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Journal Article

Crank-Angle Resolved Real-Time Engine Modelling: A Seamless Transfer from Concept Design to HiL Testing

Virtual system integration and testing using hardware-in-the-loop (HiL) simulation enables front-loading of development tasks, provides a safer and reliable testing environment and reduces prototype hardware costs. One of the greatest challenges to overcome when performing HiL simulations is assuring a high model accuracy under stringent real-time requirements with acceptable development effort. This article represents a novel solution by deriving the plant model for HiL directly from the existing detailed models from the component layout phase using co-simulation methodology. It provides an effective and efficient model implementation and validation process followed by detailed quantitative analysis of the test results referred to the engine test bench measurements.
Journal Article

Hardware-in-the-Loop-Based Virtual Calibration Approach to Meet Real Driving Emissions Requirements

The use of state-of-the-art model-based calibration tools generate only limited benefits for seamless validation in powertrain calibration due to the often neglected system-level simulation of a closed-loop vehicle environment. This study presents a Hardware-in-the-Loop (HiL)-based virtual calibration approach to establish an accurate virtual calibration platform using physical plant models. It is based on a customisable real-time HiL simulation environment. The use of physical models to predict the behaviour of a complete powertrain makes the HiL test bench particularly suited for Engine Control Unit (ECU) calibration. With the virtual test rig approach, the calibration for the critical extended driving and ambient conditions of the new Real Driving Emissions (RDE) requirements can efficiently be optimised. This technique offers a clear advantage in terms of reducing calibration time and costs.
Technical Paper

Scalable Mean Value Modeling for Real-Time Engine Simulations with Improved Consistency and Adaptability

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.