Tutorial of model-based powertrain and aftertreatment system control design and implementation

Zhu, Guoming; Wang, Junmin; Sun, Zongxuan; Chen, Xiang

doi:10.1109/acc.2015.7171042

Cited by 4 publications

(3 citation statements)

References 97 publications

(120 reference statements)

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“…In the automotive industry, various types of models characterizing operations of automotive systems are available [2,7]. While it is still a very challenging job in practice to derive robust optimal solutions from direct model-based optimizations with these existing models, due to highly nonlinear complexities, an interesting development would be to utilize the available modeling information to provide feedforward or feedback assistance for the data-driven optimization algorithms so that the deficiencies mentioned above could be remedied.…”

Section: Systemsmentioning

confidence: 99%

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Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems

Tan

Chen

Tan

et al. 2019

Mechanical Engineering

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Essentially, the performance improvement of automotive systems is a multi-objective optimization problem [1–4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally requires precise tuning and coordination of accessible system inputs. In the past, such optimization problems have been approximately solved through expensive calibration procedures or an off-line local model-based approaches where either a regressive model or a first-principle model is used. The model-based optimization provides the advantage of finding the optimal model parameters to allow the model to be used to predict the real system behavior reasonably [5]. However, other than the model complexities, there are practically two issues facing the integrity of these models: modeling uncertainty due to inaccurate parameter values and/or unmodeled dynamics, and locally effective range around operating points. As a result, the optimum solutions extracted from the model-based approach could be subject to failure of expected performance [6].

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Section: Systemsmentioning

confidence: 99%

“…ssentially, the performance improvement of automotive systems is a multi-objective optimization problem [1][2][3][4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally require precise tuning and coordination of accessible system inputs.…”

mentioning

confidence: 99%

Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems

Tan

Chen

Tan

et al. 2019

Mechanical Engineering

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“…This introduces a coupling between the VGT and the EGR loops in the air path. The dynamics and control of this system have been topics of active research within the engine control community [5][6][7][8] for the last two decades. It is common for real-time VGT-EGR control systems to use supplier-provided turbocharger efficiency maps for given VGT vane positions.…”

Section: Introductionmentioning

confidence: 99%

Control-oriented turbine power model for a variable-geometry turbocharger

Zeng

Zhu

2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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A control-oriented model for the variable-geometry turbocharger is critical for model-based variable-geometry turbocharger control design. Typically, the variable-geometry turbocharger turbine power is modeled with a fixed mechanical efficiency of the turbocharger on the assumption of an isentropic process. The fixed-efficiency approach is an oversimplification and may lead to modeling errors because of an overpredicted or underpredicted compressor power. This leads to the use of lookup-table-based approaches for defining the mechanical efficiency of the turbocharger. Unfortunately, since the vane position of a variable-geometry turbocharger introduces a third dimension into these maps, real-time implementation requires three-dimensional interpolations with increased complexity. Map-based approaches offer greater fidelity in comparison with the fixed-efficiency approach but may introduce additional errors due to interpolation between the maps and extrapolation to extend the operational range outside the map. Interpolation errors can be managed by using dense maps with extensive flow bench testing; smooth extrapolation is necessary when the turbine is operated outside the mapped region, e.g. in low-flow and low-speed conditions. Extending the map to this region requires very precise flow control and measurement using a motor-driven compressor, which currently is not a standard test procedure. In this paper, a physics-based control-oriented model of the turbine power and the associated power loss is proposed and developed, where the turbine efficiency is modeled as a function of both the vane position of the variable-geometry turbocharger and the speed of the turbine shaft. As a result, the proposed model eliminates the interpolation errors with smooth extension to operational conditions outside typically mapped regions.

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