Authors:
Mert Mokukcu
1
;
Philippe Fiani
2
;
Sylvain Chavanne
2
;
Lahsen Ait Taleb
2
;
Cristina Vlad
3
and
Emmanuel Godoy
3
Affiliations:
1
Sherpa Engineering, Centrale Supélec - CNRS - Univ. Paris-Sud and Université Paris Saclay, France
;
2
Sherpa Engineering, France
;
3
Centrale Supélec - CNRS - Univ. Paris-Sud and Université Paris Saclay, France
Keyword(s):
Control Design, Energy Management, Complex Systems, Modeling, Integrated Design, Systems Modeling, Interconnected Systems, System-level Design, System Verification, Automotive Engineering, System Architecture.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Modeling, Simulation and Architectures
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
;
Systems Modeling and Simulation
Abstract:
With the advances on component technology, communication and information, energy systems are becoming more complex. In this context, energy optimization based on various criteria requires the development of relevant and representative models that are able to characterize the system behaviour. Within this study, functional modeling is used to represent a system at a higher level of abstraction, with simple equations, local control loops and a decision manager (DM) for handling the energy flow. The reduced complexity and fast simulation of this model simplify the validation of system architecture and components sizing, as well as the performances evaluation of energy management algorithms according to different criteria. Once this first validation is completed, the following step in the system design process is to test the same algorithms on a more accurate model, represented at multi-physical level, that has its own local controllers and global resource manager (GRM). One way to compl
ete this second validation is to use the information computed using the functional model, to design a high level controller of a more complex multi-physical model. To this purpose, a solution is proposed to interconnect the two models, of the same system, that are represented at different level of abstraction. First, it is shown how the GRM can be extracted from the functional model. Secondly, it is presented how this management system can be adapted in order to be used at multi-physical level. Both models are developed for a plug-in parallel hybrid vehicle (PHEV), and the interconnection solution is illustrated for the considered application.
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