IAV Automotive Engineering and Calico Systems Announce the Formation Of Automotive Neural Network Consortium (AutoNNC)

Apr 26, 2001, 01:00 ET from IAV Automotive Engineering from ,Calico Systems

    ANN ARBOR, Mich., April 26 /PRNewswire/ -- IAV Automotive Engineering and
 Calico Systems announce the formation of an automotive research and
 development consortium aimed at the widespread deployment of neural network
 software technologies in powertrain applications.  The Automotive Neural
 Network Consortium (AutoNNC) will conduct a four-year program of pre-
 competitive research in the emerging area of the application of artificial
 neural networks (ANNs) to the control, calibration, diagnosis, simulation and
 modeling of advanced powertrain systems.  Targeted applications will include
 light-duty gasoline and diesel vehicles, medium and heavy-duty diesel engines,
 hybrid electric vehicles and other emerging technologies such as fuel cells.
     Given sufficient data, neural networks (which are believed to mimic the
 manner in which the human brain operates) have the capability to learn the
 complex, multidimensional and non-linear relationships between many
 independent and dependent variables.  For example, given sufficient data on
 real engine performance during training, a neural network model can be
 developed that shows the relationships between engine performance, individual
 exhaust gas emissions rates and engine operating parameters in real-time,
 across any transient operating cycle.  Once developed, this model can then be
 used for real-time optimal engine control, for on-line diagnostics, for off-
 line engine calibration or for engine simulation.
     Ever-stricter emission regulations and increasing demands for improved
 vehicle fuel economy have resulted in tremendous complexity in automotive
 powertrain control.  With the explosive proliferation of emerging engine and
 exhaust aftertreatment technologies, there is apparently no end in sight to
 this trend.  The targeted use of neural networks in powertrain applications
 has the potential to reduce software complexity enormously while adding
 significant new functionality.  "The replacement of a physical model with a
 neural network-based algorithm for charge air determination in a new high
 complexity gasoline engine application resulted in a significant reduction in
 the time required to code and calibrate a new engine control strategy," said
 Sven Meyer, Senior Control Systems Engineer at IAV Inc.
     The potential high value applications of ANNs to powertrain development
 are not just in the area of engine control.  According to Dr. Chris Atkinson,
 Chief Engineer of Calico Systems, "Recent successes in expediting engine and
 vehicle calibration using neural network modeling-based techniques have opened
 up a whole new avenue of approach for reducing vehicle time-to-market.  Neural
 network-based rapid calibration methods have reduced the time required to
 calibrate a five parameter diesel engine control system with over 2700
 discrete calibrateable parameters from 12 months to 3 months."
     The Consortium is targeted primarily at automotive manufacturers, engine
 manufacturers, powertrain component and system suppliers, and engine control
 and calibration tool suppliers.  The Consortium will conduct projects of
 relevance to both gasoline and diesel powertrain engineers by employing a dual
 track approach.  All work conducted will be non-vehicle or engine platform
 specific and, as an alternative, will use state-of-the-art generic engine
 control hardware and engines.  The technology targeted will be 2005-2007
 emissions standards compliant.
     The pre-competitive research focus of the Consortium will be to
 investigate the use of neural network-based techniques to
 
     *  Reduce powertrain software complexity, while accommodating new
 technologies and approaches
     *  Reduce time-to-market for new powertrain control systems
     *  Reduce significantly the time, effort and costs required to calibrate
 engines and vehicles for emissions, fuel consumption and driveability
 constraints
     *  Develop new on board diagnostic techniques
     *  Reduce hardware requirements with virtual sensing technologies
     *  Develop fully optimized, transient neural network model-based mapless
 engine control systems
 
     The benefits to participation in the Consortium will include the
 identification of potential smart applications of ANNs in next generation
 powertrain activities, the development of industry standards for the
 integration of ANN techniques in powertrain control and calibration, and
 access to reliable results from case studies and real-world investigations of
 successful ANN implementation.  It is widely anticipated that this decade will
 see the widespread deployment of ANNs in select powertrain control,
 calibration, diagnostics, modeling and simulation applications, and this
 Consortium aims to facilitate that goal.
     The Automotive Neural Network Consortium (AutoNNC), which will commence in
 July 2001, will be co-hosted by IAV Automotive Engineering Inc., and Calico
 Systems Inc.
     For more information on the Automotive Neural Network Consortium, go to
 www.autonnc.org
 
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                http://tbutton.prnewswire.com/prn/11690X07364158
 
