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A. ROUSSEAU,S. SAGLINI,M. JAKLOV,D. GRAY,K. HARDY 한국자동차공학회 2003 International journal of automotive technology Vol.4 No.1
The Center for Transportation Research at the Argonne National Laboratory (ANL) supports the DOE by evaluating advanced automotive technologies in a systems conlext. ANL has developed a unique set of compatible simulation tools and test equipment to perform an integrated systems analysis project from modeling through hardware testing and validation. This project utilized these capabilities to demonstrate the trade-off in fuel economy and Oxides of Nitrogen (NOx) emissions in a so-called 'pre-transmission' parallel hybrid powertrain. The powertrain contiguration (in simulation and on the dynamometer) consists of a Compression Ignition Direct Ignition (CIDI) engine. a Continuously Variable Transmission (CVT) and an electric drive motor coupled to the CVT input shaft. The trade-off is studied in a simulated environment using PSAT with different controllers (fuzzy logic and rule based) and engine models (neural network and steady state models developed from ANL data),<br/>
Rousseau, A.,Saglini, S.,Jakov, M.,Gray, D.,Hardy, K. The Korean Society of Automotive Engineers 2003 International journal of automotive technology Vol.4 No.1
The Center for Transportation Research at the Argonne National Laboratory (ANL) supports the DOE by evaluating advanced automotive technologies in a systems context. ha has developed a unique set of compatible simulation tools and test equipment to perform an integrated systems analysis project from modeling through hardware testing and validation. This project utilized these capabilities to demonstrate the trade-off in fuel economy and Oxides of Nitrogen (NOx) emissions in a so-called ‘pre-transmission’ parallel hybrid powertrain. The powertrain configuration (in simulation and on the dynamometer) consists of a Compression Ignition Direct Ignition (CIDI) engine, a Continuously Variable Transmission (CVT) and an electric drive motor coupled to the CVT input shaft. The trade-off is studied in a simulated environment using PSAT with different controllers (fuzzy logic and rule based) and engine models (neural network and steady state models developed from ANL data).