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Powertrain Blockset

Model and simulate automotive powertrain systems

Powertrain Blockset™ provides preassembled automotive vehicle reference applications for gasoline, diesel, hybrid, fuel cell, and battery electric propulsion systems. The blockset includes a component library for engines, traction motors, batteries, transmissions, tires, and driver models, as well as component and supervisory controllers.

Powertrain Blockset offers the Virtual Vehicle Composer app for configuring and parameterizing models, as well as prebuilt workflows for resizing components, calibrating models from data, optimizing shift schedules, and generating deep learning dynamic plant and state estimators. You can use these models for design tradeoff analysis and component sizing, control parameter optimization, and hardware-in-the-loop (HIL) testing. The models are open, so you can incorporate your own subsystems and customize them as needed.

What Is Powertrain Blockset?

Reference Applications

Closed-loop models for drive cycle, energy management, and performance analyses

Vehicle icon shown alongside electric motor and battery icons.

Electric Vehicles

Vehicle icon shown alongside internal combustion engine, electric motor and battery icons.

Hybrid Electric Vehicles

Vehicle icon shown alongside internal combustion engine icon.

Conventional Vehicles

Product Highlights

Virtual Vehicle Composer

Interactively parameterize, configure, and build a virtual vehicle for fuel economy, EV range, energy management, and performance analyses.

Build Custom Vehicle Models with Virtual Vehicle Composer App

Examples

Plots showing motor torque and efficiency characteristics overlayed onto the data calibration tab in the Virtual Vehicle Composer app.

Engine and Motor Resize Workflows

Use predefined workflows in the Virtual Vehicle Composer app for resizing engines and mapped motors based on high-level specifications such as maximum power. Associated controllers are automatically recalibrated for the resized component.

Documentation | Examples

Neural network icon shown alongside motor, internal combustion engine, and battery icons.

Generate Deep Learning Component Models

Generate reduced-order models (ROM) using dynamic deep learning networks for vehicle components, including engines or motors, for hardware-in-the-loop (HIL) testing and control design activities.

Deep Learning Processes for Transient Engine and Battery Plant Model Development Video

Documentation | Examples

Mesh plot showing q-axis flux as a function of d-axis current and q-axis current for a permanent magnet synchronous motor.

Virtual Calibration

Calibrate controllers and tune plant model parameters for components including engines, turbochargers, and motors before integrating them with your system-level vehicle model.

Virtual Design of Electrified Powertrain Systems (17:44)

Documentation | Examples

Speedgoat hardware with a physical connection to a monitor that is displaying a vehicle model, instrumentation dials, and plots.

HIL and Cloud Support

Capture important physical effects while achieving real-time execution using Simulink Real-Time™. Deploy to the cloud to add computing power for large-scale simulation studies.

Building a Hybrid Electric Vehicle Prototype System for Processor-in-the-Loop Simulations

Examples

Simscape models of a cooling system for a battery and motor shown originating from a vehicle icon.

Simscape Integration

With Simscape™, incorporate electrical, thermal, and mechanical designs into your vehicle models by assembling components into a schematic or selecting prebuilt options in the Virtual Vehicle Composer app.

Hybrid Electric Vehicles (8 videos)