{"id":188810,"date":"2024-06-21T14:15:47","date_gmt":"2024-06-21T12:15:47","guid":{"rendered":"https:\/\/www.rse-web.it\/pubblicazioni\/an-online-state-of-health-estimation-method-for-lithium-ion-batteries-based-on-time-partitioning-and-data-driven-model-identification\/"},"modified":"2024-07-02T14:28:34","modified_gmt":"2024-07-02T12:28:34","slug":"an-online-state-of-health-estimation-method-for-lithium-ion-batteries-based-on-time-partitioning-and-data-driven-model-identification","status":"publish","type":"pubblicazioni","link":"https:\/\/www.rse-web.it\/en\/publications\/an-online-state-of-health-estimation-method-for-lithium-ion-batteries-based-on-time-partitioning-and-data-driven-model-identification\/","title":{"rendered":"An online state of health estimation method for lithium-ion batteries based on time partitioning and data-driven model identification"},"content":{"rendered":"<p class=\"last-updated-date\">Recently updated on July 2nd, 2024 at 02:28 pm<\/p>","protected":false},"excerpt":{"rendered":"<p>In recent years, the number of batteries used for smart grids and electric vehicles has been steadily increasing. To properly maintain these systems over time, it is essential to monitor the battery&#8217;s state of health (SoH). Standard techniques in the literature provide an accurate estimation of the state of health primarily through offline testing or with prior knowledge of model parameters. This paper proposes a new algorithm, the State of Health Estimator (SHE), which deduces the battery model online and uses this characterization to provide a reliable and accurate estimate of both the battery&#8217;s actual capacity and internal resistance.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1302,1315,1390],"targets":[1314],"pubblicazioni_tipologie":[773],"class_list":["post-188810","pubblicazioni","type-pubblicazioni","status-publish","hentry","tag-batteries","tag-smart-grids-en","tag-transportation-electric-mobility","targets-press-media-en","pubblicazioni_tipologie-isi-article-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":188409,"post_author":"93","post_date":"2024-06-13 15:10:21","post_date_gmt":"2024-06-13 13:10:21","post_content":"","post_title":"Electrochemical and thermal storage technologies","post_excerpt":"The goal of this project is to develop electrochemical and thermal storage technologies with improved performance and greater environmental and economic sustainability by optimizing the use of resources, material formulations and synthesis methods, diagnostic and control processes, and prototype design.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"electrochemical-and-thermal-storage-technologies","to_ping":"","pinged":"","post_modified":"2024-07-06 15:23:41","post_modified_gmt":"2024-07-06 13:23:41","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/electrochemical-and-thermal-storage-technologies\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"<p>In recent years, the number of batteries used for smart grids and electric vehicles has been steadily increasing. To properly maintain these systems over time, it is essential to monitor the battery&#8217;s state of health (SoH), determine when it is no longer useful for the current application, and potentially repurpose it in another context, a concept known as second-life battery. However, standard techniques in the literature provide an accurate estimation of the SoH primarily through offline testing or with prior knowledge of model parameters.<\/p>\n<p>&nbsp;<\/p>\n<p>This paper proposes a new algorithm, the State of Health Estimator (SHE), which deduces the battery model online, i.e., during its operational life, and uses this characterization to provide a reliable and accurate estimate of both the battery&#8217;s actual capacity and internal resistance, considering both the ohmic and polarization components. The experimental campaign, conducted on real data, shows satisfactory performance, with an average error of 1.2% and 4% in estimating the battery&#8217;s maximum capacity and internal resistance, respectively.<\/p>\n","scarica_file":false,"link_estreno":[{"link_text":"Download Publication","link":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352152X22014591"}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2022-11-15","autori":"M. Mussi, M. Restelli, F. Trov\u00f2 (Politecnico di Milano), L. Pellegrino (RSE S.p.A.)","destinazione":"Journal of Energy Storage, Vol. 55, Part B, N. 105467, November 15, 2022","rif_rse":"22008966"},"satellite_post_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An online state of health estimation method for lithium-ion batteries based on time partitioning and data-driven model identification - RSE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rse-web.it\/en\/publications\/an-online-state-of-health-estimation-method-for-lithium-ion-batteries-based-on-time-partitioning-and-data-driven-model-identification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An online state of health estimation method for lithium-ion batteries based on time partitioning and data-driven model identification - RSE\" \/>\n<meta property=\"og:description\" content=\"In recent years, the number of batteries used for smart grids and electric vehicles has been steadily increasing. 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