Year 1 concentrated on laying a solid foundation for the rest of the project in particular by defining the requirements and specifications, setting the required testing and test methods and establishing the use cases.
Year 2 put the emphasis on establishing a common understanding and defining concrete actions of modeling options for SPD, driver, ageing using AI and analytical techniques.
The focus moved to developing test, measurement and characterization methods and models required to implement a digital twin for lifetime prediction. Modeling activities are targeting the four selected use cases.
AI-TWILIGHT.EU or AI-TWILIGHT.org public website is disseminating 51 different activities. A LinkedIn page was setup and provides regular updates to the general public.
Work achieved so far
•Requirements and specification
a. Selection of 4 main use cases across the application areas (Automotive, street lighting, general lighting, horticulture), targeting specific purposes (eg : CLO / multichannel/ design and operation). Backup UCs pursued for specific applications.
b. End user requirements for LED system lifetime, survival rate, flux degradation and colour shift. The results were published in a public report.
•Data collection
a. Development and release to the consortium of a database platform for collecting field, measurement, characteristion data sets.
b. Definition of the testing requirements in correlation with the use cases (elaboration of mission profiles)
c. Establishing a dataset of pre-existing LED stress experiments in the form of LM-80 and product qualification data, and performing an initial load of the LIMS (laboratory information management system) with >60,000 experiment records.
•Measurements and characterisation
a. Detailed description and measurements of each use case in close relation with modelling requirements : LED chip lab measurements, driver description, thermal characterization of the luminaires, full description of the materials and optics, optical measurements.
b. Two different kind of test setups are built up for the measurement of fast optical (and electrical) transients of LED packages in order to support data corrections to be applied in the new high throughput test methods being developed through proper modelling.
c. Developing and setting up new accelerated aging tests for the selected LEDs types.
d. Launch aging tests of selected carrier devices in order to allow proper modelling of LED packages that are foreseen to be used in certain use-cases.
•Modeling and Digital twin generation
a. Identification and definition of the modeling extensions required to extend model-based design methodology to lifetime prediction.
b. Methodology approach for creating DIgital Twins in the context of SMEs
c. Review the steps towards “industrializing” the prior digitalized design and product development workflows of LED based luminaires as proposed by the prior Delphi4LED project through an improved, fully automated parameter extraction procedure that is capable of the identification of parameter sets of the Delphi4LED multi-domain LED models as well as parameters of SPD models and is open be part of an AI-based model identification approach aimed at the identification of the parameters of the dynamics of degradation of LEDs’ light-output properties.
d. A further achievement in this regard is completion of a web-based tool aimed at the fast and automated parameter extraction for the Delphi4LED Spice-like multi-domain LED model. Besides ease of use, robust and quick operation, the requirements against the test data used as input for the parameter identification got significantly released compared the past recommendations of Delphi4LED. Thus, it is sufficient to perform IVL characterization at about a dozen operating points (opposed to 30+ operating points recommended previously) and there is no need to set the junction temperature for the IVL characterization to a specific value; all together helping reduce testing efforts.
e. In terms of LED driver design an electro-thermal modelling and simulation workflow is proposed, assuring precise temperature values for components critical from a reliability perspective.
•Standardisation
a. Supporting the generation of improved AI-based aging models and test-related standardization activities with new aging measurements and LEDs degradation data.
