Hello guys, welcome back to our blog. In this article, I will discuss mini projects or hands-on demo for software-defined vehicles like SDVs simulation using CANoe or MATLAB, and creating a basic OTA workflow.

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Hands-on Demo for Software-Defined Vehicles (SDVs)

As SDVs gain traction, it is vital not only to understand their theoretical foundations but also to engage in practical experimentation. This section focuses on actionable, hands-on mini projects and demos that learners and professionals can undertake to gain a real-world understanding of SDV components, including in-vehicle networks, OTA updates, and middleware communication. These demos provide a window into how SDVs are designed, developed, tested, and deployed using industry-standard tools and frameworks.

SDV Simulation using CANoe or MATLAB

✅ Overview:

Simulation plays a critical role in modern automotive development. Tools like Vector CANoe and MATLAB/Simulink allow developers to emulate vehicle ECUs, run diagnostics, simulate communication protocols like CAN, LIN, or FlexRay, and validate system behavior in virtual environments.

🧩 Option A: CANoe Simulation

🔧 Tools Needed:

Vector CANoe
Database file (DBC)
CAPL scripting
Virtual CAN interface (e.g., CANoe Simulation Setup)

🛠️ Steps to Create a Simple SDV CANoe Simulation:

a. Setup Project:

Launch CANoe.
Create a new configuration.
Load the DBC file defining the network (CAN messages, signals).

b. Add Simulation Nodes:

Add at least two simulated ECUs.
Use CAPL (Communication Access Programming Language) to create signal processing logic.

c. Implement Signal Flow:

Write CAPL code to send periodic messages.
Receive and visualize signals on the Trace and Graphics window.

d. Simulate ECUs in SDV:

One ECU can represent an ADAS control unit.
Another can simulate a Brake Controller ECU sending deceleration signals.

e. Testing:

Run the simulation.
Monitor performance, message exchange, and behavior.

📈 Learning Outcome:

Understand how ECUs communicate.
Hands-on exposure to CAN signal transmission.
Real-world experience of SDV communication layers.

🧩 Option B: MATLAB/Simulink Simulation

🔧 Tools Needed:

MATLAB/Simulink
Vehicle Dynamics Blockset
CAN Communication Toolbox

🛠️ Steps to Simulate a Vehicle Function:

a. Create Model:

Use Simulink to build a simple EV powertrain model.
Integrate sensors, actuators, and control logic.

b. Add CAN Interface:

Use blocks from the Vehicle Network Toolbox to simulate a CAN bus.
Define signals, messages, and routing logic.

c. Control Logic:

Add ADAS-like logic (e.g., cruise control, auto braking).
Use Stateflow for condition-based triggering.

d. Run Simulation:

Simulate dynamic behavior.
Analyze signal flow in the scope window.

📈 Learning Outcome:

Visualize vehicle behavior under various scenarios.
Understand physical plant models in SDVs.
Learn CAN message simulation in the MATLAB environment.

Creating a Basic OTA Workflow Demo

✅ Overview:

Over-the-air (OTA) updates are a cornerstone feature in SDVs. This mini project involves setting up a simplified OTA workflow using virtual environments to emulate software updates over a network.

🧩 Tools You Can Use:

Docker
Python Flask (as OTA server)
Simulated ECUs (e.g., Raspberry Pi or virtual Linux machines)
Postman or cURL for API triggering

🛠️ Steps to Simulate a Basic OTA Update Flow:

a. Set Up OTA Server:

Create a Flask-based REST API server.
Host firmware versions and metadata.
Provide endpoints like /check-update, /download, /install.

b. Create Client (Vehicle Emulator):

Create a Python script to emulate ECU behavior.
Client checks for updates using REST calls.

c. Download & Install Simulation:

Emulate downloading a firmware file (could be a simple .txt or .bin).
Simulate an integrity check using hash verification.
Log simulated “installation.”

d. Optional Logging & Rollback:

Log success/failure.
Add mock rollback procedure for failed updates.

📈 Learning Outcome:

Understand OTA architecture: Server, client, and integrity checks.
Demonstrate updated workflow in software-defined environments.
Experience the modularity and scalability of SDV updates.

Middleware Signal Flow Concept (SOME/IP or DDS)

✅ Overview:

Middleware is the glue that connects applications and services across domains in SDVs. This mini demo illustrates how a signal flows from one component to another using a middleware protocol like SOME/IP (Scalable service-Oriented Middleware over IP) or DDS (Data Distribution Service).

🧩 Simulation of Middleware Communication

Tools You Can Use:

Eclipse Cyclone DDS or OpenDDS
GENIVI SOME/IP Demo Apps
Python or C++ for service/client stubs

🛠️ Steps for Middleware Communication Flow:

a. SOME/IP Service-Client Setup:

Use GENIVI’s vsomeip framework.
Create a “Service” app that sends speed data.
Create a “Client” app that subscribes to speed data.

b. DDS Setup (Alternative):

Define data topic (e.g., VehicleSpeed).
The publisher writes on the topic; the Subscriber reads.
Use Cyclone DDS Python or C++ API.

c. Run & Visualize Flow:

Launch the service and client.
Log timestamps, message payloads.

📈 Learning Outcome:

Understand service-oriented architecture in SDVs.
Implement communication middleware for real-time systems.
Develop service-discovery-based interaction.

🔚 Conclusion

These hands-on mini projects offer immense learning value:

CANoe and MATLAB/Simulink simulations enable engineers to validate their designs in a virtual, cost-effective way.
OTA updates demystify the complexity of remote update infrastructure.
Middleware flows give insight into how decoupled software blocks communicate in real-time with robustness and safety.

Whether you’re a student, developer, or automotive engineer, these SDV mini projects allow you to go beyond theory and experience the future of automotive technology firsthand.

This was about “Mini Project / Hands-on Demo for Software-Defined Vehicles (SDVs)“. Thank you for reading.

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Chetan Shidling

Automotive | Technical Blogger | Engineer | Content Creator

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