Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning

Bhattacharjee, Arnab; Verma, Abhishek; Mishra, Sukumar; Saha, Tapan Kumar

doi:10.1109/tvt.2021.3064287

Cited by 90 publications

(33 citation statements)

References 24 publications

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“…e affine optical flow vector for each pixel in the frame is as follows: (13) where f k (x + i, y + j), f k−1 (x, y) are the horizontal and vertical components of the affine optical flow, respectively. e quality of the motion feature information extracted from the convolutional neural network determines that the method is very important for accurate extraction and motion identification in the spatially weighted motion feature classifier [14][15][16].…”

Section: Technical Feature Extraction Of Long-distance Runningmentioning

confidence: 99%

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Wang

2022

Security and Communication Networks

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For long-distance running tactics, a computer vision-based motion correction solution is presented. The depth information is merged into the KCF algorithm to enhance it, which overcomes the classic KCF method’s incapacity to tackle the tracking drift issue caused by occlusion and extracts the technical features of long-distance running motions. Based on computer vision, the posture area of long-distance runners is detected, and the technical movements of long-distance runners are recognized. Calculate the centroid coordinates of the wrong technical movement correction area in long-distance running, and generate a tracking image of the wrong technical movement in long-distance running. The foreground and background information of the image is separated by the optical flow feature of machine vision, and the motion trajectory of the long-distance running error technique is extracted, and the motion correction of the long-distance running technique based on computer vision is realized. The experimental results show that the method in this paper has better accuracy in extracting long-distance running motion features and can accurately identify and correct the technical movements of the long-distance running, the technical movements of the head, and the technical movements of the body balance, and the correction efficiency is high.

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Section: Technical Feature Extraction Of Long-distance Runningmentioning

confidence: 99%

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Wang

2022

Security and Communication Networks

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“…Among all DL architectures compared in the study, the proposed transformer model achieved the lowest RMSE of 1.1075%, 1.3139% and 1.1914% and MAE of 0.4441%, 0.5680% and 0.6502% on the test drive cycles outperforming even the recurrent models which has been widely used for SOC estimation as shown in Table 2 . We also note that the convolutional models such as the Resnet 40 and the Inception Time 51 also outperformed the conventional GRU 41 and LSTM 52 model. The baseline Transformer model that is not trained with the proposed training framework scores poorly along with the feedforward DNN.…”

Section: Resultsmentioning

confidence: 77%

“…Secondly, the models that are trained on one cell chemistry do not apply to other cell chemistry. Even though preliminary work indicates that transfer learning is possible 40 , further tests are still required to verify its accuracy if it applies to more cells with differing chemistry. In most cases a model that is trained on one Li-ion battery cell data does not generalize well across another cell and may require re-training of the model from scratch.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan

How

Lipu

et al. 2021

Sci Rep

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Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

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“…Given a source domain, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {D}_{s}$ \end{document} and a corresponding task, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {T}_{s}$ \end{document} , and a target domain, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {D}_{t}$ \end{document} and a corresponding task, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {T}_{t}$ \end{document} , the objective of transfer learning is to improve the performance of a machine learning model in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {D}_{t}$ \end{document} using the knowledge acquired in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {D}_{s}$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {T}_{s}$ \end{document} [54] . Transfer learning has played a significant role in the facilitating the use of deep learning in numerous applications [55] – [57] . In this work, we empirically demonstrate how knowledge transfer is equally effective for vision transformer based framework in medical image classification.…”

Section: Proposed Methodsmentioning

confidence: 99%

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Mondal

Bhattacharjee

Singla

et al. 2022

IEEE J. Transl. Eng. Health Med.

Self Cite

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Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - . Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

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Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning

Cited by 90 publications

References 24 publications

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

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