Thermal management modeling and simulation have become critical to electric vehicle (EV) development. Modelica is well-suited for this domain thanks to its multi-disciplinary, acausal modeling capabilities, which naturally capture the physical behavior of batteries, electric motors, power electronics, and their associated cooling systems.
The ThermofluidStream (TFS)​ library, originally developed at DLR, introduces a novel modeling paradigm in Modelica that enables efficient simulation of large-scale thermal-fluid network models. YSLAB, developed by Nanjing Yuansi SimTek, is a browser-based Modelica simulation environment that lowers the barrier to entry for system-level modeling and interactive exploration.
This tutorial is structured in two parts. We begin with an introduction to thermodynamics modeling at the component level using YSLAB. Participants will then engage in a hands-on session to build an EV thermal management system model using the TFS library, from component instantiation to system assembly and simulation.

New Energy Vehicles (NEVs) drive China's automotive market. However, calibrating thermal management for the battery, motor, and inverter is a major bottleneck, especially as development cycles shrink to 24 months. High-fidelity thermal-fluid models are too slow for repeated calibration and fixed-step real-time execution. This paper presents an end-to-end industrial workflow: 1) Build high-fidelity models using 3D data; 2) Train reduced-order models (ROMs), implemented here as nonintrusive data-driven surrogate models, from simulation data; 3) Export the trained surrogates as FMI-compliant co-simulation FMUs for NI VeriStand real-time testing. 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 equation-level model-order-reduction method. The reported speedup is interpreted as repeated-simulation acceleration, with data generation and training treated as one-time amortized costs. For the reported battery validation cases, temperature RMSE was evaluated against experimental or CAE reference data and normalized by the initial temperature of the battery heat-generation operating condition. The calculation results show that the single-cell RMSE is within 5%, the battery-pack RMSE is within 10%, and the absolute temperature error is within 3 K. For a representative 10-module battery-pack cooling case, the 250 s closed-loop simulation time was reduced from 2200 s to 44 s after surrogate-FMU replacement, and the calibrated PI response met the control targets.