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Mr. John Smith

Job title



In the transport sector, the reduction of real driving emissions and fuel consumption in long haul traffic is one of the main societal challenges. Regulations e.g. Euro VI are a baseline with respect to targets in defined test environment conditions, while a clear need exist to have real driving efficiency measurements to promote the introduction of innovative solutions for fuel consumption and emission reductions. Reduction of CO2 and pollutant emissions interact with each other and vary with the specific vehicle application, operating conditions and mission. The overall objective of this work is the development of new means of predictive and comprehensive powertrain control in an optimal way, exploiting to the full potential of the individual systems for each vehicle application and mission. The pre-condition for predictive vehicle control is the knowledge on future velocity profile. A dynamic eHorizon system is developed and applied in 3 different trucks with a “look ahead” capability of delivering static and dynamic data with respect to the road ahead. Control strategy improvements are implemented in the following main technical clusters: - Global powertrain and vehicle supervisor, - Hybridization, - Engine and EAS control, - Thermal management The first improvement approach targets existing control strategies. This will mostly rely on migration from direct control strategies to model-based control strategies as well as on integration of existing strategies. The model-based control strategy approach relies on a mathematical model of the physics of parts of the system (e.g. combustion), which is running in real time on the computing platform and can provide more accurate information of the current state of the system. Furthermore, a mathematical (sub) system description is the base to use predictive control strategies. The integration of different control enables tighter synchronization between the (highly dependent) systems and, therefore, improved performance. The second improvement approach targets the introduction of extended and predictive input data. This additional data will provide predictive information of the vehicle mission and environment situation, therefore enabling predictive strategies that can control the systems according to events that will occur with high probability in the short-term future. Examples are the identification of hills or traffic jams and the resulting tailoring of the control strategies up to activation or deactivation of specific auxiliaries according to this information. The dependency on traffic directly leads to the problem of non-reproducible validation measurements on real roads. Therefore, a simulation – based validation strategy is developed. The simulation elements of the trucks are validated on various platforms like HiL, engine testbed and demonstrator trucks. Next the question of “representable traffic conditions” was solved by the analysis of European traffic routes and transfer of typical traffic scenarios on the VECTO CO2 Long-Haul cycle. The simulation platform enables interaction of the ego-vehicle with defined traffic scenarios for validation. This contribution presents selected outcomes and overall results of the EU-funded 3-year research program IMPERIUM with a fuel consumption reduction of -20% compared to model - year 2014 vehicle.

Dr. Alois Danninger, AVL, AUSTRIA Dipl.-Ing. Uwe Martin, AVL List GmbH, AUSTRIA

CO2 reduction for long haul trucks by predictive control

F2020-ADM-014 • Paper + Video • FISITA World Congress 2021 • ADM - Advanced Vehicle Driveline and Energy Management