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The ever-increasing cost of automotive powertrain development is due to the more complex technologies required to meet the latest emissions legislation and customer expectations. Manufacturers need to conduct extensive development loops of test bench and on-road testing to verify the hardware, emission control system, corresponding ECU software function development. Increased resources are required to build up a comparably large number of prototype vehicles to calibrate all the ECU algorithms and functionalities. Increasing powertrain complexity leads typically to a strong increase of conventional calibration efforts. Therefore, there is a strongly increasing need for an advanced calibration approach based on multi-facial XiL simulation.

Advanced powertrains for modern vehicles require the optimization of conventional combustion engines in combination with tailored electrification and vehicle connectivity strategies. The resulting systems and their control devices feature many degrees of freedom with a large number of available adjustment parameters. This obviously presents major challenges to the development of the corresponding powertrain control logics. Hence, the identification of an optimal system calibration is a non-trivial task. To address this situation, physics-based control approaches are evolving and successively replacing conventional map-based control strategies in order to handle more complex powertrain topologies. Physics-based control approaches enable a significant reduction in calibration effort, and also improve the control robustness.