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Despite the trend in increased prosperity, the Indian automotive market, which is traditionally dominated by highly cost-oriented producion, is very sensitive to the price of fuels and vehicles. Due to these very specific market demands, the U-LCV (ultra-light commercial vehicle) segment with single cylinder natural aspirated Diesel engines (typical sub 650 cc displacement) is gaining immense popularity in the recent years. By moving to 2016, with the announcement of leapfrogging directly to Bharat Stage VI (BS VI) emission legislation in India, and in addition to the mandatory application of Diesel particle filters (DPF), there will be a need to implement effective NOx aftertreament systems. Due to the very low power-to-weight ratio of these particular applications, the engine operation takes place under full load conditions in a significant portion of the test cycle.

As emission regulations are becoming increasingly stringent worldwide, multiple exhaust aftertreatment devices are considered in order to minimize diesel engine tailpipe emissions. For the typical diesel applications in developing markets like India, the fuel consumption is a very decisive selling argument for customers. The total cost of ownership needs to be as low as possible. To meet these competing requirements, the aftertreatment and engines must be optimized at the same time as the performance of the one system affects the other. In state-of-the-art calibration processes, the aftertreatment systems are considered separately from the calibration of the thermodynamics. This conventional approach makes it more challenging to achieve a simultaneous optimization of the fuel consumption and tailpipe emissions under transient operating conditions.

The development of one cycle resolution control strategies and the research at HCCI engines demands an accurate estimation of the trapped mass. In contrast to current methods for determining the mass flow, which are only able to determine averaged values of the flow entering the cylinders, the present paper proposes a methodology based on the in-cylinder pressure resonance. The determination of such frequency allows inferring the cylinder mass with one cycle resolution. In addition, the method permits determining error metrics based on the mass conservation principle. Validation results for a reactivity controlled compression ignition (RCCI) engine equipped with electrohydraulic variable valve timing (VVT) are presented to illustrate the performance of the method.

Observers can be used for combining different information sources, as fast models with slow but accurate sensors. For that, a Kalman filter can be used for identifying the bias and cancelling its variation during time. However, normal calibration procedure is iterative and ad-hoc and this does not get optimal results. Furthermore, the lack of enough accurate references make difficult to estimate the best tuning, and more if the calibration pretends to be an online procedure. For solving this, the paper presents a novel calibration method for Kalman filter based on a Monte Carlo analysis, simulating real conditions by means of statistical distributions. This makes possible to create actual references for estimating error metrics of the observer output. A previous sensitivity study is presented for understanding the performance of the algorithm under different conditions.