In recent years, researchers and manufacturers have increased their interest on predictive control strategies for light-duty vehicles, based on electronic horizon availability. Despite this involvement, the on-board implementation of predictive features is still limited in modern automotive control systems. This paper deals with the development of a predictive NOx emissions control function for a diesel hybrid electric vehicle, equipped with an electrically heated after-treatment system composed by a Diesel Oxidation Catalyst (DOC), a Diesel Particulate Filter (DPF), and a Selective Catalytic Reactor (SCR). Such function makes use of an a-priori-known vehicle speed trajectory that would be made available by the electronic horizon provider, and it presents two main sections. The first one predicts the aftertreatment system temperature and the NOx emissions both at the engine out and at the tailpipe over the prediction horizon. The second section defines the powertrain and after-treatment control policy, with the objective of minimizing after-treatment electric heating energy and SCR urea consumption, while respecting the legal NOx limits for the given mission. Furthermore, if the estimated pollutant production exceeds the limits even if the aftertreatment system is operated in the highest efficiency conditions, the predictive control function redefines the torque demanded to the internal combustion engine (and the one requested to the electric motor) to match the legal limits. In terms of results, this novel approach to emissions control shows the benefits coming from the usage of predictive information in combination with powertrain hybridization, and it can be applied to any HEV configuration.


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    Titel :

    Predictive NOx emission control of a diesel-HEV for CO2 and urea consumption reduction


    Beteiligte:

    Erscheinungsdatum :

    2019-01-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629





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