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Reduction of experimental effort in conventional engine calibration process by using reduced order model

The calibration process of the internal combustion engine becomes more costly and time-consuming than ever due to complexity of the engines and stringent emission regulations. The accurate and fast calibration of the engine requires a lot of performance tests which demands time, cost, and expert manpower as well as test facilities that must be taken into considerations. This process needs a large number of test points which should be examined to get an optimum map of the engine. Although some works have been done on reducing test time for the engine calibration, still the process timing remains one of the challenges for the automotive industry. The present study aims to decrease the calibration cost and time by employing the proper orthogonal decomposition (POD) method. The POD-based reduced order model is applied to reduce the number of experimental engine tests to regenerate the entire calibration table by using only a few operating points accurately. The low, mid-range and high RPM test points of a small single-cylinder, two-stroke engine are chosen as the input to the mathematical model to regenerate the missing calibration data. The comparison between the generated and the corresponding experimental data indicates that they are in good agreement (<10% difference) which implies the POD model is capable to decrease the number of test points and consequently the run time of dynamometer. Furthermore, although this approach is implemented for a conventional calibration process, however, the methodology can be extended to complex calibration procedures.