arXiv subjects

Simon Montoya-Bedoya

Publications and source records attributed to Simon Montoya-Bedoya.

3 recordsLinked to original sources

arXiv2026

Review of ultrasonic methods for monitoring, damage detection, and processing of lithium-ion batteries throughout their life-cycle

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

physics.app-ph↗

arXiv2023

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

cs.LG↗

arXiv2021

Noninvasive ultrasound for Lithium-ion batteries state estimation

Lithium-ion battery degradation estimation using fast and noninvasive techniques is a crucial issue in the circular economy framework of this technology. Currently, most of the approaches used to establish the battery-state (i.e., State of Charge (SoC), State of Health (SoH)) require time-consuming processes. In the present preliminary study, an ultrasound array was used to assess the influence of the SoC and SoH on the variations in the time of flight (TOF) and the speed of sound (SOS) of the ultrasound wave inside the batteries. Nine aged 18650 Lithium-ion batteries were imaged at 100% and 0% SoC using a Vantage-256 system (Verasonics, Inc.) equipped with a 64-element ultrasound array and a center frequency of 5 MHz (Imasonic SAS). It was found that second-life batteries have a complex ultrasound response due to the presence of many degradation pathways and, thus, making it harder to analyze the ultrasound measurements. Although further analysis must be done to elucidate a clear correlation between changes in the ultrasound wave properties and the battery state estimation, this approach seems very promising for future nondestructive evaluation of second-life batteries.

eess.SP↗