BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Amsterdam BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251223T152717Z UID:43D368C7-81E4-48C4-9414-4C8D3C5DC503 DTSTART;TZID=Europe/Amsterdam:20251216T160000 DTEND;TZID=Europe/Amsterdam:20251216T170000 DESCRIPTION:--Technical Webinar Series by IEEE Benelux Section Transportati on Electrification Council Chapter--\n\nTopic: Statistically Designed Powe r Processing Architecture for Second-Life Batteries\n\nSpeaker: Dr. Xiaofa n Cui\, Assistant Professor\, University of California\, Los Angeles (UCLA )\n\n[]\n\nBio: Xiaofan Cui is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California\, Los Angeles (UCLA). He was a postdoctoral researcher at Stanford University f rom 2022 to 2023. Dr. Cui received his bachelor's degree in Electrical Eng ineering and a dual bachelor's degree in Economics from Tsinghua Universit y in 2016. He earned his Master's degree in Mathematics and his Ph.D. in E lectrical and Computer Engineering from the University of Michigan\, Ann A rbor\, in 2022. His research interests include the modeling\, control\, an d design of energy systems and high-performance power electronics. He has received the IEEE Power Electronics Society Best Paper Award\, Hellman Fel low Award\, UCLA Innovation Faculty Fellowship\, the Michigan Translationa l Research and Commercialization Award\, and the Towner Prize for Distingu ished Academic Achievement.\n\nSUMMARY: Second-life battery energy storage systems (SL-BESS) have emerged as a cost-effective and sustainable soluti on to the rapidly growing supply of retired electric-vehicle batteries. Un like new batteries\, however\, second-life batteries exhibit highly stocha stic availability and largely heterogeneous cell characteristics. Without proper management\, these variations can lead to severe performance degrad ation. Moreover\, conventional power-electronics architectures for managin g SL batteries become inefficient and costly under such conditions\, under mining the economic value of SL-BESS.\n\nIn this talk\, I will begin by su mmarizing key insights gained from real experiments on commercially retire d EV batteries\, tested in a laboratory environment that emulates grid usa ge. I will then introduce a statistically designed partial-power-processin g architecture to mitigate the stochasticity\, heterogeneity\, and thereby substantially reduce balancing costs. The underlying design principles ge neralize across a wide range of applications\, including DC microgrids\, b ehind-the-meter storage\, and utility-scale energy storage. Validated thro ugh Monte Carlo analysis\, statistical hardware-in-the-loop simulations\, and hardware demonstrations\, the proposed architecture consistently outpe rforms conventional designs across diverse battery configurations.\n\nVirt ual: https://events.vtools.ieee.org/m/518385 LOCATION:Virtual: https://events.vtools.ieee.org/m/518385 ORGANIZER:z.qin-2@tudelft.nl SEQUENCE:32 SUMMARY:Statistically Designed Power Processing Architecture for Second-Lif e Batteries URL;VALUE=URI:https://events.vtools.ieee.org/m/518385 X-ALT-DESC:Description: <br /><p class="MsoNormal"><span style="font-family : arial\, helvetica\, sans-serif\;"><strong>--Technical Webinar Series by IEEE Benelux Section Transportation Electrification Council Chapter--</str ong></span></p>\n<p class="MsoNormal"><span style="font-size: 12pt\; font- family: arial\, helvetica\, sans-serif\;"><strong>Topic: Statistically Des igned Power Processing Architecture for Second-Life Batteries</strong></sp an></p>\n<p class="MsoNormal"><span style="font-size: 12pt\; font-family: arial\, helvetica\, sans-serif\;"><strong>Speaker: <span lang="EN-US">Dr. Xiaofan Cui\, Assistant Professor\, University of California\, Los Angeles (UCLA)</span></strong></span></p>\n<p class="MsoNormal"><span lang="EN-GB "><img src="https://events.vtools.ieee.org/vtools_ui/media/display/9f572b7 f-45ef-4123-814c-12b34848a42a" alt="" width="257" height="228"></span></p> \n<p class="MsoNormal"><span style="font-family: arial\, helvetica\, sans- serif\;"><span style="font-size: 12pt\;"><span lang="EN-GB"><strong>Bio:</ strong> </span></span>Xiaofan Cui is an Assistant Professor in the Departm ent of Electrical and Computer Engineering at the University of California \, Los Angeles (UCLA). He was a postdoctoral researcher at Stanford Univer sity from 2022 to 2023. Dr. Cui received his bachelor's degree in Electric al Engineering and a dual bachelor's degree in Economics from Tsinghua Uni versity in 2016. He earned his Master's degree in Mathematics and his Ph.D . in Electrical and Computer Engineering from the University of Michigan\, Ann Arbor\, in 2022. His research interests include the modeling\, contro l\, and design of energy systems and high-performance power electronics. H e has received the IEEE Power Electronics Society Best Paper Award\, Hellm an Fellow Award\, UCLA Innovation Faculty Fellowship\, the Michigan Transl ational Research and Commercialization Award\, and the Towner Prize for Di stinguished Academic Achievement.</span></p>\n<p class="MsoNormal"><span s tyle="font-family: arial\, helvetica\, sans-serif\;"><span style="font-siz e: 12pt\;"><strong><span lang="EN-GB">SUMMARY</span></strong><span lang="E N-GB">: </span></span>Second-life battery energy storage systems (SL-BESS) have emerged as a cost-effective and sustainable solution to the rapidly growing supply of retired electric-vehicle batteries. Unlike new batteries \, however\, second-life batteries exhibit highly stochastic availability and largely heterogeneous cell characteristics. Without proper management\ , these variations can lead to severe performance degradation. Moreover\, conventional power-electronics architectures for managing SL batteries bec ome inefficient and costly under such conditions\, undermining the economi c value of SL-BESS.</span></p>\n<p><span style="font-family: arial\, helve tica\, sans-serif\;">In this talk\, I will begin by summarizing key insigh ts gained from real experiments on commercially retired EV batteries\, tes ted in a laboratory environment that emulates grid usage. I will then intr oduce a statistically designed partial-power-processing architecture to mi tigate the stochasticity\, heterogeneity\, and thereby substantially reduc e balancing costs. The underlying design principles generalize across a wi de range of applications\, including DC microgrids\, behind-the-meter stor age\, and utility-scale energy storage. Validated through Monte Carlo anal ysis\, statistical hardware-in-the-loop simulations\, and hardware demonst rations\, the proposed architecture consistently outperforms conventional designs across diverse battery configurations.</span></p> END:VEVENT END:VCALENDAR