{"id":5433,"date":"2023-11-22T10:39:55","date_gmt":"2023-11-22T08:39:55","guid":{"rendered":"https:\/\/howlab-dev.i3a.es\/?p=5433"},"modified":"2023-11-22T10:39:57","modified_gmt":"2023-11-22T08:39:57","slug":"antonio-bono-nuez","status":"publish","type":"post","link":"https:\/\/howlab.i3a.es\/es\/antonio-bono-nuez\/","title":{"rendered":"Antonio Bono Nuez"},"content":{"rendered":"<div id=\"pl-gb5433-69dedcdf77cdd\"  class=\"panel-layout\" ><div id=\"pg-gb5433-69dedcdf77cdd-0\"  class=\"panel-grid panel-has-style\" ><div class=\"siteorigin-panels-stretch panel-row-style panel-row-style-for-gb5433-69dedcdf77cdd-0\" data-stretch-type=\"full-width-stretch\" ><div id=\"pgc-gb5433-69dedcdf77cdd-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-gb5433-69dedcdf77cdd-0-0-0\" class=\"so-panel widget widget_sow-hero panel-first-child panel-last-child\" data-index=\"0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-hero so-widget-sow-hero-default-93415d0e2dbf-5433 so-widget-fittext-wrapper\"\n\t\t\t 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href=\"..\/team\/\">Investigadores<\/a><\/h3>\n<h1 style=\"text-align: center\"><strong>Antonio Bono Nuez<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t<ol class=\"sow-slider-pagination\">\n\t\t\t\t\t\t\t\t\t\t\t<li><a href=\"#\" data-goto=\"0\" aria-label=\"mostrar diapositiva 1\"><\/a><\/li>\n\t\t\t\t\t\t\t\t\t<\/ol>\n\n\t\t\t\t<div class=\"sow-slide-nav sow-slide-nav-next\">\n\t\t\t\t\t<a href=\"#\" data-goto=\"next\" aria-label=\"diapositiva siguiente\" data-action=\"next\">\n\t\t\t\t\t\t<em class=\"sow-sld-icon-thin-right\"><\/em>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\n\t\t\t\t<div class=\"sow-slide-nav sow-slide-nav-prev\">\n\t\t\t\t\t<a href=\"#\" data-goto=\"previous\" aria-label=\"diapositiva anterior\" data-action=\"prev\">\n\t\t\t\t\t\t<em class=\"sow-sld-icon-thin-left\"><\/em>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n\n<div id=\"pl-gb5433-69dedcdf78b7c\"  class=\"panel-layout\" ><div 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href=\"#\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttarget=\"_blank\" \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trel=\"noopener noreferrer\" \t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"37\" height=\"37\" src=\"https:\/\/howlab.i3a.es\/wp-content\/uploads\/2020\/10\/google-scholar.png\" class=\"sow-image-grid-image_html\" alt=\"\" title=\"\" srcset=\"https:\/\/howlab.i3a.es\/wp-content\/uploads\/2020\/10\/google-scholar.png 37w, https:\/\/howlab.i3a.es\/wp-content\/uploads\/2020\/10\/google-scholar-12x12.png 12w\" sizes=\"auto, (max-width: 37px) 100vw, 37px\" \/>\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n<\/div><\/div><\/div><\/div><div id=\"pgc-gb5433-69dedcdf78b7c-0-1\"  class=\"panel-grid-cell\" ><div id=\"panel-gb5433-69dedcdf78b7c-0-1-0\" class=\"so-panel widget widget_sow-editor panel-first-child panel-last-child\" data-index=\"2\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<blockquote>\n<p><em>Professor<\/em><\/p>\n<p><strong>Tel\u00e9fono:<\/strong> +34 976 761 927<\/p>\n<p><strong>Email:<\/strong> antoniob@unizar.es<\/p>\n<p><strong>Address:<\/strong> Dpto. IEC, Edif. Ada Byron, C\/ Mar\u00eda de Luna, 1 50018 (Zaragoza, Spain)<\/p>\n<\/blockquote>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n<div id=\"pl-gb5433-69dedcdf7a856\"  class=\"panel-layout\" ><div id=\"pg-gb5433-69dedcdf7a856-0\"  class=\"panel-grid panel-has-style\" ><div class=\"panel-row-style panel-row-style-for-gb5433-69dedcdf7a856-0\" ><div id=\"pgc-gb5433-69dedcdf7a856-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-gb5433-69dedcdf7a856-0-0-0\" class=\"so-panel widget widget_sow-headline panel-first-child\" data-index=\"0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-headline so-widget-sow-headline-default-c1f9b4bab95c-5433\"\n\t\t\t\n\t\t><div class=\"sow-headline-container\">\n\t\t\t\t\t\t\t<h5 class=\"sow-headline\">\n\t\t\t\t\t\tABOUT ME\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"decoration\">\n\t\t\t\t\t\t<div class=\"decoration-inside\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n<\/div><\/div><div id=\"panel-gb5433-69dedcdf7a856-0-0-1\" class=\"so-panel widget widget_sow-editor panel-last-child\" data-index=\"1\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<p>Antonio Bono ha desarrollado su carrera investigadora en el contexto de sistemas de machine learning implementados en dispositivos electr\u00f3nicos.<\/p>\n<p>Obtuvo el t\u00edtulo de Ingeniero T\u00e9cnico en Electr\u00f3nica Industrial en 1995 para despu\u00e9s entrar a trabajar como dise\u00f1ador hardware\/software en el departamento de I+D de la empresa Teltronic S.A.U. donde estuvo 4 a\u00f1os y 7 meses.<\/p>\n<p>En el a\u00f1o 2001 entro como profesor interino a tiempo completo en la Universidad de Zaragoza con responsabilidad docente desde el primer d\u00eda en la asignatura troncal obligatoria Sistemas electr\u00f3nicos basados en microprocesadores e Instrumentaci\u00f3n Electr\u00f3nica, a la vez que sigui\u00f3 form\u00e1ndose en Ingenier\u00eda Industrial.