Estimation of Heat Release and In-Cylinder Pressure in Diesel Engines from Basic Testbed Data

The present paper proposes a novel approach for the estimation of the in-cylinder pressure and heat release in diesel engines from basic testbed measurements (i.e., brake mean effective pressure (BMEP), gross indicated mean effective pressure (IMEP360), peak firing pressure (PFP), crank angle at which 50% of fuel mass has burnt (MFB50) and exhaust gas temperature (Texh). The method exploits a previously developed low-throughput combustion model, based on the accumulated fuel mass approach, which has been tuned by a genetic algorithm (GA) optimizer. The latter adjusts the main combustion model parameters to minimize an objective function, which depends on the prediction errors of BMEP, IMEP360, PFP, MFB50 and Texh. Several scenarios were evaluated in which different subsets of the four previous quantities were assumed to be known from experimental activities. The proposed method is particularly useful when in-cylinder pressure traces are unavailable and only basic testbed data exist. The results show that the in-cylinder pressure and heat release profiles are estimated with a high level of accuracy, since the root mean squared error is of the order of 1–2.5 bar and 2–2.7 × 10−2 kJ, respectively, depending on the considered scenario, while requiring a modest computational effort which is of the order of 3–6 min per test. Moreover, the low-throughput nature of the method makes it straightforward for other researchers to implement and reproduce results on different engines. The approach is also fuel-independent and can be applied to engines running on alternative/zero-carbon fuels, which are currently being extensively studied as potential ways to reduce the environmental impact of internal combustion engines.