top of page

Integrating MBSE & Modelica: Early-Stage Simulation for Complex Cyber-Physical Systems

Writer's picture: Alex MagdanzAlex Magdanz

In today’s world of complex cyber-physical systems, designing effective solutions requires integration across multiple domains. This post explores the integration of Model-Based Systems Engineering (MBSE) and Modelica to bring simulation into the earliest stages of system development. By doing so, we aim to facilitate better collaboration, enable holistic design, and support robust decision-making.


 

Why Continuous Engineering is Necessary

Traditional engineering processes often involve numerous stakeholders operating in silos, each with distinct tools, models, and workflows. This lack of integration delays the application of simulation, increasing friction and hindering collaboration.

To address these challenges, the importance of early-stage architecture validation and verification (V&V) using digital artifacts is emphasized. Early simulation ensures that variability and versatility are effectively managed during the initial phases of design.


 

A Holistic Approach to System Design

MBSE provides the foundation for integrating different engineering domains through the RFLP model:

  • Requirements (R): Define the needs of the system.

  • Functions (F): Identify functional behaviors.

  • Logical Structure (L): Map out relationships between system components.

  • Physical Implementation (P): Translate logical design into physical reality.

This structure corresponds to the left side of the V-model (see figure above), cascading horizontally and vertically across levels of detail, from product lines to subsystems. Tight integration across these layers ensures digital continuity and supports better information flow.


 

The Role of System Simulation

System simulation is vital for validating functional architectures, enabling the following:

  1. Performance Validation: Ensuring that the functional design meets specified requirements.

  2. 3D Design Input: Sizing and load case generation.

  3. Simulation-Based Certification: Establishing traceability between artifacts.

To achieve this, ESI implements a tool-agnostic integration layer. This middle layer bridges front-end authoring tools and back-end data storage, ensuring seamless communication while maintaining flexibility to swap tools without disrupting workflows.


 

Leveraging MBSE for Simulation Model Development

To transfer system-level information to simulation models, four key aspects were integrated:

  1. Logical Architecture: Using SysML stereotypes to link logical components with Modelica models categorized by physical domain.

  2. Design Parameters: Automatically transferring parameters to simulation models for consistent allocation.

  3. Configurations: Utilizing SysML 2.0 variability diagrams to implement model flexibility and create specific configurations.

  4. Test Model Generation: Extracting functional test scenarios from requirements to implement in simulation and verify system behavior.


 

Demonstration: Designing an Electric Semi-Truck

A practical demonstration involves designing a commercial electric vehicle for perishable goods transportation. Two key architectural decisions were evaluated:

Function

Method 1

Method 2

Power Source

Battery

Fuel Cell

Cabin Heating

Resistive Heating

Heat Pump

By using high-level performance requirements (e.g., range, consumption, comfort), the system architecture was created, and simulation model templates were automatically generated. Verification simulations tested these architectures under different scenarios, revealing:

  • Batteries and heat pumps consume less energy.

  • Heat pumps offer additional cooling benefits.


Based on these findings, the battery + heat pump configuration was selected, setting the stage for detailed design, which included a comprehensive evaluation of the chosen configuration:

  • Testing against ambient conditions ranging from -40°C to +40°C.

  • Identifying design spaces for cabin comfort parameters.

Visualization tools like parallel coordinates diagrams supported decision-making by showing how design parameters impact requirement fulfillment.

Ultimately, 6 out of 8 requirements were met, necessitating further refinement of the remaining parameters.


 

Key Takeaways

This process demonstrates how early-stage simulation and holistic system modeling can:

  1. Enable digital continuity, minimizing costly late-stage changes.

  2. Support a tool-agnostic approach, avoiding data duplication.

  3. Allow users to validate and verify architectures when critical decisions are being made.

By integrating MBSE and Modelica, engineers can align cross-domain workflows and unlock new levels of efficiency in developing complex systems. This approach not only saves time and resources but also enhances the capability to design robust, future-ready systems.

36 views0 comments

Comments


bottom of page