With increasing fuel prices, minimization of fuel consumption is an important factor in achieving low total cost of ownership in commercial vehicles. At the same time, legislative requirements with regard to emissions, on-board diagnosis and in-use compliance have to be fulfilled. Complex aftertreatment systems that need to be operated in regions of high efficiency are necessary to achieve these requirements. It can be expected that due to the potential in increasing overall system efficiency, hybrid and heat recuperation systems will even further increase the complexity of commercial vehicle powertrains in the near future. In order to optimize emissions and fuel consumption under real driving conditions, a holistic view of the overall vehicle is necessary and the engine control unit has to evolve to an energy flow coordinator managing the energy flow between the individual components of the powertrain. In this study, two approaches for such an energy flow controller are exemplarily implemented and tested within a longitudinal vehicle simulation. The ECMS (Equivalent Consumption Minimization Strategy) is fast and can be implemented real-time capable on current controller hardware. In the shown implementation, it only includes a single strategy to manage the catalyst temperature and would need to be extended to account for other thermal management measures. The MPC (Model Predictive Control) offers the potential to predict the operation of the vehicle within a certain time horizon. The implementation shown here certainly is only a first demonstration and needs further improvements. The approach used to predict the required future torques is based on a static operation map of the vehicle and the assumption of a constant future velocity. For this reason any dynamical effects appearing during breaking and acceleration are not taken into account. Compared to the ECMS, the implementation of the MPC shown here does not yet include the efficiency of the aftertreatment system and would also have to account for thermal management measures applied by the combustion system. In addition, the formulation of an optimization problem being feasible in all driving conditions turned out to be difficult. For a real-life application, the MPC would need to be extended and made more robust. For example, the total vehicle weight will differ within a certain range depending on the cargo. This would have a significant effect on the predicted results, making the total vehicle weight an important additional input to the MPC. Furthermore, acceleration and deceleration might also be included as additional input variables based on navigation system information and possibly vehicle-to-vehicle communication. However, as such an input is highly susceptible to faults, it might be combined with a learning algorithm for the model gains.


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    Titel :

    Management of energy flow in complex commercial vehicle powertrains


    Weitere Titelangaben:

    Management des Energiestroms in komplexen Antriebssträngen von Nutzfahrzeugen


    Beteiligte:
    Seebode, J. (Autor:in) / Ecker, P. (Autor:in) / Henning, L. (Autor:in) / Behnk, K. (Autor:in)


    Erscheinungsdatum :

    2011


    Format / Umfang :

    19 Seiten, 11 Bilder, 2 Tabellen, 10 Quellen


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Datenträger


    Sprache :

    Englisch




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    Seebode, Joern / Behnk, Kai / Henning, Lars et al. | SAE Technical Papers | 2012


    Management of energy flow in complex commercial vehicle powertrains

    Seebode,J. / Eckert,P. / Henning,L. et al. | Kraftfahrwesen | 2011


    Management of Energy Flow in Complex Commercial Vehicle Powertrains

    Eckert, P. / Henning, L. / Rezaei, R. et al. | British Library Conference Proceedings | 2012


    Trends in commercial vehicle powertrains

    Esch,T. / FH Aachen,DE | Kraftfahrwesen | 2010