Validation of thermal imaging as a tool for failure mode detection development

Olarte, Javier; Dauvergne, Jean-Luc; Herrán, Alvaro; Drewett, Nicholas E.; Bekaert, Émilie; Zulueta, Ekaitz; Ferret, Raquel

doi:10.3934/energy.2019.5.646

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Thirdly, a machine learning model is trained to learn the relationship between the SOH of the battery and the extracted features. With the model trained, the final step is to implement it in a battery management system (BMS) for online application if needed [53,120].…”

Section: New Model Challenges and Opportunitiesmentioning

confidence: 99%

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

et al. 2023

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The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.

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Section: New Model Challenges and Opportunitiesmentioning

confidence: 99%

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

et al. 2023

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“…The summary of all improved results is shown in Table 5. This optimization combined the exploration capability of DE with the fine convergence of the Nelder-Mead Simplex method as shown in Equation (11), where the improvement metric was defined. The authors compared these two methods with the mean square error.…”

Section: Identified Parameter Setsmentioning

confidence: 99%

“…Improvement % = 100• J DEprediction − J DEoptim J DEprediction (11) In Table 5, the mean and standard deviation improvement values relating to one differential evolution optimization are shown. The improvement is related to the RMSE obtained from the two-step optimization compared with the RMSE obtained by only taking into account the first-step optimization using DE.…”

Section: Identified Parameter Setsmentioning

confidence: 99%

“…Possibly one of the positive messages of this report is the importance of collecting as much experimental data as possible to implement incremental improvements in the models. Indeed, the more relevant models, which range from simple one-dimensional models to highly complex multi-dimensional coupled ones, as suggested by Olarte et al [11], are be made by in operando electrochemical data, as in the case of this work.…”

Section: Introductionmentioning

confidence: 96%

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Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Olarte

Ilarduya²,

Zulueta

et al. 2021

Electronics

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Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.

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“…In this research, a refined approach linking thermal imaging to internal battery reactions is proposed to define early failure detection descriptors. This method has been experimentally proven using lead-acid batteries, highlighting the challenges of operando battery thermal imaging and the need for future iterative design advancements [14].…”

Section: Introductionmentioning

confidence: 99%

A Condition Monitoring System for Electric Vehicle Batteries Based on a Convolutional Neural Network Using Thermal Image

Senthilraj,

Shanker

2023

IJRITCC

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A new monitoring technique has been developed to evaluate the capacity and performance of Lithium-ion batteries batteries by utilizing two convolutional neural networks (CNNs) models, Deep convolutional neural network (DnCNN) and CNN with BFGS quasi-Newton optimization. The system utilizes thermal images of lithium-ion batteries as input for training and testing. DnCNN model is utilised to accurately calculate battery capacity and performance, and the performance is evaluated using mean squared error (MSE) and PSNR. The CNN-based training method employs the BFGS quasi-Newton algorithm to measure battery capacity accurately by evaluating the mean squared error (MSE) and regression results. The proposed condition monitoring system using thermal imaging and CNN models, specifically the CNN- BFGS quasi-Newton algorithm model, can accurately detect battery capacity with an accuracy rate of 98.5%, compared to the DnCNN model with an accuracy rate of 96.7%. The proposed system can address the critical issue of battery capacity and degradation in EVs, providing a more sustainable and efficient alternative for real-time applications.

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Validation of thermal imaging as a tool for failure mode detection development

Cited by 6 publications

References 23 publications

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

A Condition Monitoring System for Electric Vehicle Batteries Based on a Convolutional Neural Network Using Thermal Image

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