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Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Temporal Convolutional Network for Remaining Useful Life Prediction of Lithium Ion Batteries
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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Temporal Convolutional Network for Remaining Useful Life Prediction of Lithium Ion Batteries</title> <meta name="description" content="Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Temporal Convolutional Network for Remaining Useful Life Prediction of Lithium Ion Batteries"> <meta name="keywords" content="lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link 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href="https://publications.waset.org/abstracts/search?q=Xinghua%20Zhu"> Xinghua Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Delong%20Li"> Delong Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle鈥檚 power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system鈥檚 reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure鈥檚 strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model鈥檚 generalizability and be extremely useful in industrial settings. <iframe src="https://publications.waset.org/abstracts/157453.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lithium-ion%20battery" title="lithium-ion battery">lithium-ion battery</a>, <a href="https://publications.waset.org/abstracts/search?q=remaining%20useful%20life" title=" remaining useful life"> remaining useful life</a>, <a href="https://publications.waset.org/abstracts/search?q=complete%20EEMD%20with%20adaptive%20noise" title=" complete EEMD with adaptive noise"> complete EEMD with adaptive noise</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20convolutional%20net" title=" temporal convolutional net"> temporal convolutional net</a> </p> <a href="https://publications.waset.org/abstracts/157453/complete-ensemble-empirical-mode-decomposition-with-adaptive-noise-temporal-convolutional-network-for-remaining-useful-life-prediction-of-lithium-ion-batteries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157453.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a 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