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Optimization techniques are applied to improve the continuing challenges associated with the design of green energy devices, contributing in shaping sustainable processes.

BMW, Alenia Aermacchi, Ford, and other leading companies and research institutions rely on the modeFRONTIER design optimization platform to develop lighter products. 

Top industry players choose ESTECO technology to effectively set up design tasks associated with EV/HEV systems and to quickly identify the right engineering strategies to cope with highly constrained design problems. 

Optimization techniques and Hardware-In-the-Loop frameworks for improved plant models

Hardware-in-the-loop (HIL) simulation is gaining more and more importance in the development and testing of complex real-time embedded systems. Thanks to HIL simulation techniques, the complexity of the plant under control can be included in the testing and development steps by introducing numerical models for all the linked systems. The embedded systems to be tested interact with these mathematical representations and provide an effective way for validating them when physical testing becomes too expensive or hard to achieve.

One of the main challenges control engineers are constantly faced with is that plant models still do not entirely match real world physics. This forces designers to incessantly refine such models to reduce the sources preventing to reach the ideal reliability.1 Moreover, Model Based Design has introduced many complex permutations of design, thus the test volumes in the latter process phases have increased by over 10 times.1 Still, a large portion of such tests are performed manually, requiring much effort and lengthening the time to market.

By applying HIL techniques it is possible to determine failures in embedded hardware and software at an early stage and identify the errors before they are carried forward through the entire development cycle. 

Considering this scenario, optimization techniques are becoming​ a powerful tool to support the testing and development of plants by establishing best practices for saving time and reducing costs. The use of the capabilities of the optimization algorithms leads to time reduction and improvements in model development, as well as to parallel software/hardware development. The result is a better representation of complex systems and exclusion of repeated configurations. Automated testing and calibration tasks can also be enhanced by reducing the test steps and automating the system calibration for testing at system level. 


1 Mandip Khorana, The development and optimization of a real-world control system using NI LABVIEW, 2010.

The work focuses on the Rapid Model Development for complex systems that can be possible through a measurement based approach to model creation, in addition optimization methods can powerful tools for better model creation of non-repetitive systems. Optimization technologies allow for reduced test steps resulting in reduced test time, and auto system calibration for system level testing.

This proceeding, presented at the modeFRONTIER User's Meeting 2012, focuses on the methodology to optimize parameters for real time control system in the field of kinematic analysis. Furthermore LabView was used to configure the Real-Machine-Target optimization

LabVIEW users can now improve product performance and reduce testing time by taking advantage of the newly released mF4LV, and its Hardware-In-the-Loop (HIL) optimization capabilities. This innovative integration solution developed by ESTECO is a “light” add-on version of modeFRONTIER to couple with LabVIEW, helping engineers and scientists quickly reach a sought-after response from the hardware or system under test.

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