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UID:pretalx-amfc2026-YNEHY3@modelica.simtek.cc
DTSTART;TZID=CST:20260920T160000
DTEND;TZID=CST:20260920T180000
DESCRIPTION:Thermal management modeling and simulation have become critical
  to electric vehicle (EV) development. Modelica is well-suited for this do
 main thanks to its multi-disciplinary\, acausal modeling capabilities\, wh
 ich naturally capture the physical behavior of batteries\, electric motors
 \, power electronics\, and their associated cooling systems.\nThe Thermofl
 uidStream (TFS)​ library\, originally developed at DLR\, introduces a no
 vel modeling paradigm in Modelica that enables efficient simulation of lar
 ge-scale thermal-fluid network models. YSLAB\, developed by Nanjing Yuansi
  SimTek\, is a browser-based Modelica simulation environment that lowers t
 he barrier to entry for system-level modeling and interactive exploration.
 \nThis tutorial is structured in two parts. We begin with an introduction 
 to thermodynamics modeling at the component level using YSLAB. Participant
 s will then engage in a hands-on session to build an EV thermal management
  system model using the TFS library\, from component instantiation to syst
 em assembly and simulation.
DTSTAMP:20261004T070643Z
LOCATION:(Electric) Mobility & Buildings (R2003)
SUMMARY:Workshop 3: EV thermal management modeling practice with TFS and YS
 SIM.YSLAB - Junjie Chen
URL:https://modelica.simtek.cc/amfc2026/talk/YNEHY3/
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UID:pretalx-amfc2026-P7TTP8@modelica.simtek.cc
DTSTART;TZID=CST:20260922T135000
DTEND;TZID=CST:20260922T141500
DESCRIPTION:New Energy Vehicles (NEVs) drive China's automotive market. How
 ever\, calibrating thermal management for the battery\, motor\, and invert
 er is a major bottleneck\, especially as development cycles shrink to 24 m
 onths. High-fidelity thermal-fluid models are too slow for repeated calibr
 ation and fixed-step real-time execution. This paper presents an end-to-en
 d industrial workflow: 1) Build high-fidelity models using 3D data\; 2) Tr
 ain reduced-order models (ROMs)\, implemented here as nonintrusive data-dr
 iven surrogate models\, from simulation data\; 3) Export the trained surro
 gates as FMI-compliant co-simulation FMUs for NI VeriStand real-time testi
 ng. The ROM tool proves efficient for repeated calibration runs\; in this 
 paper\, ROM refers to a non-intrusive datadriven surrogate model exported 
 as an FMI-compliant cosimulation FMU\, rather than a projection-based or e
 quation-level model-order-reduction method. The reported speedup is interp
 reted as repeated-simulation acceleration\, with data generation and train
 ing treated as one-time amortized costs. For the reported battery validati
 on cases\, temperature RMSE was evaluated against experimental or CAE refe
 rence data and normalized by the initial temperature of the battery heat-g
 eneration operating condition. The calculation results show that the singl
 e-cell RMSE is within 5%\, the battery-pack RMSE is within 10%\, and the a
 bsolute temperature error is within 3 K. For a representative 10-module ba
 ttery-pack cooling case\, the 250 s closed-loop simulation time was reduce
 d from 2200 s to 44 s after surrogate-FMU replacement\, and the calibrated
  PI response met the control targets.
DTSTAMP:20261004T070643Z
LOCATION:(Electric) Mobility & Buildings (R2003)
SUMMARY:Calibration workflow development using FMI-based reduced-order mode
 ls for NEVs thermal management - Rui Gao\, Yuhao Xu\, Junjie Chen\, Junyan
 g Hou\, Weilin Li\, Da Li\, Yang Qi\, Yue Tang
URL:https://modelica.simtek.cc/amfc2026/talk/P7TTP8/
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