Energy management is an important technology for maximizing the energy efficiency of hybrid vehicles. In the process of developing cost-optimal powertrain control strategies, existing studies have explored the interactions between fuel economy and battery degradation to prolong the battery service period and reduce fuel consumption simultaneously. However, computational efficiency will be sacrificed in order to search optimal control sequences due to the nonlinear powertrain model and many optimization variables in the optimal control problem, which hinders the implementation in real-time applications. To this end, this article combines second-order cone programming and model predictive control algorithms to formulate a computationally efficient energy management strategy for a series hybrid electric vehicle. Specifically, three main contributions are made which distinguish our work from existing studies. First, based on the constructed convex powertrain model, two objectives, fuel economy and battery degradation, are optimized by the proposed hybrid algorithm. Second, a comparison study that compares the strategies with and without battery degradation optimization is presented to validate the effectiveness of the proposed control strategy. Finally, by changing the size of the prediction horizon, several simulation results are discussed to evaluate the computational efficiency of the devised method. Furthermore, the effects of different battery and fuel prices on optimized results are analyzed.


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    Titel :

    Real-Time Multiobjective Energy Management for Electrified Powertrains: A Convex Optimization-Driven Predictive Approach


    Beteiligte:
    Li, Yapeng (Autor:in) / Wang, Feng (Autor:in) / Tang, Xiaolin (Autor:in) / Lin, Xianke (Autor:in) / Liu, Changpeng (Autor:in) / Hu, Xiaosong (Autor:in)


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    3043296 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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