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Torque control is a basic element of engine control systems, in particular since it has become a standard interface for different functionalities. Torque control is also a critical requirement emission test cycle simulation on test benches. This torque control is usually reached by extensive, physical based modeling of the vehicle. This paper presents an approach to avoid this effort and to obtain a dramatic reduction of the parametrization work, by first determining an approximated model and then updating it online during operation. This model is than used for a stable inverse control. To handle model uncertainties and perturbation a correction feedback, with robustifying effect, is added to the control structure. This approach is detailed using data and measurements on a BMW M47D production diesel engine on a dynamic test bench.

The proposed vehicle structure with an integrated-starter-generator (ISG) and the possible change of the net voltage offers substantial opportunities for energy and thus fuel saving at several levels or increased dynamical performance. However, the arrangement of two power sources - combustion engine and the ISG - brings more complexity in the system and makes it necessary to control the power flow of both sources, which has to be done by an energy management system. This paper describes possible changes in the powertrain setting and a systematic approach for the design of an energy management system without using heuristic design rules. Measurement results on a dynamical test bench for the FTP-75 emission test cycle confirm the increased fuel economy and an adequate battery charge level.

Hybrid cars are a very interesting option for consumption and pollution reduction in urban areas, as they allow to reduce part load operation of the engine. The hybrid operation is based on the combination of two power sources, and it is well known that a sensible choice of the power split strategy can strongly influence the total consumption as well as the emissions. Therefore, control design is usually considered a critical issue. This paper, however, argues that it might be better not to fix a control structure, but to state the problem in terms of an optimization problem, without an a-priori control structure, and that this leads to better estimates of the achievable targets and in this way to better control results.