Prediction Aging Model for Supercapacitor's Calendar Life in Vehicular Applications

Mejdoubi, Asmae El; Oukaour, Amrane; Chaoui, Hicham; Gualous, Hamid; Sabor, Jalal; Youssef, S.

doi:10.1109/tvt.2016.2539681

Cited by 28 publications

(9 citation statements)

References 31 publications

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“…Over the last few decades, considerable attention has been paid to extending this model for acceleration degradation modeling. For example, Mejdoubi and his co-authors [63] employed voltage and temperature to accelerate the degradation process of supercapacitors, and then incorporated the aging effects using the generalized Eyring relationship. Besides, the current is another environmental stress that has been combined with other stress factors to accelerate chemical reaction processes.…”

Section: A: Physical Acceleration Modelmentioning

confidence: 99%

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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Reliability engineering plays an important role in the design, manufacture, maintenance, and replacement of industrial products. Over the last few decades, accelerated degradation testing (ADT) has been largely utilized to shorten test durations, reduce the samples needed, and provide sufficient degradation data to ensure the effective reliability assessment of the concerned products. Meanwhile, performance degradation modeling has been recognized as an essential approach to help researchers and producers understand the health conditions of the deteriorating systems. However, the diversity in reliability tests, degradation models, and statistical analysis techniques has increased the difficulty in selecting appropriate reliability assessment methods in specific scenarios. Besides, there are no systematic reviews focused on modeling and analysis of performance degradation data. Therefore, this paper aims to (1) present ADT fundamentals, including the basic theory, ADT methods, accelerated stress variables, type of acceleration models, as well as ADT optimization, (2) comprehensively review current states and future challenges in degradation modeling, (3) discuss the problem of model mis-specification and compare different approaches for parameter estimation, (4) highlight future opportunities and possible directions deserving further research.

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Section: A: Physical Acceleration Modelmentioning

confidence: 99%

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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“…They produce solid and gaseous products, which will block porosity of SCs and increase internal pressure, leading to capacitance reduction and ESR increase further (as shown in Fig 1). According to a number of research results [6,7,16,[8][9][10][11][12][13][14][15], the operating temperature, voltage, and current are the critical stressors of failure, which will speed up the rate of chemical reaction and solvent decomposition, playing important roles in aging rate and lifetime expectancy. Thus, the lifetime model can be established as a function of these stressors.…”

Section: Pof Mechanism Of Lifetime Modeling For Scsmentioning

confidence: 99%

“…It should be noted that the forms listed in Table 2 are the basic parts of the lifetime model, and there may exist some other components of scaling factor. For example, in [15], the authors added a nonlinear pre-exponential factor into the lifetime model to improve the prediction accuracy.…”

Section: The Modeling Of Vmentioning

confidence: 99%

“…To solve these challenges, the reliability of SCs needs to be studied, in order to understand the performance and real lifetime in practical conditions rather than test conditions given by the datasheet, and help design appropriate products for specific requirements, so as to reduce the cost of O&M and optimize the LCOS. Several lifetime models [6,7,16,[8][9][10][11][12][13][14][15] have been employed to predict the operation lifetime, but there is still a lack of systematical analysis especially from viewpoint of failure mechanism. Given these requirements, this paper reviews different lifetime models of SCs in a systematical way, and proposes some suggestions for system design based on reliability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supercapacitor Lifetime Modeling and Reliability-Oriented System Design in Energy Storage Application

Shuai¹,

Liu²,

Li³

et al. 2020

Preprint

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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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Prediction Aging Model for Supercapacitor's Calendar Life in Vehicular Applications

Cited by 28 publications

References 31 publications

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Supercapacitor Lifetime Modeling and Reliability-Oriented System Design in Energy Storage Application

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

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