An innovative Connected Optimal Predictive Control is proposed in this paper for Connected Energy Management purposes applied to Hybrid Vehicles, for minimization of energy and CO2 during a given trip, according to the driving conditions that can be predicted by intelligent navigation systems with real-time connectivity to the Cloud [3]. The theory proposed for such real-time optimal predictive algorithms is based on the mathematical Pontryagins Maximum Principle (“PMP”) [1] [2], that provides general solutions for optimization of dynamic systems with integral criteria, under given constraints. Several technical approaches are presented to get feasible real-time solving computation for this dynamic optimization. The calculation of a “trip planning” becomes then possible in embedded controllers synchronized to more powerful servers and computers connected to the Vehicle. Significant gains of more than −10% of CO2 are demonstrated, maintaining acceptable performances and drivability.


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    Title :

    Connected optimal predictive control for hybrid vehicles


    Contributors:


    Publication date :

    2016-11-01


    Size :

    1364963 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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