Cylinder pressure or heat release rate tracking algorithms for direct-injected compression ignition engines allow to account for various disturbances, such as changes in the fuel characteristics or the effects of engine wear. They are suited for flex-fuel engines and allow the use of multi-injection strategies; hence, they show potential for mobile applications. However, they are based on the assumption that the heat release rate or cylinder pressure correlates well with engine efficiency and emissions and therefore allows a clean and efficient engine operation. With the objective to evaluate the potential of such tracking algorithms, this assumption is investigated for conventional as well as for low-temperature combustion strategies with diesel as fuel of choice. Based on experimental data, exploratory data analysis methods are applied to evaluate how sensitive engine efficiency and emissions are to changes of the heat release rate or cylinder pressure. Furthermore, an extended tracking algorithm is proposed, which can be applied for premixed charge compression ignition combustion concepts.
Multiple injections are widely used for direct-injection compression-ignition engines to mainly increase efficiency, lower pollutant emissions, and increase exhaust enthalpy. However, with each additional injection the degrees of freedom increase, which makes finding an optimal injection input by design of experiments a time-consuming task. In this paper, we present a model-based calibration method that determines the number of injections for a predefined set of requirements. First, we derive a zero-dimensional crank-angle-resolved cylinder process model based on first principles. The model includes a fuel injector and requires a low calibration effort. Second, we use this model in an optimal control problem that minimizes the fuel consumption subject to several constraints such as load, maximal pressure, maximal pressure gradient, engine-out temperature, and the limitations of the fuel injector. The optimal injector inputs are used as feedforward control signals on a real engine to validate the simulative results. In general, the experimental results are in good agreement with those obtained in simulations. Finally, we compare our approach to a state-of-the-art method known as pressure reference tracking which consists of two separate steps: the creation of an optimal pressure reference and the tracking by a discrete injector. We show that our method, which combines these two steps in a single optimization problem, results in an increase in indicated efficiency compared to the solution obtained by pressure reference tracking.
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