<\/p>\n<p>En 2006 concurs\u00f3 y obtuvo la plaza de profesor colaborador (contratado laboral a tiempo completo indefinido).<\/p>\n<p>En 2008 obtuvo el t\u00edtulo de Ingeniero Industrial con la menci\u00f3n de Electr\u00f3nica Industrial.<\/p>\n<p>En 2016 obtuvo el t\u00edtulo de doctor en el programa de Tecnolog\u00eda Electr\u00f3nica.<\/p>\n<p>En 2019 transform\u00f3 su plaza a profesor contratado doctor, donde contin\u00faa actualmente trabajando en la Escuela de Ingenier\u00eda y Arquitectura.<\/p>\n<p>Entre 2001 y 2016 colabor\u00f3 estrechamente y perteneci\u00f3 a los grupos Tecnodiscap y CVLab colabor\u00f3 aplicando t\u00e9cnicas de machine learning en diversos proyectos, entre los que destacan varios proyectos europeos asociados a la medici\u00f3n y mejora de la calidad de vida en ancianos y personas con discapacidad en sus hogares y otros centros.<\/p>\n<p>En 2017 se incorpor\u00f3 al grupo de HowLAB donde ha trabajado, entre otros, aplicando machine learning a proyectos que incluyen sistemas de gesti\u00f3n de energ\u00eda, lo que incluye modelado de bater\u00edas, estimaci\u00f3n de degradaci\u00f3n y sistemas de predicci\u00f3n de ca\u00eddas en instalaciones offgrid (donde ha sido Investigador Principal).<\/p>\n<p>Los m\u00e9ritos personales m\u00e1s destacados incluyen:<\/p>\n<ul>\n<li>10 publicaciones en revistas indexadas, 2 lecture notes y 35 comunicaciones a congresos. Publicaci\u00f3n en revistas relevantes, tales como IEEE Transactions on Industrial Electronics, Energies, Journal of Power Sources, IEEE Sensors Journal, Neural Computing and Applications y Journal of Ambient Intelligence and Smart Environments.<\/li>\n<li>En cuanto a los proyectos de investigaci\u00f3n ha sido el investigador principal de un proyecto y colaborador en 19 proyectos que incluyen tanto financiaci\u00f3n p\u00fablica como privada, as\u00ed como nacional e internacional.<\/li>\n<li>Derivado de la investigaci\u00f3n llevada a cabo en colaboraci\u00f3n con empresas privadas es\u00a0co-inventor en 2 patentes, una con BSH Electrodom\u00e9sticos.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/janovas.unizar.es\/sideral\/CV\/antonio-bono-nuez\">CV Universidad de Zaragoza<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><div id=\"pg-gb5433-69dedcdf7a856-1\"  class=\"panel-grid panel-has-style\" ><div class=\"panel-row-style panel-row-style-for-gb5433-69dedcdf7a856-1\" ><div 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\"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=inproceedings#tppubs\" >Proceedings Articles<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=misc#tppubs\" >Miscel\u00e1nea<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=phdthesis#tppubs\" >Tesis doctorales<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=techreport#tppubs\" >Informes t\u00e9cnicos<\/option>\r\n                <\/select><\/div><input type=\"hidden\" name=\"trp-form-language\" value=\"es\"\/><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">40 registros<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 de 8 <a href=\"https:\/\/howlab.i3a.es\/es\/antonio-bono-nuez\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"p\u00e1gina siguiente\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/howlab.i3a.es\/es\/antonio-bono-nuez\/?limit=8&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"\u00faltima p\u00e1gina\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2021\">2021<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Sanz-Gorrachategui, Iv\u00e1n;  Pastor-Flores, Pablo;  Pajovic, Milutin;  Wang, Ye;  Orlik, Philip V;  Bernal-Ruiz, Carlos;  Bono, Antonio;  Artal-Sevil, Jes\u00fas Sergio<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('416','tp_links')\" style=\"cursor:pointer;\">Remaining Useful Life Estimation for LFP Cells in Second-Life Applications<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">IEEE Transactions on Instrumentation and Measurement, <\/span><span class=\"tp_pub_additional_volume\">vol. 70, <\/span><span class=\"tp_pub_additional_pages\">pp. 1-10, <\/span><span class=\"tp_pub_additional_year\">2021<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1557-9662<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_416\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{9343322,<br \/>\r\ntitle = {Remaining Useful Life Estimation for LFP Cells in Second-Life Applications},<br \/>\r\nauthor = {Iv\u00e1n Sanz-Gorrachategui and Pablo Pastor-Flores and Milutin Pajovic and Ye Wang and Philip V Orlik and Carlos Bernal-Ruiz and Antonio Bono and Jes\u00fas Sergio Artal-Sevil},<br \/>\r\ndoi = {10.1109\/TIM.2021.3055791},<br \/>\r\nissn = {1557-9662},<br \/>\r\nyear  = {2021},<br \/>\r\ndate = {2021-01-01},<br \/>\r\njournal = {IEEE Transactions on Instrumentation and Measurement},<br \/>\r\nvolume = {70},<br \/>\r\npages = {1-10},<br \/>\r\nabstract = {The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in