Toward a Systematic Design for Turbocharged Engine Control

Gregory, Stewart; Borrelli, Francesco; Pekař, Jaroslav; Germann, David; Pachner, Daniel; Kihas, Dejan

doi:10.1007/978-1-84996-071-7_14

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…they are centered in low level control of a given subsystem, usually tracking a reference, rather than the engine operation global optimization). Some recent examples may be found in [65,[94][95][96][97].…”

Section: Mils / Silsmentioning

confidence: 99%

“…One significant problem is that a model may work accurately at a subsystem level, but when coupled with other models, significant deviations may occur because of several closedloop interactions at an engine level [97]. Furthermore, many models are too simple for catching the actual complexity of the system and their prediction capabilities are limited to the identification dataset; this is specially significant in combustion and pollutant formation models [80,96].…”

Section: Mils / Silsmentioning

confidence: 99%

See 1 more Smart Citation

A Challenging Future for the IC Engine: New Technologies and the Control Role

Payri

Lujan

Guardiola

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-New regulations on pollutants and, specially, on CO 2 emissions could restrict the use of the internal combustion engine in automotive applications. This paper presents a review of different technologies under development for meeting such regulations, ranging from new combustion concepts to advanced boosting methods and after-treatment systems. Many of them need an accurate control of the operating conditions and, in many cases, they impose demanding requirements at a system integration level. In this framework, engine control disciplines will be key for the implementation and development of the next generation engines, taking profit of recent advancements in models, methods and sensors. According to authors' opinion, the internal combustion engine will still be the dominant technology in automotive applications for the next decades.Re´sume´-Un challenge pour le futur du moteur a`combustion interne : nouvelles technologies et roˆle du controˆle moteur -Les nouvelles normes sur les e´missions, en particulier le CO 2 , pourraient re´duire l'utilisation du moteur a`combustion interne pour les ve´hicules. Cet article pre´sente une revue de diffe´rentes technologies en cours de de´veloppement afin de respecter ces normes, depuis de nouveaux concepts de combustion jusqu'a`des syste`mes avance´s de suralimentation ou de post-traitement. La plupart de ces technologies demande un controˆle pre´cis des conditions de fonctionnement et impose souvent de fortes contraintes lors de l'inte´gration des syste`mes. Dans ce contexte et en profitant des dernie`res avance´es dans les mode`les, les me´thodes et les capteurs, le controˆle moteur jouera un roˆle clef dans la mise en oeuvre et le de´veloppement de la prochaine ge´ne´ration de moteurs. De l'avis des auteurs, le moteur ac ombustion interne restera la technologie dominante pour les ve´hicules des prochaines de´cennies.

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Section: Mils / Silsmentioning

confidence: 99%

Section: Mils / Silsmentioning

confidence: 99%

A Challenging Future for the IC Engine: New Technologies and the Control Role

Payri

Lujan

Guardiola

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…However, in contrast to model-predictive feedback control, where linear models may be used that are valid only around the reference maps [16], the full nonlinear model would have to be considered. Despite the application of custom-tailored algorithms, already the simpler optimisation problems encountered within modelpredictive control are difficult to be solved in realtime due to the limited computational power and memory provided by the ECU [77]. Therefore, an online optimisation is not a feasible option at this time.…”

Section: Engineering Aspectsmentioning

confidence: 99%

Optimal Control of Diesel Engines: Numerical Methods, Applications, and Experimental Validation

Asprion

Chinellato²,

Guzzella

2014

Mathematical Problems in Engineering

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In response to the increasingly stringent emission regulations and a demand for ever lower fuel consumption, diesel engines have become complex systems. The exploitation of any leftover potential during transient operation is crucial. However, even an experienced calibration engineer cannot conceive all the dynamic cross couplings between the many actuators. Therefore, a highly iterative procedure is required to obtain a single engine calibration, which in turn causes a high demand for test-bench time. Physics-based mathematical models and a dynamic optimisation are the tools to alleviate this dilemma. This paper presents the methods required to implement such an approach. The optimisation-oriented modelling of diesel engines is summarised, and the numerical methods required to solve the corresponding large-scale optimal control problems are presented. The resulting optimal control input trajectories over long driving profiles are shown to provide enough information to allow conclusions to be drawn for causal control strategies. Ways of utilising this data are illustrated, which indicate that a fully automated dynamic calibration of the engine control unit is conceivable. An experimental validation demonstrates the meaningfulness of these results. The measurement results show that the optimisation predicts the reduction of the fuel consumption and the cumulative pollutant emissions with a relative error of around 10% on highly transient driving cycles.

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“…In practice, only few industrial systems have been developed, e.g. by Honeywell (Stewart et al (2010)) and Hoerbiger (Ängeby et al (2010), for a special application), and both of them based on linear model predictive control, which yields a suboptimal solution, but allows taking explicitly in account bounds on the inputs and constraints on the state or output variables. Nonlinear optimal control has not been considered for long time, mainly because the existing approaches for the solution of the nonlinear optimal control problem are limited to a very small number of cases, and available models hardly fulfill with these conditions.…”

Section: Introductionmentioning

confidence: 99%