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Web-based modeling platforms and automated modelchecking workflows in CI/CD pipelines require remotely callable Modelica frontend services. This paper presents SMC (Simtek Modelica Compiler), a service-oriented Modelica frontend implemented in Go. SMC exposes model loading, structured queries, diagnostics, and flattening through a service API backed by a pipeline for parsing, instantiation, semantic analysis, connection processing, and flat-model generation. Drawing on the SMC implementation, we also discuss the engineering implications of Go for single-binary deployment, concurrent processing, representation of Modelica language constructs, and memory allocation. We demonstrate the prototype through integration with a web modeling platform. A common set of 606 comparable classes from the Modelica Standard Library (MSL) 4.0.0 is used both to assess frontend-result consistency against the OpenModelica Compiler (OMC) and to measure flattening time, memory allocation, and garbage-collection behavior. The evaluation covers frontend artifacts and in-process processing costs; backend processing, code generation, simulation, and FMU export are outside its scope.

The high penetration of renewable energy increases grid volatility, necessitating flexible demand-side resources such as District Cooling Systems (DCS) with thermal storage. However, the multi-timescale coupling of equipment, pipelines, and buildings makes it difficult to integrate high-fidelity physical models with optimization. This paper proposes an FMI-based co-simulation framework for day-ahead DCS optimization. A fullchain Modelica model is exported as an FMI 2.0 CoSimulation FMU and coupled with MATLAB optimizers. The main contribution is a standardized FMU-in-the-loop workflow for day-ahead mixed-integer nonlinear programming (MINLP) scheduling with discrete unit commitment, together with a systematic quantification of the accuracy-runtime trade-off induced by FMI communication settings. Sensitivity analysis identifies a practical configuration for the investigated case, while a comparison of the genetic algorithm (GA) and surrogateopt reveals the solution-quality and computation-time trade-off. Compared with storagepriority control, the cost-optimization scenario reduces operating costs by 0.97%, the energy-optimization scenario reduces electricity consumption by 4.58%, and the carbon-optimization scenario reduces CO2 emissions by 4.20%. The physical feasibility of the generated schedules is verified by dynamic FMU 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.