Objectified Evaluation and Classification of Passenger Vehicles Longitudinal Drivability Capabilities in Automated Load Change Drive Maneuvers at Engine-in-the-Loop Test Benches 2020-01-0245
The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks. Usually, drivability calibration takes place towards the end of the vehicle development program as soon as a sufficient level of product maturity is achieved. Hence, for streamlining the entire development process, various powertrain engineering tasks need to be shifted from the overall vehicle level to component conception phases. In this context, highly dynamic and appropriated “Hardware-in-the-Loop” (HiL) component test benches are the means of choice. Particularly for drivability calibration tasks, an objectified evaluation and classification approach is indispensable to be applied at HiL test benches for the identification and evaluation of drivability influencing factors during those heterogeneous testing scenarios.
Before this backdrop, this article presents the transfer and validation of an objectification and classification approach for longitudinal vehicle drivability capabilities from vehicle level to highly dynamic “Engine-in-the-Loop” (EiL) test benches. First, for two different “engine control unit” (ECU) drivability calibration data sets (sporty and comfort), a multitude of automated longitudinal drive maneuvers is performed in various gears and for different start conditions with a real vehicle on a proving ground. Subsequently, these test procedures are reproduced with the same combustion engine and ECU version at an EiL test bench. Thereto, modifications and enhancements to the test bench hardware, automation system and the real-time control models are required. Their influence on the objectified drivability test results and the degree of congruence between the drivability measurements with the real vehicle on the road and the EiL test bench are determined. In this context, internal combustion engine (ICE) related measurement data are presented for individual load change drive maneuvers. By utilizing a drivability objectification approach, the differences between the two ECU drivability calibrations are determined for both test scenarios. Furthermore, various characteristic drivability attributes are illustrated and explained for several gears for both test scenarios and also directly compared to each other. In addition, the gear specific identification and reflecting quality for the different characteristic drivability attributes for both ECU calibrations are illustrated and discussed in detail by utilizing characteristic 3D diagrams. The transferability of the drivability evaluation approach from the real vehicle to the EiL test bench is validated successfully. A good correlation in the characteristic drivability attributes can be achieved between both test scenarios.
Citation: Guse, D., Heusch, C., Klein, S., Fahrbach, T. et al., "Objectified Evaluation and Classification of Passenger Vehicles Longitudinal Drivability Capabilities in Automated Load Change Drive Maneuvers at Engine-in-the-Loop Test Benches," SAE Technical Paper 2020-01-0245, 2020, https://doi.org/10.4271/2020-01-0245. Download Citation
Daniel Guse, Christian Heusch, Serge Klein, Timm Fahrbach, Jakob Andert, Stefan Pischinger, Stefan Tegelkamp, Martin Nijs, Johannes Scharf
RWTH Aachen University, FEV Europe GmbH