This paper discusses the use of "instantaneous" high resolution (1 Hz) emission data for the construction of emissions patterns applicable to non-standard vehicle driving patterns. Extensive measurements of twenty EURO-1 gasoline passenger cars are available and have been used to predict emission factors for standard (ie legislative) as well as (non-standard) "real world" driving patterns. It is shown that emission level predictions based upon chassis dynamometer tests of standard driving cycles significantly underestimate the emission level of real-world driving behavior. The emission characteristics of modern passenger cars equipped with three-way catalytic converter are a low basic emission level on the one hand and frequent emission "peaks" on the other hand. For real-world driving, up to one-half of the entire emission can be emitted during these short-lasting peaks. Their frequency depends on various aspects, among them the level of "dynamics" of the driving pattern. Because of this, the average speed as single parameter to characterize the emission of a specific driving pattern is not sufficient. The instantaneous emissions approach uses an additional parameter representing engine load in order to resolve the differences between driving patterns with comparable average speeds but different levels of "dynamics". A thorough investigation of different statistical models and methods to further improve the prediction capability of the instantaneous emission approach is discussed. The fundamental differences in emission reduction strategies between different car manufacturers make the task of constructing a model valid for all catalyst passenger cars seemingly impossible. (A) For the covering abstract see IRRD E101903.


  • English

Media Info

  • Features: References;
  • Pagination: p. 193-202
  • Serial:
    • Volume: 76

Subject/Index Terms

Filing Info

  • Accession Number: 00768747
  • Record Type: Publication
  • Source Agency: Transport Research Laboratory
  • Files: ITRD
  • Created Date: Sep 10 1999 12:00AM