From static to dynamic optimisation of Diesel-engine control

Aspriona, Jonas; Chinellato, Oscar; Onder, Christopher H.; Guzzella, Lino

doi:10.1109/cdc.2013.6760970

Cited by 3 publications

(3 citation statements)

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“…The modelling errors are in the range of 0.6% and 5%, respectively. For the soot emissions, the simple model described in [34] is used. It captures the effects of the injection pressure and the air-to-fuel ratio (AFR).…”

Section: The Model Usedmentioning

confidence: 99%

“…Throughout this section, engine A is considered. From the solution of the OCP over a sufficiently long time horizon such as test case 7, implications for the control structure can be derived [34]. The optimal trajectories of the control inputs defining the combustion, that is, the SOI and the rail pressure, can be represented accurately by static maps over the engine operating range.…”

Section: Model-based Engine Calibrationmentioning

confidence: 99%

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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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Section: The Model Usedmentioning

confidence: 99%

Section: Model-based Engine Calibrationmentioning

confidence: 99%

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

Asprion

Chinellato²,

Guzzella

2014

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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“…One method is implementing the optimal controls as transient compensation maps together with the normal stationary calibration. This approach is advocated in [4,32,47]. Another approach along the same lines, i.e.…”

Section: Optimal Control Of Diesel Enginesmentioning

confidence: 99%

Optimal Control of Electrified Powertrains

Sivertsson

2015

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The cover: Front: Formulation and solution of an optimal control problem, coincidentally the benchmark problem in Paper 6. Back: Positive and not so positive exit messages from the solvers used in the dissertation. Typeset with L A T E X 2εPrinted by LiU-Tryck, Linköping, Sweden 2015 i Abstract Vehicle powertrain electrification, i.e. combining the internal combustion engine (ICE) with an electric motor (EM), is a potential way of meeting the increased demands for efficient and low emission transportation, at a price of increased powertrain complexity since more degrees of freedom (DoF) have been introduced. Optimal control is used in a series of studies of how to best exploit the additional DoFs.In a diesel-electric powertrain the absence of a secondary energy storage and mechanical connection between the ICE and the wheels means that all electricity used by the EMs needs to be produced simultaneously by the ICE, whose rotational speed is a DoF. This in combination with the relatively slow dynamics of the turbocharger in the ICE puts high requirements on good transient control.In optimal control studies, accurate models with good extrapolation properties are needed. For this aim two nonlinear physics based models are developed and made available that fulfill these requirements, these are also smooth in the region of interest, to enable gradient based optimization techniques. Using optimal control and one of the developed models, the turbocharger dynamics are shown to have a strong impact on how to control the powertrain and neglecting these can lead to erroneous estimates both in the response of the powertrain as well as how the powertrain should be controlled. Also the objective, whether time or fuel is to be minimized, influences the engine speed-torque path to be used, even though it is shown that the time optimal solution is almost fuel optimal. To increase the freedom of the powertrain control, a small energy storage can be added to assist in the transients. This is shown to be especially useful to decrease the response time of the powertrain, but the manner it is used, depends on the time horizon of the optimal control problem.The resulting optimal control solutions are for certain cases oscillatory when stationary controls would have been expected. This is shown to be neither an artifact of the discretization used nor a result of the modeling assumptions used. Instead it is for the formulated problems actually optimal to use periodic control in certain stationary operating points. Measurements show that the pumping torque is different depending on whether the controls are periodic or constant despite the same average value. Whether this is beneficial or not depends on the operating point and control frequency, but can be predicted using optimal periodic control theory.In hybrid electric vehicles (HEV) the size of the energy storage reduces the impact of poor transient control, since the battery can compensate for the slower dynamics of the ICE. For HEVs the problem instead is how and when to use the b...

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