Virtual 48 V Mild Hybridization: Efficient Validation by Engine-in-the-Loop

Klein, Serge; Griefnow, Philip; Guse, Daniel; Xia, Feihong; Andert, Jakob

doi:10.4271/2018-01-0410

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…Therefore, EiL shows great potential for the frontloading of powertrain development tasks, in particular for consumption and emission issues. 5,[11][12][13][14][15][16] It combines the advantages of simulation, that is, reduced financial and time costs, reproducibility and flexibility, with the fidelity of physical testing. This paper focuses on a critical component of the simulation environment: the driver model.…”

Section: Contextmentioning

confidence: 99%

Design and off-line tuning of a longitudinal driver model for EiL applications

Gilormini

Chessé

Tauzia

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Tightnening emission regulations and increasing powertrain complexity lead car manufacturers to rely on novel testing methods in order to frontload development. Among these, Engine-in-the-Loop, that is, the coupling of a physical internal combustion engine (ICE) on a testbed with a virtual environment, shows great promise for emission- and consumption-related tasks. In particular, this study focuses on the driver model, a simple yet crucial component of the virtual environment. A longitudinal driver model is developed in Simulink based on the PI-regulation structure and augmented with anti-windup, cycle preview, and takeoff strategy. While the PI approach is generally chosen in the literature, this study details the implementation of the added functions, and proposes a method for the gains of the model to be tuned in simulation by considering engine dynamics, and using several performance indicators. The virtual driver is then tested in a complete EiL setup simulating an electric hybrid driveline and shows satisfactory cycle-following and overall behavior on a WLTC. Robustness of the tuning method is also studied by varying vehicle parameters on the EiL bench.

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Section: Contextmentioning

confidence: 99%

Design and off-line tuning of a longitudinal driver model for EiL applications

Gilormini

Chessé

Tauzia

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…The main motivation of R2R2D is a seamless system integration and testing process with an early start using virtual components. The benefits could be a multidimensional system optimisation in transient driving condition during concept study and function development 2,3 , or an early determination of component weaknesses in a safe test bench environment close to reality without expensive prototype manufacturing 4,5 . Even a pre-calibration can be possible to provide the initial parameter sets for powertrain control units 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Road-to-rig-to-desktop: Virtual development using real-time engine modelling and powertrain co-simulation

Andert

Xia

Klein

et al. 2018

International Journal of Engine Research

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By front-loading of the conventional vehicle testing to engine test bench or even further forward to offline simulations, it is possible to assess a large variation of powertrain design parameters and testing manoeuvres in the early development stages. This entails a substantial cost reduction compared to physical vehicle testing and hence an optimisation of the modern powertrain development process. This approach is often referred to as road-to-rig-to-desktop. To demonstrate the potential of this road-to-rig-to-desktop methodology as a seamless development process, a crank angle–resolved real-time engine model for a turbocharged gasoline engine was built with the simulation tool GT-POWER®. The model was validated with measurement data from an engine test bench and integrated into a vehicle co-simulation, which also includes a dual clutch transmission, the chassis, the environment and the automated driver. The most relevant functions of the engine and the transmission control systems were implemented in a Simulink-based software control unit. To verify the engine model in the transient vehicle simulation, two 900-s time windows from a 2-h real driving emission test, representing urban and motorway conditions, are simulated using the developed co-simulation platform. The simulation results are compared with the respective vehicle measurement data. The fuel consumption deviation caused by the combustion engine model is within 5%. The transient system behaviour and the dominant engine operation points could be predicted with a satisfying accuracy.

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