Model-Based Engine Calibration for Best Fuel Efficiency

Paper #:
  • 950983

Published:
  • 1995-02-01
Citation:
Onder, C. and Geering, H., "Model-Based Engine Calibration for Best Fuel Efficiency," SAE Technical Paper 950983, 1995, https://doi.org/10.4271/950983.
Pages:
10
Abstract:
Today's engine management systems for SI engines consist of static and dynamic control algorithms. The static functions of the engine management guarantee the correct stationary operation of the engine in all the possible operating points. The static functions are contained mainly in two lookup tables, one for the spark advance and one for the metered depending on engine speed and load. Usually these lookup tables are determined with experiments on the engine test bench. In this paper, a model-based method for the evaluation of the fuel-optimal maps for spark advance and metered fuel is described.The method can be divided into several steps: 1. Measurement and identification of all the engine parameters in a reference point (including the pressure in one cylinder) Calculation of the burn-through function (progress of the combustion) Iterative calculation of the amount of residual exhaust gas Approximation of the definitive burn-through function with the Vibe equation 2. Optimisation of the specific engine work by simulation, varying spark advance and metered fuel (the change in the combustion is taken into account by varying the Vibe parameters according to Csallner's equations). 3. Based on the engine parameter measurements of step 1, prediction of the parameters for a new operating point in the vicinity of the reference point using simulation only. 4. Search for the optimal spark advance and metered fuel in the new operating point according to step 2 The method has been tested with a BMW M30 engine (3.4 liter 6-cylinder) on a dynamic test bench. The results show that very few experiments yield good starting values for the fuel-optimal calibration of the engine. They prepare the way for further optimisation work on the test bench which now can focus on a rather narrow region around the optimum.
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