second-life applications. One of the challenges is to obtain an accurate remaining useful life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second-life cycles the battery will last. In this article, the RUL estimation problem is considered. We propose several health indicators (HIs), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as regularized logistic regression (RLR), and regression techniques such as multivariable linear regression (MLR) and multilayer perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent lithium iron phosphate (LFP) data set from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 root mean square error (RMSE) cycles for cells that live up to 1200 cycles, and 0.24% mean relative error (MRE) for the prediction of the evolution of capacity.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_416\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in second-life applications. One of the challenges is to obtain an accurate remaining useful life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second-life cycles the battery will last. In this article, the RUL estimation problem is considered. We propose several health indicators (HIs), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as regularized logistic regression (RLR), and regression techniques such as multivariable linear regression (MLR) and multilayer perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent lithium iron phosphate (LFP) data set from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 root mean square error (RMSE) cycles for cells that live up to 1200 cycles, and 0.24% mean relative error (MRE) for the prediction of the evolution of capacity.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_416\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/TIM.2021.3055791\" title=\"DOI de seguimiento:10.1109\/TIM.2021.3055791\" target=\"_blank\">doi:10.1109\/TIM.2021.3055791<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2020\">2020<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Guill\u00e9n-Asensio, A;  Sanz-Gorrachategui, I;  Pastor-Flores, P;  Artal-Sevil, J S;  Bono, Antonio;  Br\u00edo, Bonifacio Mart\u00edn;  Bernal-Ruiz, C<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('417','tp_links')\" style=\"cursor:pointer;\">Battery state prediction in photovoltaic standalone installations<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_booktitle\">2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), <\/span><span class=\"tp_pub_additional_pages\">pp. 1-6, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_417\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('417','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_417\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('417','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_417\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('417','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_417\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{9243111,<br \/>\r\ntitle = {Battery state prediction in photovoltaic standalone installations},<br \/>\r\nauthor = {A Guill\u00e9n-Asensio and I Sanz-Gorrachategui and P Pastor-Flores and J S Artal-Sevil and Antonio Bono and Bonifacio Mart\u00edn Br\u00edo and C Bernal-Ruiz},<br \/>\r\ndoi = {10.1109\/EVER48776.2020.9243111},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-09-01},<br \/>\r\nbooktitle = {2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)},<br \/>\r\npages = {1-6},<br \/>\r\nabstract = {Effective energy treatment is one of the challenges to which scientists are devoting more interest and resources. Currently, there is a need to optimize the generation of energy by renewable methods and to improve the storage and management of battery cells. One of these emerging lines aims to face the problem of uncertainty that exists in the generation of energy in photovoltaic installations. In this paper, we proposed to use machine learning methods to predict the state (voltage) of the batteries at several days into the future. The results obtained from two recurrent neural networks such as NARX and LSTM are compared, getting good results with both. Two approaches are considered: sample-based prediction and pattern-based forecasting.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('417','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_417\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Effective energy treatment is one of the challenges to which scientists are devoting more interest and resources. Currently, there is a need to optimize the generation of energy by renewable methods and to improve the storage and management of battery cells. One of these emerging lines aims to face the problem of uncertainty that exists in the generation of energy in photovoltaic installations. In this paper, we proposed to use machine learning methods to predict the state (voltage) of the batteries at several days into the future. The results obtained from two recurrent neural networks such as NARX and LSTM are compared, getting good results with both. Two approaches are considered: sample-based prediction and pattern-based forecasting.