Tražilica projekata

Na ovoj stranici nalazi se popis domaćih i međunarodnih projekata na kojima FER sudjeluje kao nositelj projekta ili partner. Poveznica s imena pojedinog projekta vodi na stranicu na kojoj možete pronaći više detalja o odabranom projektu.

U popisu projekata koriste se sljedeće oznake:

  • Linija financiranja određuje izvor financiranja, npr.:
    • MZOS označava financiranje Ministarstva znanosti, obrazovanja i sporta Republike Hrvatske (MZOS). Broj nakon skraćenice MZOS, npr. 'MZOS 2007', označava godinu u kojoj je projekt započeo s radom. 
    • BICRO označava financiranje Poslovno-inovacijskog centra Hrvatske - BICRO d.o.o., 
    • HZZ označava financiranje Hrvatske zaklade za znanost (stari naziv: NZZ - Nacionalna zaklada za znanost, visoko školstvo i tehnologijski razvoj Republike Hrvatske).
  • Tip određuje vrstu projekta. 
    • ZP označava znanstvene projekte MZOS-a, 
    • INFO označava iProjekte.

Podaci u tražilici preuzimaju se periodično iz baze međunarodnih projekata Sveučilišta u Zagrebu.


Projekti

   

Project

Acronym:
ADELE 
Name:
Advanced evolutionary learning based methods for optimal characterisation of non-linear aftertreatment technologies 
Project status:
From: 2016-02-01 To: 2019-01-31 (Execution)
Contract number:
Action line:
FORD Global University Research Programs (URP) 
Type (Programme):
Ostali 
Instrument:
Ostalo 
Project cost:
120.000,00 USD
Project funding:
120.000,00 USD

Project coordinator

Organisation Name:
Fakultet elektrotehnike i računarstva 
Organisation adress:
Unska 3, 10000 Zagreb 
Organisation country:
Hrvatska 
Contact person name:
prof. dr. sc. Zdenko Kovačić 
Contact person email:

Croatian partner

Organisation name:
Fakultet elektrotehnike i računarstva 
Organisation address:
Unska 3, 10 000 Zagreb 
Contact person name:
prof. dr. sc. Zdenko Kovačić
Contact person tel:
01/6129-796 
Contact person fax:
Contact person e-mail:

Partners

Organisation name Country
Ford Research Center Aachen  Njemačka 

Short description of project

The main focus of research will be on LNT (Lean NOx Trap) models. While widely used in European applications these systems are very sensitive to ageing and sulfur poisoning requiring models that can track these features together with a good accuracy in wide operating range to yield a satisfactory prediction on Real World Driving Cycles. Recently established Sulfur ageing SGB rig at Ford Research in Aachen can do complex characterization of thermal and sulfur ageing of LNT samples. The task is to establish methods and algorithms for optimal transfer of the measured data into the existing models or identify the need for specific model features that need to be added or enhanced in order to improve the model prediction quality. The models will further be validated by the data stemming from various engine dyno and vehicle tests for given catalyst formulations. The goal is to develop custom made algorithms based on genetic (evolutionary) algorithms to initially tune the existing models. In the second phase of the project algorithms will be developed that provide self-tuning structures that can describe effects beyond functionality of the current models. Finally the optimal test setup for lab experiments will be investigated in order to enable creation of high fidelity data which in turn will lead to further enhancement of model tuning.  

Short description of the task performed by Croatian partner

The main focus of research will be on LNT (Lean NOx Trap) models. While widely used in European applications these systems are very sensitive to ageing and sulfur poisoning requiring models that can track these features together with a good accuracy in wide operating range to yield a satisfactory prediction on Real World Driving Cycles. Recently established Sulfur ageing SGB rig at Ford Research in Aachen can do complex characterization of thermal and sulfur ageing of LNT samples. The task is to establish methods and algorithms for optimal transfer of the measured data into the existing models or identify the need for specific model features that need to be added or enhanced in order to improve the model prediction quality. The models will further be validated by the data stemming from various engine dyno and vehicle tests for given catalyst formulations. The goal is to develop custom made algorithms based on genetic (evolutionary) algorithms to initially tune the existing models. In the second phase of the project algorithms will be developed that provide self-tuning structures that can describe effects beyond functionality of the current models. Finally the optimal test setup for lab experiments will be investigated in order to enable creation of high fidelity data which in turn will lead to further enhancement of model tuning.