System Identification for Automotive Systems: Opportunities and Challenges

  • Daniel Alberer
  • Håkan Hjalmarsson
  • Luigi del Re
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 418)


Without control many essential targets of the automotive industry could not be achieved. As control relies directly or indirectly on models and model quality directly influences the control performance, especially in feedforward structures as widely used in the automotive world, good models are needed. Good first principle models would be the first choice, and their determination is frequently difficult or even impossible. Against this background methods and tools developed by the system identification community could be used to obtain fast and reliably models, but a large gap seems to exist: neither these methods are sufficiently well known in the automotive community, nor enough attention is paid by the system identification community to the needs of the automotive industry. This introduction summarizes the state of the art and highlights possible critical issues for a future cooperation as they arose from an ACCM Workshop on Identification for Automotive Systems recently held in Linz, Austria.


Model Predictive Control Homogeneous Charge Compression Ignition Principle Model Hammerstein Model Optimal Experiment Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer London 2012

Authors and Affiliations

  • Daniel Alberer
    • Håkan Hjalmarsson
      • 1
    • Luigi del Re
      1. 1.ACCESS Linnaeus Center, School of Electrical EngineeringKTH – Royal, Institute of TechnologyStockholmSweden

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