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('417','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_417\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/EVER48776.2020.9243111\" title=\"DOI de seguimiento:10.1109\/EVER48776.2020.9243111\" target=\"_blank\">doi:10.1109\/EVER48776.2020.9243111<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('417','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Artal-Sevil, J S;  Sanz-Gorrachategui, I;  Pastor, P;  Bernal-Ruiz, C;  Perez-Cebolla, F J;  Bono, Antonio<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('418','tp_links')\" style=\"cursor:pointer;\">New Smart Control based on MPPT\/MEPT Algorithm for Hybrid Fuel Cell Power System<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_booktitle\">2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), <\/span><span class=\"tp_pub_additional_pages\">pp. 1-10, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_418\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('418','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_418\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('418','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_418\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('418','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_418\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{9243139,<br \/>\r\ntitle = {New Smart Control based on MPPT\/MEPT Algorithm for Hybrid Fuel Cell Power System},<br \/>\r\nauthor = {J S Artal-Sevil and I Sanz-Gorrachategui and P Pastor and C Bernal-Ruiz and F J Perez-Cebolla and Antonio Bono},<br \/>\r\ndoi = {10.1109\/EVER48776.2020.9243139},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-09-01},<br \/>\r\nbooktitle = {2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)},<br \/>\r\npages = {1-10},<br \/>\r\nabstract = {In this paper, a new algorithm for enhanced management of a hybrid fuel-cell system is proposed. The purpose of this algorithm, which has been called Maximum Efficiency Point Tracking algorithm (MEPT-algorithm), is to polarize the fuel cell in an optimum point maximizing its efficiency and minimizing the fuel consumption. The goal is to guarantee fast power transients in the load while the fuel cell is kept in the optimum point by the hybridization of a fuel cell with an energy storage system (ESS) based on ultracapacitors. The hybrid power system model has been simulated in Matlab\/Simulink applying the proposed algorithm and the classical Maximum Power Point Tracking (MPPT) algorithm. The combined control in DC\/DC converters allows us to modify the Fuel Cell's operating point, as well as its MPPT or MEPT algorithm. A detailed analysis of the results verifies the benefits of the use of MEPT vs only the MPPT. Typical figures of merit and conclusions are also presented in this paper.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('418','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_418\" style=\"display:none;\"><div class=\"tp_abstract_entry\">In this paper, a new algorithm for enhanced management of a hybrid fuel-cell system is proposed. The purpose of this algorithm, which has been called Maximum Efficiency Point Tracking algorithm (MEPT-algorithm), is to polarize the fuel cell in an optimum point maximizing its efficiency and minimizing the fuel consumption. The goal is to guarantee fast power transients in the load while the fuel cell is kept in the optimum point by the hybridization of a fuel cell with an energy storage system (ESS) based on ultracapacitors. The hybrid power system model has been simulated in Matlab\/Simulink applying the proposed algorithm and the classical Maximum Power Point Tracking (MPPT) algorithm. The combined control in DC\/DC converters allows us to modify the Fuel Cell&#8217;s operating point, as well as its MPPT or MEPT algorithm. A detailed analysis of the results verifies the benefits of the use of MEPT vs only the MPPT. Typical figures of merit and conclusions are also presented in this paper.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('418','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_418\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/EVER48776.2020.9243139\" title=\"DOI de seguimiento:10.1109\/EVER48776.2020.9243139\" target=\"_blank\">doi:10.1109\/EVER48776.2020.9243139<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('418','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Artal-Sevil, J S;  Bernal-Ruiz, C;  Bono, Antonio;  Pe\u00f1as, Santos M<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('419','tp_links')\" style=\"cursor:pointer;\">Design of a Fuzzy-Controller for a Magnetic Levitation System using Hall-Effect sensors<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_booktitle\">2020 XIV Technologies Applied to Electronics Teaching Conference (TAEE), <\/span><span class=\"tp_pub_additional_pages\">pp. 1-9, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_419\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('419','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_419\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('419','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_419\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('419','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_419\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{9163711,<br \/>\r\ntitle = {Design of a Fuzzy-Controller for a Magnetic Levitation System using Hall-Effect sensors},<br \/>\r\nauthor = {J S Artal-Sevil and C Bernal-Ruiz and Antonio Bono and Santos M Pe\u00f1as},<br \/>\r\ndoi = {10.1109\/TAEE46915.2020.9163711},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-07-01},<br \/>\r\nbooktitle = {2020 XIV Technologies Applied to Electronics Teaching Conference (TAEE)},<br \/>\r\npages = {1-9},<br \/>\r\nabstract = {This paper shows the implementation of Fuzzy Control techniques on a small Magnetic Levitation system. The analyzed application is a study problem of a non-linear and unstable system. Fuzzy control is an appropriate alternative for those cases where modelling the system is complicated, mainly due to the mathematical complexity of the model. The advantage of Fuzzy techniques is that any additional adjustment in the system only requires modifying the different fuzzy rules implemented, instead of redesigning the entire associated controller again. The control implemented here is made up of two feedback loops (position and current variables). A Hall sensor has been used to determine the position of the sphere to be levitated. Matlab\/Simulink has been the software used for the development of the model and its simulation. In this way, the dynamic response of the system has been studied. The results and the different figures of merit obtained are shown by simulating the model presented. The initial objectives have been satisfactorily achieved.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('419','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_419\" style=\"display:none;\"><div class=\"tp_abstract_entry\">This paper shows the implementation of Fuzzy Control techniques on a small Magnetic Levitation system. The analyzed application is a study problem of a non-linear and unstable system. Fuzzy control is an appropriate alternative for those cases where modelling the system is complicated, mainly due to the mathematical complexity of the model. The advantage of Fuzzy techniques is that any additional adjustment in the system only requires modifying the different fuzzy rules implemented, instead of redesigning the entire associated controller again. The control implemented here is made up of two feedback loops (position and current variables). A Hall sensor has been used to determine the position of the sphere to be levitated. Matlab\/Simulink has been the software used for the development of the model and its simulation. In this way, the dynamic response of the system has been studied. The results and the different figures of merit obtained are shown by simulating the model presented. The initial objectives have been satisfactorily achieved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('419','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_419\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/TAEE46915.2020.9163711\" title=\"DOI de seguimiento:10.1109\/TAEE46915.2020.9163711\" target=\"_blank\">doi:10.1109\/TAEE46915.2020.9163711<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('419','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2019\">2019<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Pastor-Flores, Pablo;  Bernal-Ruiz, Carlos;  Sanz-Gorrachategui, Iv\u00e1n;  Bono, Antonio;  Br\u00edo, Bonifacio Mart\u00edn;  Artal-Sevil, Jes\u00fas Sergio;  Perez-Cebolla, Francisco J<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('424','tp_links')\" style=\"cursor:pointer;\">Analysis of Li-ion battery degradation using self-organizing maps<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_booktitle\">IECON 2019 &#8211; 45th Annual Conference of the IEEE Industrial Electronics Society, <\/span><span class=\"tp_pub_additional_pages\">pp. 4525-4530, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2577-1647<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_424\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('424','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_424\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('424','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_424\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('424','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_424\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{8926907,<br \/>\r\ntitle = {Analysis of Li-ion battery degradation using self-organizing maps},<br \/>\r\nauthor = {Pablo Pastor-Flores and Carlos Bernal-Ruiz