MBD Simulation AI Platform for Automotive
A platform that runs MATLAB / Simulink control models and simulations in parallel on Azure / Kubernetes, supporting verification, parameter exploration, and code generation from the specification stage.
Challenge
In conventional development, evaluation required building physical prototypes, so every defect caused large rework. Implementing software meant reading through massive specifications, which invited misreads and bugs, and some evaluation cases could not be tested physically on real hardware.
Solution
Built a platform that runs MATLAB / Simulink control models and simulations at scale on Azure, based on Model-Based Development (MBD) and CAE. Used Kubernetes (AKS) to run analysis jobs in parallel, connecting control-model verification, parameter exploration, and automated result aggregation from the specification stage. Models run as executable specifications, enabling automatic code generation from models and simulation of evaluation scenarios that are hard to reproduce on real hardware.
Result
Teams can verify control-model behavior at the specification stage, finding and fixing most defects during modeling. Rework after software implementation is minimized, and a wider range of evaluation scenarios improves reliability.
Team
1 member, PoC phase
Simulation infrastructure and MBD workflow design
Role
Handled simulation infrastructure, MBD/CAE workflow design, and MATLAB/Simulink integration.
Connected control-model execution, parallel analysis jobs, and result aggregation into a single workflow on Azure.
Tech Stack
Key Features
Run MATLAB / Simulink control models and simulations in parallel as analysis jobs on Kubernetes (AKS)
Verify model behavior at the specification stage, before writing implementation code
Parameter exploration and automatic aggregation of simulation results
Automatic code generation from verified models
Simulate evaluation scenarios that are hard or impossible to reproduce on real hardware
Technical Highlights
Catch Defects During Modeling
Because control models are built when the specification is fixed, behavior can be verified before coding, so most defects are found and fixed at the modeling stage and post-implementation rework is minimized.
Models as Executable Specifications
Running models as living specifications removes spec misreads, smooths implementation, and allows programs to be generated directly from the models.
Broader, More Reliable Evaluation
Simulation covers evaluation cases that cannot be tested physically on real hardware, widening coverage and improving reliability.