PERSIST – A scalable software architecture for the control of diverse automotive hybrid topologies

Richenhagen, Johannes; Venkitachalam, Hariharan; Pischinger, Stefan; Schloßer, Axel

doi:10.1007/978-3-658-08844-6_4

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Two controllers are used for performance benchmarking: 1) a EURO 6 production-line controller including control of airpath, injection (mass and timing), common rail pressure, and EATS (see Fig. 2) [35], [36] and 2) an economic MPC using 0.1-s horizon without look-ahead information [19]. This work develops an LAeMPC that shares similar objectives to the eMPC [19] but with a longer horizon of look-ahead information.…”

Section: System and Benchmark Controllersmentioning

confidence: 99%

See 1 more Smart Citation

A Hierarchical Economic Model Predictive Controller That Exploits Look-Ahead Information of Roads to Boost Engine Performance

Liu

Dizqah

Herreros

et al. 2023

IEEE Trans. Contr. Syst. Technol.

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Sensors and communication capabilities of connected vehicles provide look-ahead information that can be exploited by vehicle controllers. This work demonstrates the benefits of look-ahead information combined with hierarchical economic model predictive control for the airpath management of compression ignition engines. This work exploits road information predicted with a 0.1-and 2-s horizon to simultaneously control fast and slow engine dynamics, respectively. It controls the variable nozzle turbocharger and dual-loop exhaust gas recirculation, at a 0.01-s rate, to simultaneously optimize NOx, soot, and fuel economy. Simulation studies and hardware-in-loop implementation on an ARM Cortex-A15 processor demonstrate improved NOx, soot, and torque tracking without compromising fuel economy, and a worst case computation time of 8.92 ms.

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Section: System and Benchmark Controllersmentioning

confidence: 99%

“…The resulting performance difference of the LAeMPC against the eMPC implies the potential advantage of using look-ahead information. Compared to the eMPCs, the production-line controller is reactive and employs a combination of feedforward and PI/PID [35], [36].…”

Section: System and Benchmark Controllersmentioning

confidence: 99%

A Hierarchical Economic Model Predictive Controller That Exploits Look-Ahead Information of Roads to Boost Engine Performance

Liu

Dizqah

Herreros

et al. 2023

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…This model includes physics-based air path and combustion models as well as a model-based control structure, which consists of a scalable software (SW) architecture for the control of several automotive types. 16–18 Thanks to this last, the model is flexible toward hardware (HW) configuration changes. It can easily be adapted to different layouts by deleting the HW components that are not required for the application.…”

Section: Simulation Setup and Boundary Conditionsmentioning

confidence: 99%

“…Based on this and on the desired filling level in the cylinder, the boost control mechanisms are set to their target positions. 17…”

Section: Simulation Setup and Boundary Conditionsmentioning

confidence: 99%

Smart rule-based diesel engine control strategies by means of predictive driving information

Vagnoni

Eisenbarth

Andert

et al. 2019

International Journal of Engine Research

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The increasing connectivity of future vehicles allows the prediction of the powertrain operational profiles. This technology will improve the transient control of the engine and its exhaust gas aftertreatment systems. This article describes the development of a rule-based algorithm for the air path control, which uses the knowledge of upcoming driving events to reduce especially [Formula: see text] and particulate (soot) emissions. In the first section of this article, the boosting and the lean [Formula: see text] trap systems of a diesel powertrain are investigated as relevant sub-systems for shorter prediction horizons, suitable for Car-to-X communication range. Reference control strategies, based on state-of-the-art engine control unit algorithms and suitable predictive control logics, are compared for the two sub-systems in a model in the loop simulation environment. The simulation driving cycles are based on Worldwide harmonized Light-duty Test Cycle and Real Driving Emissions regulations. Due to the shorter, and consequently more probable, prediction horizon and the demonstrated emission improvements, a dedicated rule-based algorithm for the air path control is developed and benchmarked in the Worldwide harmonized Light-duty Test Cycle as described in the second part of this article. Worldwide harmonized Light-duty Test Cycle test results show an improvement potential for engine-out soot and [Formula: see text] emissions of up to 5.2% and 1.2%, respectively, for the air path case and a reduction of the average fuel consumption in Real Driving Emissions of up to 1% for the lean NOx trap case. In addition, the developed rule-based algorithm allows the adjustment of the desired NOx–soot trade-off, while keeping the fuel consumption constant. The study concludes with brief recommendations for future research directions, as for example, the introduction of a prediction module for the estimation of the vehicle operational profile in the prediction horizon.

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X-in-the-Loop-basierte Kalibrierung: HiL Simulation eines virtuellen Dieselantriebsstrangs

et al. 2018

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PERSIST – A scalable software architecture for the control of diverse automotive hybrid topologies

Cited by 6 publications

References 2 publications

A Hierarchical Economic Model Predictive Controller That Exploits Look-Ahead Information of Roads to Boost Engine Performance

A Hierarchical Economic Model Predictive Controller That Exploits Look-Ahead Information of Roads to Boost Engine Performance

Smart rule-based diesel engine control strategies by means of predictive driving information

X-in-the-Loop-basierte Kalibrierung: HiL Simulation eines virtuellen Dieselantriebsstrangs

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