and Iv\u00e1n Sanz-Gorrachategui and Antonio Bono and Bonifacio Mart\u00edn Br\u00edo and Jes\u00fas Sergio Artal-Sevil and Francisco J Perez-Cebolla},<br \/>\r\ndoi = {10.1109\/IECON.2019.8926907},<br \/>\r\nissn = {2577-1647},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-10-01},<br \/>\r\nbooktitle = {IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society},<br \/>\r\nvolume = {1},<br \/>\r\npages = {4525-4530},<br \/>\r\nabstract = {This paper proposes a new methodology to identify the different degradation processes of Li-Ion battery cells. The goal of this study is to determine if different degradation factors can be separated by waveform analysis from aged cells with similar remaining capacity. In contrast to other works, the proposed method identifies the past operating conditions in the cell, regardless of the actual State of Health. The methodology is based on a data-driven approach by using a SOM (Self-organizing map), an unsupervised neural network. To verify the hypothesis a SOM has been trained with laboratory data from whole data cycles, to classify cells concerning their degradation path and according to their discharge voltage patterns. Additionally, this new methodology based on the SOM allows discriminating groups of cells with different cycling conditions (based on depth of discharge, ambient temperature and discharge current). This research line is very promising for classification of used cells, not only depending on their current static parameters (capacity, impedance), but also the battery use in their past life. This will allow making predictions of the Remaining Useful Life (RUL) of a battery with greater precision.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('424','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_424\" style=\"display:none;\"><div class=\"tp_abstract_entry\">This paper proposes a new methodology to identify the different degradation processes of Li-Ion battery cells. The goal of this study is to determine if different degradation factors can be separated by waveform analysis from aged cells with similar remaining capacity. In contrast to other works, the proposed method identifies the past operating conditions in the cell, regardless of the actual State of Health. The methodology is based on a data-driven approach by using a SOM (Self-organizing map), an unsupervised neural network. To verify the hypothesis a SOM has been trained with laboratory data from whole data cycles, to classify cells concerning their degradation path and according to their discharge voltage patterns. Additionally, this new methodology based on the SOM allows discriminating groups of cells with different cycling conditions (based on depth of discharge, ambient temperature and discharge current). This research line is very promising for classification of used cells, not only depending on their current static parameters (capacity, impedance), but also the battery use in their past life. This will allow making predictions of the Remaining Useful Life (RUL) of a battery with greater precision.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('424','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_424\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/IECON.2019.8926907\" title=\"DOI de seguimiento:10.1109\/IECON.2019.8926907\" target=\"_blank\">doi:10.1109\/IECON.2019.8926907<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('424','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><\/div><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">40 registros<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 de 8 <a href=\"https:\/\/howlab.i3a.es\/es\/antonio-bono-nuez\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"p\u00e1gina siguiente\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/howlab.i3a.es\/es\/antonio-bono-nuez\/?limit=8&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"\u00faltima p\u00e1gina\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div>\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":3600,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[238,239],"tags":[],"class_list":["post-5433","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-reseacher","category-team"],"_links":{"self":[{"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/posts\/5433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/comments?post=5433"}],"version-history":[{"count":1,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/posts\/5433\/revisions"}],"predecessor-version":[{"id":5434,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/posts\/5433\/revisions\/5434"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/media\/3600"}],"wp:attachment":[{"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/media?parent=5433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/categories?post=5433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/howlab.i3a.es\/es\/wp-json\/wp\/v2\/tags?post=5433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}