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Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks | IEEE Conference Publication | IEEE Xplore

Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks


Abstract:

The accurate determination of the condition of a battery is crucial for an optimized operation in the context of several loading and ambient conditions. A tool to achieve...Show More

Abstract:

The accurate determination of the condition of a battery is crucial for an optimized operation in the context of several loading and ambient conditions. A tool to achieve this goal is the electrochemical impedance spectroscopy (EIS), which is a non-destructive measuring method to analyze the internal processes in a cell and results in a complex valued impedance as a function of the frequency. The measurements require high efforts for the experimental setup and the measurement time. This paper presents a method to approximate the EIS with an artificial neural network (ANN) and an equivalent circuit model (ECM). Only four frequencies are used to estimate a whole spectrum. The lowest total distance between the measured and the estimated points, which is used as evaluation criterion, is at a value of 28. 10 -5. This as well as the visual results show the accuracy of the method and the potential to dramatically reduce the measurement time.
Date of Conference: 25-27 March 2024
Date Added to IEEE Xplore: 05 June 2024
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Conference Location: Bristol, United Kingdom

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