Get Started with Powertrain Blockset
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.
Tutorials
- Getting Started with Powertrain Blockset
Run the conventional vehicle reference application and examine the final drive gear ratio impact on fuel economy and tailpipe emissions.
- Conventional Vehicle Spark-Ignition Engine Fuel Economy and Emissions
Calculate the city and highway fuel economy for a conventional vehicle with a 1.5–L spark-ignition (SI) engine.
- Conventional Vehicle Powertrain Efficiency
Examine the impact of the conventional vehicle transmission efficiency on the powertrain efficiency.
- Get Started with Virtual Vehicle Composer
Use the Virtual Vehicle Composer app to configure, build, test, and analyze a virtual vehicle.
Reference Application Projects and Templates
- Internal Combustion Engine Reference Application Projects
Use these reference applications as a starting point for your own internal combustion engine vehicle models.
- Hybrid and Electric Vehicle Reference Application Projects
Start building your own hybrid and electric vehicle models with reference application projects.
Videos
Powertrain Blockset Overview
Learn about Powertrain Blockset capabilities, including modeling powertrain systems and performing system design tradeoff studies.