Efficient Traceable Model-Based Dynamic Optimization – EDOp

The main goal of this project is to develop a more effective model-based optimization approach by integrating optimization into the model-based development process. Models and optimization algorithms are combined into integrated models.

To reduce execution time, we will develop improved methods to compile such integrated models to efficient code for new multi-core architectures.

Profiling and debugging technology will be developed to highlight sources of performance problems.

The project includes development of optimization methods to address:

  1. Computationally heavy goal functions

  2. Dynamic optimal control of startup and load cycles are common industrial  application areas with especially problematic computation times.

Many of the results will be made available in a tool extension in the open source OpenModelica platform  including interoperability and communication with other tools using the new FMI interface standard.

Modelica Mathematical Modeling Language

Simulation: Symbolic + Numeric Methods

Compilation of a Modelica model will result in a system of sorted equations. Simulation is the runtime solution of such an equation system using a solver scheme and a run‐time system.

Modelica Optimization Extension

Project Research Summary

Optimization methods

Platform, tooling and modeling language

Applications and modeling

Integrated Open Source Tool for Modeling and Optimization

Example of Pipelined Parallel Inlined Solver Approach

Industrial Partners and Applications

Scania Truck Engine Emissions and Performance Optimization






Peter Fritzson. Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. 940 pages, Wiley-IEEE Press, 2004.
Håkan Lundvall and Peter Fritzson. Automatic Parallelization using Pipelining for Equation Based Simulation Languages In proceedings of the 14th Workshop on Compilers for Parallel Computing (CPC'2009), Zurich, Switzerland, Jan 7-9, 2009.
Kaj Holmberg, Martin Joborn, Kennet Melin. Lagrangian Based Heuristics for the Multicommodity Network Flow Problem with Fixed Costs on Paths. Europ.. Jour. of Operational Research 188, pp 101-108, 2008
Lars Eriksson, Johan Wahlström, and Markus Klein, Physical Modeling of Turbocharged Engines and Parameter Identification. pp. 59--79. Springer Verlag. In: Automotive Model Predictive Control: Models, Methods and Applications, Editors: del Re, Allgöwer, Glielmo, Guardiola, and Kolmanovsky, 2009