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Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles.
List of contents
From the Contents: Challenges and Opportunities for Identification Methods in Automotive Systems.- A Desired Modelling Environment for Automotive Powetrain Controls.- An Overview on System Identification Problems in Vehicle Chassis Control.- Linear Parameter-varying System Identification: The Subspace Approach.- A Tutorial on Numerical Methods for State and Parameter Estimation in Nonlinear Dynamic Systems.- Using Genetic Programming in Nonlinear Model Identification.- Markov Chain Modelling and On-board Identification for Automotive Vehicles.
Summary
Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles.
