To test the expectation that model-based development improves control of costs and complexity in the development of software-intensive systems, Altran conducted a global study in cooperation with the Technical University of Munich. The results of this study reveal changes in costs, time, and quality due to the application of model-based techniques. This presentation discusses:
Dr. Jens Zimmermann, Altran GmbH & Co. KG
Catalyzed particulate filters (CPFs) are used in heavy-duty diesel exhaust systems to reduce the amount of EPA-regulated particulate matter (PM) exiting the tailpipe. During transient engine operation, there is a continual change of PM residing in the filter due to changing rates of PM accumulation and oxidation. Knowing this quantity is important not only for active regeneration control system implementation, but also for quantifying the health of the device. DPF models, required for PM state estimation, exist in the literature but are often not suitable for real-time state estimation due to their computational requirements.
This presentation describes a simple model and state estimation developed using Simulink. The model has been calibrated to experimental data using the Global Optimization Toolbox. An extended Kalman filter is used for state estimation and its performance compared to engine test cell experiments of PM. The filter, also developed using Simulink, could be used for fuel-optimal control strategies as well as on-board diagnostics.
Jeremy West, Michigan Technological University
The recent natural-gas discoveries in North America have sparked a wave of interest in harnessing this source of energy for a range of applications. This presentation is an overview of a controls development effort aimed at using natural gas as a transportation fuel to offset the use of diesel fuel. In particular, focus is on the advantages provided by adopting rugged, production intent hardware from the onset and using MotoHawk, a model-based toolchain matured by almost a decade of development and refinement.
Ben Hoffman, New Eagle, LLC
To capitalize on a market opportunity in Latin America for a range of medium- to heavy-duty vehicles, Iveco had to design, implement, test, and deliver a shift range inhibitor system for vehicles with 9- and 16-speed transmissions in about six weeks. The aggressive deadline required a compressed software development schedule that left no room for specification or implementation errors. Model-Based Design with Simulink and Simulink PLC Coder™ enabled Iveco engineers to complete the transmission management system on schedule using existing programmable logic controller (PLC) hardware.
Demetrio Cortese, Iveco
With the new fuel economy and emissions requirements, multiple air system actuators must be used to meet transient power demands and change the engine operating point while minimizing emissions. Due to coupling in system dynamics, traditional single-loop PI controls prove to be insufficient. Also, multiple-input multiple-output (MIMO) control techniques are increasingly seen as an attractive alternative. Model predictive control offers a structured and intuitive way to accomplish MIMO design.
This presentation shows how to design model predictive controllers for simultaneous control of boost pressure and exhaust gas recirculation mass flow targets in the presence of driver fuel demand and engine speed changes using VGT and EGR. A high-fidelity engine model is linearized at multiple operating conditions using system identification methods. The resulting linear models are used for the design of a gain-scheduled model predictive controller. This controller is then validated in nonlinear simulation.