MathWorks Automotive Virtual Conference


Customer Presentations

Cost and Benefit of Model-Based Development of Automotive Software – Results of a Global Study

Dr. Jens Zimmermann, Altran GmbH & Co. KG

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:

  • Specific results that indicate a considerable gain in efficiency using model-based software development
  • Factors that distinguish efficient from inefficient software development using models
  • The “frontloading effect,” in which efficiency is increased by early entry into testing

About the Speaker

Jens Zimmermann

Jens is an expert consultant at Altran in Munich. He works with automotive companies on software development for embedded systems. His main topics are model-based software development and AUTOSAR, the standard for automotive software architecture. He has a Ph.D. from the LMU Munich, which he completed in cooperation with the Max-Planck-Institut for Physics in Munich and the Forschungszentrum Jülich.

CPF Model and EKF Development Using Simulink and Global Optimization Toolbox

Jeremy West, Michigan Technological University

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.

About the Speaker

Jeremy West

Jeremy is researching diesel aftertreatment systems modeling and state estimation at Michigan Technological University. His interests involve environmentally sustainable engineering within automatic controls, including fuel-optimal control strategies and microgrids research, as well as optimization algorithms and modeling. He has a B.S. in mechanical engineering and is currently completing his master's degree.

Case Study: Dual Fuel Engine Control System Development Using Model-Based Design

Ben Hoffman, New Eagle, LLC

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.

About the Speaker

Ben Hoffman

Ben works in product development and applications at New Eagle, LLC in Ann Arbor, Michigan. He has been working in the controls software area for 13 years, and with MotoHawk for the past 10 years for model-based controls. During this time he’s developed software tools for automatic code generation with Simulink as well as worked in controls application development for systems including engine control, emissions control devices, and hybrid supervisory control. Ben has been qualified as a Functional Safety Certified Automotive Engineer (FSCAE) by TÜV-Nord of Germany for ISO 26262. He holds an M.S.E.E. from the University of Michigan and a B.S.E.E. from Kettering University.

Faster, More Efficient PLC Software Development Process for the Automotive Industry

Demetrio Cortese, Iveco

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.

About the Speaker

Demetrio Cortese

Demetrio is responsible for the development of embedded software for current and future electronic control units for Iveco commercial vehicles. He has been with Iveco since 2000, and previously worked for 10 years in aerospace with the International Space Station Program on the development of flight and ground software.

MathWorks Presentations

Model-Predictive Control of Diesel Engine Airpath

Pete Maloney, MathWorks
Rong Chen, MathWorks

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.

About the Speakers

Pete Maloney

Pete is a senior principal consulting engineer for MathWorks. His main areas of focus are powertrain calibration method development and application, Model-Based Design for electronic engine control systems, and powertrain system-level optimization for the automotive and off-highway industries. Before joining MathWorks in 2000, he designed and developed electronic engine control algorithms for Ford Motor Company and Delphi Automotive Systems over a 10-year period, resulting in 15 related patents. Pete has a B.S.M.E. from Texas Tech University and an S.M.M.E. from the Massachusetts Institute of Technology. He is currently vice chairman of the SAE Control and Calibration Committee.

Rong Chen

Rong is the team lead of the Model Predictive Control Toolbox™ product. He joined MathWorks in 2004 as a senior developer for Control System Toolbox™, developing software tools to facilitate the design and analysis of control systems. His research interests include first principles–based plant modeling, multivariable controller design, and process simulation. Rong received his B.S. and M.S. degrees from the department of automation at Tsinghua University (China). He later received his Ph.D. in chemical engineering from the University of Maryland.

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