to other methods. The effectiveness of the proposed online observer is shown through experimental results and robustness to noise is also studied. S 0018-9545 (c)

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State-of-charge estimation for supercapacitors: A Kalman filtering formulation

Cited by 44 publications

References 14 publications

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

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

Online Supercapacitor Diagnosis for Electric Vehicle Applications

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