{"id":1014,"date":"2024-02-12T21:16:26","date_gmt":"2024-02-13T02:16:26","guid":{"rendered":"https:\/\/sites.temple.edu\/dslab\/?p=1014"},"modified":"2024-04-04T14:18:46","modified_gmt":"2024-04-04T18:18:46","slug":"omidrezas-paper-to-appear-in-the-journal-of-energy-storage","status":"publish","type":"post","link":"https:\/\/sites.temple.edu\/dslab\/?p=1014","title":{"rendered":"Publication Alert: Omidreza&#8217;s paper to appear in the Journal of Energy Storage"},"content":{"rendered":"\n<p><strong>Title:<\/strong> A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge<\/p>\n\n\n\n<p><strong>Abstract:<\/strong> This paper presents a reduced-order nonlinear model for Lithium-ion batteries (LiBs). Unlike mechanistic models, data-driven models offer accurate representations of system dynamics without relying on in-situ measurements and proprietary information. However, these models may perform poorly in unseen scenarios due to overfitting training data, which is typical. We propose a physics-inspired, data-driven approach to determine LiBs governing equations based on their electrochemistry rather than generic terms. We employ a sparse identification method achieved through sequentially thresholding ridge regression to construct a nonlinear model from electrical current (excitation input) and measured voltage. We formulate the problem to optimize the sparsification parameters as hyperparameters and minimize a cost function comprised of training and validation sets and the number of terms as a measure of complexity. We augment the model with a joint unscented Kalman filter to handle noisy experimental data, enabling a more accurate estimate of the state of charge (SOC) and voltage. Model performance in unseen scenarios is evaluated with urban dynamometer driving schedule (UDDS) data, where the identified model achieves a root mean square error of 1.26e\u22122 for SOC and voltage prediction.<\/p>\n\n\n\n<p>Ahmadzadeh, O., Wang, Y., &amp; Soudbakhsh, D. (2024). A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge. <em>Journal of Energy Storage<\/em>, <a href=\"https:\/\/doi.org\/10.1016\/j.est.2024.110743\">https:\/\/doi.org\/10.1016\/j.est.2024.110743<\/a><\/p>\n\n\n\n<a href=\"https:\/\/www.scimagojr.com\/journalsearch.php?q=21100400826&amp;tip=sid&amp;exact=no\" title=\"SCImago Journal &amp; Country Rank\"><img decoding=\"async\" border=\"0\" src=\"https:\/\/www.scimagojr.com\/journal_img.php?id=21100400826\" alt=\"SCImago Journal &amp; Country Rank\" \/><\/a>\n","protected":false},"excerpt":{"rendered":"<p>Title: A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge Abstract: This paper presents a reduced-order nonlinear model for Lithium-ion batteries (LiBs). Unlike mechanistic models, data-driven models offer accurate representations of system dynamics without relying on in-situ measurements and proprietary information. However, these models may perform poorly in unseen &hellip; <a href=\"https:\/\/sites.temple.edu\/dslab\/?p=1014\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Publication Alert: Omidreza&#8217;s paper to appear in the Journal of Energy Storage<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":18182,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[13],"tags":[],"class_list":["post-1014","post","type-post","status-publish","format-standard","hentry","category-news2024"],"acf":[],"_links":{"self":[{"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/posts\/1014","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/users\/18182"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1014"}],"version-history":[{"count":3,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions"}],"predecessor-version":[{"id":1075,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions\/1075"}],"wp:attachment":[{"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1014"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1014"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.temple.edu\/dslab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1014"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}