A Correlation Methodology Between AVL Mean Value Engine Model and Measurements With Concept Analysis of Mean Value Representation for Engine Transient Tests

Paper #:
  • 2017-24-0053

  • 2017-09-04
The use of state of the art simulation tools to allow for effective front-loading of the calibration process is essential to off-set these additional efforts; therefore, the process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight of representation domain of a mean value engine model. This paper focuses on the methodologies for correlating dynamic simulations with vehicle measured dynamic data (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle. This correlation is performed using simulated tests run within the AVL mean value model MoBEO (model based engine optimization). A conceptual analysis is dedicated to the intrinsic uncertainties of a mean value representation (measures and simulations) with respect to an ideal high-resolution dynamic representation; this is carried out for two purposes: (i) to understand the intrinsic uncertainties of a mean value representation domain and (ii) to understand how to correlate at best the simulated value with the measurements during transient cycles, particularly when the fundamental parameters (e.g. emission mass flow rate, temperatures or the EGR rate calculated from CO2 measures) depend on factors characterized by heterogenous dynamics and different transport/propagation times. Furthermore, elementary methods to compensate the time lag/delays and the sensors response time are discussed to obtain a proper correlation between measures and simulations. Using these methods, the objective is to explain how the correlated engine values and the small differences between simulated and measured results can be sourced by specific dynamic phenomena and how they impact on the final results. The analysis concludes with a global assessment of model to measures correlation and with the expected level of confidence for the model based calibration process.
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