Monitoring lead-acid battery function using operando neutron radiography

Campillo-Robles, Jose Miguel; Goonetilleke, Damian; Soler, D.; Sharma, Neeraj; Rodríguez, Damian Martín; Bücherl, T.; Makowska, Małgorzata; Türkilmaz, Pinar; Karahan, Volkan

doi:10.1016/j.jpowsour.2019.226976

Cited by 13 publications

(9 citation statements)

References 61 publications

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“…. ., T. 1 Train the sample, and obtain the weak learning machine f t (x). Calculate the maximum error…”

Section: Adaboost Algorithmmentioning

confidence: 99%

“…Then, as the number of nodes at the hidden layer contin grow, it does not have a significant influence on the accuracy of the model, but th putational complexity is increased. Therefore, the sig function is used as the act function for the OSELM model, and the number of nodes at the hidden layer is set After determining the activation function f and the number of hidden layer number p of new samples required for each update of the OSELM algorithm sho decided, assuming p is in the range of [1,80], and p gradually increases by 10 nod accuracy/ ( %) It can be seen that when the sig function is used as the activation function, the obtained prediction results are the most optimal. When the number of nodes at the hidden layer is 80, the accuracies on both the training set and the test set reach the highest values without further increase.…”

Section: Selection Of Model Parametersmentioning

confidence: 99%

“…Due to advantages in terms of mature production technology, high reliability, low production cost, and strong environmental applicability, lead-acid batteries have broad applications in various fields such as starters and portable power for vehicles, storage of excess energy from renewable sources, and backup or uninterrupted power supply systems [1]. Especially in terms of large-scale energy storage, lead-acid batteries remain to this day the most commonly found battery technology in operating microgrids [2].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Sun¹,

Zhang

Sun³

et al. 2022

Energies

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Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

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“…. ., T. 1 Train the sample, and obtain the weak learning machine f t (x). Calculate the maximum error…”

Section: Adaboost Algorithmmentioning

confidence: 99%

Section: Selection Of Model Parametersmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

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Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Sun¹,

Zhang

Sun³

et al. 2022

Energies

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“…Previous in operando neutron attenuation imaging of a lithium pouch cell showed the possibility of time-resolved tomography as a battery undergoes charging/discharging [25]. Campillo-Robles et al provided the first neutron radiography images of lead-acid batteries sharing the challenges behind observing changes in eletrolytic concentrations due to a lack of sufficient contrast [26]. Several significant works have used neutron imaging to observe lithium concentrations and mobilities [27,28], lithium hydride and hydrolysis products [29], and lithium transport in battery fuel cells with sintered electrodes [30].…”

Section: Introductionmentioning

confidence: 99%

Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction

et al. 2022

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We survey several neutron imaging and diffraction methods for non-destructive testing and evaluation of intact, commercial lithium-ion batteries. Specifically, far-field interferometry was explored as an option to probe a wide range of autocorrelation lengths within the batteries via neutron imaging. The dark-field interferometry images change remarkably from fresh to worn batteries, and from charged to discharged batteries. When attempting to search for visual evidence of battery degradation, neutron Talbot-Lau grating interferometry exposed battery layering and particle scattering through dark-field imaging. Bragg edge imaging also reveals battery wear and state of charge. Neutron diffraction observed chemical changes between fresh and worn, charged and discharged batteries. However, the utility of these methods, for commercial batteries, is dependent upon battery size and shape, with 19 to 43 mAh prismatic batteries proving most convenient for these experimental methods. This study reports some of the first spatially resolved, small angle scattering (dark-field) images showing battery degradation.

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“…Thermal neutrons are usually used for NR imaging because most materials show higher attenuation for low energy neutrons. For this reason, thermal NR is the most popular NR NDT method used in many fields of research and industry such as security applications [1]- [9] medicine [10][11][12][13][14][15][16][17], material sciences [18][19][20][21][22][23][24][25][26][27][28][29], geology [30], archeology [31][32] etc. A suitable neutron beam is necessary in order to establish a thermal NR facility.…”

Section: Introductionmentioning

confidence: 99%

Development of a Neutron Radiography System based on a 10 MeV Electron Linac

Fantidis

Nicolaou

2020

mjee

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A thermal neutron radiography unit using the neutrons which emits a 10 MeV electron linac compact has been designed and simulated via MCNPX Monte Carlo code. The facility was carried out for an extensive range of values for the collimator ratio L/D, the main parameter which describes the quality of the produced radiographic images. The results show that the presented facility provides high thermal neutron flux; while with the use of single sapphire filter fulfills all the suggested values which characterize a high quality thermal neutron radiography system. A comparison with other similar facilities indicates that the use of a photoneutron source using a 10 MeV electrons beam is a useful substitutional for radiographic purposes.

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Monitoring lead-acid battery function using operando neutron radiography

Cited by 13 publications

References 61 publications

Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction

Development of a Neutron Radiography System based on a 10 MeV Electron Linac

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