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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:20261004T070804Z
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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