 

SOURCE IAV Automotive Engineering; Calico Systems
    ANN ARBOR, Mich., April 26 /PRNewswire/ -- IAV Automotive Engineering and
 Calico Systems announce the formation of an automotive research and
 development consortium aimed at the widespread deployment of neural network
 software technologies in powertrain applications.  The Automotive Neural
 Network Consortium (AutoNNC) will conduct a four-year program of pre-
 competitive research in the emerging area of the application of artificial
 neural networks (ANNs) to the control, calibration, diagnosis, simulation and
 modeling of advanced powertrain systems.  Targeted applications will include
 light-duty gasoline and diesel vehicles, medium and heavy-duty diesel engines,
 hybrid electric vehicles and other emerging technologies such as fuel cells.
     Given sufficient data, neural networks (which are believed to mimic the
 manner in which the human brain operates) have the capability to learn the
 complex, multidimensional and non-linear relationships between many
 independent and dependent variables.  For example, given sufficient data on
 real engine performance during training, a neural network model can be
 developed that shows the relationships between engine performance, individual
 exhaust gas emissions rates and engine operating parameters in real-time,
 across any transient operating cycle.  Once developed, this model can then be
 used for real-time optimal engine control, for on-line diagnostics, for off-
 line engine calibration or for engine simulation.
     Ever-stricter emission regulations and increasing demands for improved
 vehicle fuel economy have resulted in tremendous complexity in automotive
 powertrain control.  With the explosive proliferation of emerging engine and
 exhaust aftertreatment technologies, there is apparently no end in sight to
 this trend.  The targeted use of neural networks in powertrain applications
 has the potential to reduce software complexity enormously while adding
 significant new functionality.  "The replacement of a physical model with a
 neural network-based algorithm for charge air determination in a new high
 complexity gasoline engine application resulted in a significant reduction in
 the time required to code and calibrate a new engine control strategy," said
 Sven Meyer, Senior Control Systems Engineer at IAV Inc.
     The potential high value applications of ANNs to powertrain development
 are not just in the area of engine control.  According to Dr. Chris Atkinson,
 Chief Engineer of Calico Systems, "Recent successes in expediting engine and
 vehicle calibration using neural network modeling-based techniques have opened
 up a whole new avenue of approach for reducing vehicle time-to-market.  Neural
 network-based rapid calibration methods have reduced the time required to
 calibrate a five parameter diesel engine control system with over 2700
 discrete calibrateable parameters from 12 months to 3 months."
     The Consortium is targeted primarily at automotive manufacturers, engine
 manufacturers, powertrain component and system suppliers, and engine control
 and calibration tool suppliers.  The Consortium will conduct projects of
 relevance to both gasoline and diesel powertrain engineers by employing a dual
 track approach.  All work conducted will be non-vehicle or engine platform
 specific and, as an alternative, will use state-of-the-art generic engine
 control hardware and engines.  The technology targeted will be 2005-2007
 emissions standards compliant.
     The pre-competitive research focus of the Consortium will be to
 investigate the use of neural network-based techniques to
 
     *  Reduce powertrain software complexity, while accommodating new
 technologies and approaches
     *  Reduce time-to-market for new powertrain control systems
     *  Reduce significantly the time, effort and costs required to calibrate
 engines and vehicles for emissions, fuel consumption and driveability
 constraints
     *  Develop new on board diagnostic techniques
     *  Reduce hardware requirements with virtual sensing technologies
     *  Develop fully optimized, transient neural network model-based mapless
 engine control systems
 
     The benefits to participation in the Consortium will include the
 identification of potential smart applications of ANNs in next generation
 powertrain activities, the development of industry standards for the
 integration of ANN techniques in powertrain control and calibration, and
 access to reliable results from case studies and real-world investigations of
 successful ANN implementation.  It is widely anticipated that this decade will
 see the widespread deployment of ANNs in select powertrain control,
 calibration, diagnostics, modeling and simulation applications, and this
 Consortium aims to facilitate that goal.
     The Automotive Neural Network Consortium (AutoNNC), which will commence in
 July 2001, will be co-hosted by IAV Automotive Engineering Inc., and Calico
 Systems Inc.
     For more information on the Automotive Neural Network Consortium, go to
 www.autonnc.org
 
                     MAKE YOUR OPINION COUNT -  Click Here
                http://tbutton.prnewswire.com/prn/11690X07364158
 
 SOURCE  IAV Automotive Engineering; Calico Systems