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

Abstract

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.

Comparison Between Recursive Least Squares Method and Kalman Filter for Online Identification of Supercapacitor State of Health

“…θ(t)) K(t + 1) = P (t).x(t + 1) T . ((I + x(t + 1).P (t).x(t + 1) T ) −1 P (t + 1) = P (t) − K(t + 1).x(t + 1).P (t) (26) Figure 13 shows the functional diagram of the RLS method for parameters identification.…”

“…Soualhi et al [25] proposed a procedure based on fuzzy logic and artificial neural network (ANN) to estimate the internal resistance and capacitance of SCs. Roman et al [26] also proposed a machine learning Model based on ANN to estimate the capacitance of electrochemical capacitors at high temperature. ANN and fuzzy logic can be a powerful technique to provide a robust identification for systems that are subjected to uncertainties [27].…”

“…ANN and fuzzy logic can be a powerful technique to provide a robust identification for systems that are subjected to uncertainties [27]. However, these techniques usually require long and tedious offline tests for each new component to have enough training data [26,28]. In addition, they usually require a high computation time and complexity [29].…”

Adaptation of a Real-Time Deep Learning Approach With an Analog Fault Detection Technique for Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles

“…With the correct prediction of ESR and capacitance degradation, the reliability, lifetime and peak output current of the CB can be calculated. In [8]- [9] off-line learning data were used to predict the degradation independent of sudden failures. However, a sudden fault may occur in the internal capacitors of the CB.…”

Prognostics for Electromagnetic Relays Using Deep Learning

“…PHM as extensively reviewed in e.g., [17]- [19]) by creating actionable insights from data through data-driven Condition Based Maintenance (CBM) or Predictive Maintenance (PdM), is confronted with high volumes of Multivariate Time Series Data (MVTD). With the advent of Artificial Intelligence (AI), research is addressing this challenge using Machine Learning (ML) and increasingly Deep Learning (DL) for e.g., Remaining Useful Life (RUL) prediction of electronics [20]- [24]. Standalone approaches relying on Convolutional Neural Network (CNN) architectures or in combination with Recurrent Neural Network (RNN) elements employing techniques such as multivariate-time-series imaging [25] resonate with high volumes of MVTD as recent publication demonstrate [26]- [32].…”