Ultracapacitor Degradation State Diagnosis via Electrochemical Impedance Spectroscopy

Abstract

In this paper, we propose the use of the electrochemical impedance spectroscopy technique for state diagnosis of electric double-layer ultracapacitors. Ultracapacitors under investigation are Maxwell BCAP 0350 and they were stressed in several ways for our study. The analyzed stress conditions include accelerated aging tests, overvoltage, and over temperature tests. Chemical and physical effects on supercapacitor materials are pointed out through a morphologic analysis implemented by means of an environmental scanning electron microscope and a chemical composition analysis obtained by means of an energy dispersive spectrometer. Consequent degradation effects are described in terms of frequency behavior of equivalent circuital parameters

Modelling of current and temperature effects on supercapacitors ageing. Part I: Review of driving phenomenology

“…Actually, during a PC test, due to the extraction of the current, there are several impurities coming from the de-cohesion and micro-cracking of carbon electrode particles [4,5,[24][25][26][27][28][29][30]. 8 This phenomenon is higher if the extracted current is several time higher than the nominal one (as in the work here presented).…”

Study of Electrochemical Impedance of a Continuous Glucose Monitoring Sensor and its Correlation With Sensor Performance

“…This has been experimentally proven for the amperometric gas sensors, where the double layer capacitance of the sensor is shown to be linearly proportional to the sensitivity of the sensor [8]. The impedance spectroscopy has been also used to determine the health of the Lithium-ion battery cells [9], electric double-layer ultracapacitors [10], fuel cell systems [11] and glucose sensors [12] among other applications [13]. Motivated by these promising results, in this work we make an attempt to find the correlation between the sensitivity of electrochemical biosensors with their impedance.…”

Multilayer Neural Network with Multivalued Neurons MLMVN based CLASS E Resonant Inverter Fault Detection

“…In these kinds of applications a fault can lead to fatal consequences. Therefore, fault identification is crucial to prevent fault propagation and avoid catastrophic failures of the converter, which affect proper operation [1][2][3][4][5][6][7][8][9]. Among the components utilized in both PWM and resonant converters circuits [10,11], inductors and capacitors have the greatest impact on the converter operation because faults and aging manifest as changes in the value of the parameters out of tolerances.…”