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Knock is an undesirable phenomenon affecting gasoline spark-ignition (SI) engines. In order to maximize engine efficiency and output torque while limiting the knock rate, the spark timing should be adequately controlled. This paper focuses on closed-loop knock control strategies. The proposed control strategies, compared to conventional approaches, show improved performances while remaining simple to use, implement, and tune. Firstly, a deterministic controller which employs a logarithmic increase of the spark timing proves to outperform the conventional strategy in terms of spark timing average and variance. In addition, an adaptive parameter strategy which exploits stochastic information of the process is introduced. Thanks to this extension the average and the variance of the spark timing are additionally improved while preserving ease of tuning and the fast reaction times of the deterministic strategy. Throughout the paper all the knock controllers are compared with a conventional deterministic strategy and with a recently proposed stochastic one. The advantages of the proposed approaches are confirmed both by simulation and by experimental data collected at a test bench.

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This article presents a systematic approach to realize highly dynamic control strategies for the air path of diesel engines. It is based on grey-box models of individual air path components, designed to be applied in nonlinear model predictive control (NMPC). Specifically, they are suited for algorithmic differentiation and gradient-based optimization. This modular approach allows to derive models for a variety of complex air path systems and can be identified with a low amount of measurement data. An NMPC structure, which is based on these models, enables the tracking of arbitrary air path reference signals and allows the introduction of further control objectives for overactuated systems. We demonstrate our approach's general applicability and effectiveness on two different laboratory engines using rapid control prototyping hardware. For a turbocharged light-duty diesel engine with dual-loop exhaust gas recirculation (EGR), we apply the proposed control structure to track intake manifold gas conditions while simultaneously minimizing engine pumping losses. Experimental results show an excellent tracking performance and reduced pumping losses compared to other control strategies. For a turbocharged heavy-duty engine with high-pressure EGR, we experimentally demonstrate a superior tracking performance to that obtained with a reference controller. Due to the modular and systematic approach, the algorithm design is straightforward, and the experimental calibration effort is low.

The model-based method to define the optimal calibration maps for important diesel engine parameters may involve three major steps. First, the engine speed and load domain – in which the engine is operated – are identified. Then, a global engine model is created, which can be used for offline simulations to estimate engine performance. Finally, optimal calibration maps are obtained by formulating and solving an optimisation problem, with the goal of minimising fuel consumption while meeting constraints on pollutant emissions. This last step in the calibration process usually involves smoothing of the maps in order to improve drivability. This article presents a method to trade off map smoothness, brake-specific fuel consumption and nitrogen oxide emissions. After calculating the optimal but potentially non-smooth calibration maps, a variation-based smoothing method is employed to obtain different levels of smoothness by adapting a single tuning parameter. The method was experimentally validated on a heavy-duty diesel engine, and the non-road transient cycle was used as a case study. The error between the reference and actual engine torque was used as a metric for drivability, and the error was found to decrease with increasing map smoothness. After having obtained this trade-off for various fixed levels of smoothness, a time-varying smoothness calibration was generated and tested. Experimental results showed that, with a time-varying smoothness strategy, nitrogen oxide emissions could be reduced by 4%, while achieving the same drivability and fuel consumption as in the case of a fixed smoothing strategy.