BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//modelica.simtek.cc//amfc2026//speaker//CD3TKX
BEGIN:VTIMEZONE
TZID:CST
BEGIN:STANDARD
DTSTART:20000101T000000
RRULE:FREQ=YEARLY;BYMONTH=1
TZNAME:CST
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-amfc2026-TGUR3V@modelica.simtek.cc
DTSTART;TZID=CST:20260921T153500
DTEND;TZID=CST:20260921T160000
DESCRIPTION:In practical engineering\, equipment parameters are difficult t
 o obtain and measurement data is limited. This paper proposes a PINN-Model
 ica data-driven modeling method that couples a neural network with a Model
 ica model encapsulated as an FMU. Instead of directly constructing governi
 ng-equation residuals in the proposed method\, the FMU supplies a modular 
 physicsbased constraint during training. The neural network predicts both 
 terminal voltage and time-varying physical parameters\, while the FMU comp
 utes a physics-based voltage from the predicted parameters. Data and physi
 cs losses jointly constrain the neural-network voltage output\, and the se
 nsitivity of the physics loss to the FMU input parameters is evaluated num
 erically by central finite differences and combined with neural-network ba
 ckpropagation. A second-order Thevenin battery model is used as a proof-of
 -concept\, and coupled simulation is implemented through the Functional Mo
 ckup Interface (FMI) standard. The framework provides a reusable approach 
 for integrating Modelica physical models with data-driven parameter identi
 fication.
DTSTAMP:20261004T070527Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:Research on PINN Data-Driven Modeling Method Based on Modelica Mode
 l - Kaixuan Zhang\, Yanfang Liu\, Yuan Sun\, Heng Wang\, Xudong Wang\, Zhi
 dong Zhang\, Wei Wu
URL:https://modelica.simtek.cc/amfc2026/talk/TGUR3V/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-amfc2026-FMKRG7@modelica.simtek.cc
DTSTART;TZID=CST:20260922T110400
DTEND;TZID=CST:20260922T110500
DESCRIPTION:This session is chaired by: Prof. Liu Yanfang. \nShe  is the pr
 ofessor at the school of transportation sciencen and technology of Beihang
  University. She received her Ph.D. in Aerospace Manufacturing Engineering
  from Beihang University. Her research focuses on intelligent connected ve
 hicles and their applications. She has hosted multiple national level proj
 ects such as the National Natural Science Foundation of China\, and publis
 hed more than 109 academic papers. She received multiple scientific and te
 chnological awards\, such as the First Prize for Technological Invention i
 n the Mechanical Industry\, the Special Prize for Technological Progress i
 n the Chinese Mechanical Industry\, and the First Prize for Science and Te
 chnology in the Chinese Automotive Industry.
DTSTAMP:20261004T070527Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:T1S4-Session Chair - Yanfang Liu
URL:https://modelica.simtek.cc/amfc2026/talk/FMKRG7/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-amfc2026-A7YAYU@modelica.simtek.cc
DTSTART;TZID=CST:20260922T113000
DTEND;TZID=CST:20260922T115500
DESCRIPTION:With the development of electric drive systems for electric veh
 icles toward high power density and integration\, electric drive assemblie
 s integrating the drive motor\, motor controller\, and reducer have attrac
 ted lots of concerns. Under the combined effect of multiple heat sources w
 ithin a compact space\, the system heat dissipation of an electric drive s
 ystem remains an open challenge\, so it is necessary to analyze the temper
 ature rise characteristics inside the motor. This paper examines in detail
  the distribution of internal losses in the motor and calculates the therm
 al resistances of the motor components. Then\, a thermal–hydraulic coupl
 ed simulation platform on the Amesim platform for predicting the temperatu
 re rise of oil-cooled motors is developed based on the lumped-parameter th
 ermal network method. A bench test is established to verify the accuracy o
 f the simulation model. Experimental results show that the maximum error i
 s 5.53%. This study provides a reference for the thermal analysis of motor
 s in electric drive assemblies.
DTSTAMP:20261004T070527Z
LOCATION:(Electric) Mobility & Buildings (R2003)
SUMMARY:Simulation and Experimental Verification of Temperature Rise in an 
 Oil-Cooled Motor Based on Amesim - Cunhao Zhang\, Shuo Cheng\, Yanfang Liu
 \, Peng Dong\, Shuhan Wang\, Xiangyang Xu
URL:https://modelica.simtek.cc/amfc2026/talk/A7YAYU/
END:VEVENT
END:VCALENDAR