A preliminary approach to simulating cyclic variability in a port fuel injection spark ignition engine

Differences in the combustion process from one cycle to the next, termed cyclic variations, are an important feature of spark ignition engines. These variations cause fluctuations in the work output of the engine and can therefore degrade engine and vehicle performance. In addition, the uneven running caused by cyclic variability of combustion constrains the engine operating range and thus has a direct effect on fuel consumption. Existing one-dimensional engine models typically represent cyclic variability using some form of stochastic behaviour defined by a pre-set normal distribution. This approach does not offer an insight into the mechanisms underlying variability, and makes it difficult to include variability when calibrating the engine using simulation. Three-dimensional modelling approaches can offer an insight but are too complex to be used extensively within a calibration exercise. In this paper, a simple, preliminary approach using empirical functions easily generated using standard engine instrumentation is used to augment a one-dimensional engine model via co-simulation approach to include a representation of the effects of the air–fuel ratio and residual gas fraction on combustion efficiency, early rate of combustion and duration of combustion. These parameters allow the engine model to simulate the effects of deterministic aspects of cyclic variability on heat release, in-cylinder pressure and indicated mean effective pressure. The model is validated by comparing its prediction of cyclic variability under both rich and lean operation to experimental data. The resulting predictions match experimental results qualitatively and quantitatively. The model can be used to inform subsequent optimization processes, representing the variability-induced constraints on the operating envelope. This will assist in the generation of fuel efficient calibrations and also allow cycle-to-cycle variation effects to be included much earlier in the design process. The model will also aid the development of online control approaches aiming to reduce cycle-to-cycle engine variability.