Engineers use Model-Based Design to develop commercial vehicles and off-highway machines that deliver increased productivity, uptime, and safety while meeting local market and emissions requirements. They do this by using system-level models to validate requirements, then generate embedded code from the models and analytically calibrate the powertrain controllers.
The adoption of electronic controls for precision agriculture machinery and intelligent, autonomous vehicles has increased machine complexity, which in turn has increased the difficulty of verifying machine performance against requirements without extensive testing. Engineers use Model-Based Design capabilities to create multidomain simulation models that incorporate mechanical, hydraulic, electrical, and other domains; perform architecture and design tradeoffs across these domains; and verify machine performance against requirements without machine prototypes.
Advanced electronic controllers are at the heart of modern off-highway machinery. Engineers use a system model of the entire machine to design the embedded control algorithms and verify performance. They use the same model to generate code for real-time hardware-in-the-loop (HIL) simulations and to generate production code for embedded controllers, thereby verifying their designs.
To meet the Tier 4 standard in the U.S. and the Euro Stage VI standard in the EU, engineers must make tradeoffs among machine performance, particulate and NOx emissions, and component costs. As a result, they are moving away from powertrain calibration as a heuristic activity to analytical methods, such as Design of Experiments and numerical optimization, based on models developed from experimental data. Using MathWorks products for advanced math, statistics, and optimization, engineers reduce the number of prototype vehicles needed to calibrate the engines and comply with standards.