BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//modelica.simtek.cc//amfc2026//talk//7QBACH
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-amfc2026-7QBACH@modelica.simtek.cc
DTSTART;TZID=CST:20260921T095000
DTEND;TZID=CST:20260921T102000
DESCRIPTION:Leveraging its full-industry-chain advantages\, vertical integr
 ation\, and massive proprietary R&D\, BYD has achieved global leadership i
 n new energy vehicle sales. To address the growing complexity of vehicle s
 ystems while sustaining this leadership\, systematic application of system
 s engineering methods has become essential across the product lifecycle. T
 his keynote focuses on digital engineering practices with Modelica\, explo
 ring three interrelated areas: 1) co-simulation design and validation meth
 odologies for highly integrated vehicle systems\; 2) high-fidelity and com
 putationally efficient modeling approaches for diverse R&D scenarios\; and
  3) engineering implementation of toolchain integration and virtual valida
 tion. We are currently advancing Modelica-based digital engineering practi
 ces across multiple domains: developing a whole-vehicle digital engineerin
 g co-simulation platform that integrates powertrain\, thermal management\,
  and chassis control models\, realizing seamless cross-system collaborativ
 e optimization of power performance\, energy efficiency\, and handling sta
 bility\; creating digital models for HVAC thermal management systems that 
 accurately predict cabin thermal load and system power consumption and ene
 rgy flows\, significantly enhancing both energy efficiency and cabin comfo
 rt\; implementing digital development processes for seat systems that enab
 le early-stage validation of control strategies\, vibration-noise characte
 ristics\, and comfort attributes through model-driven design and simulatio
 n\, substantially reducing physical prototype development-cycles\; and pio
 neering AI-Modelica integration that leverages data-driven methods to acce
 lerate model generation\, parameter identification\, and calibration\, sub
 stantially improving the intelligence level of complex system modeling. Th
 ese practices collectively validate Modelica’s critical role in complex 
 multidisciplinary system modeling\, offering a new paradigm for enhancing 
 research and development efficiency in intelligent vehicle development.
DTSTAMP:20261004T074206Z
LOCATION:Main hall (R1016)
SUMMARY:Model-based Digital Engineering Practice for Automobiles - Yilin Zh
 ong
URL:https://modelica.simtek.cc/amfc2026/talk/7QBACH/
END:VEVENT
END:VCALENDAR