The increasing sensing and computational capacities of vehicles enable novel opportunities for optimal control strategies, including powertrain control strategies to improve fuel economy. In this chapter, we present the concept, implementation, and test results for one such strategy, which together represent the culmination of years of research collaboration between academia and industry. Our strategy leverages predictions of acceleration behavior to optimize the dynamic allocation of torque between two sources, the internal combustion engine and the electric motor, for a hybrid electric vehicle. Preliminary development stages included detailed modeling and simulation to demonstrate feasibility and fuel economy improvement potential. Implementation involved the design and fabrication of a test vehicle platform on which the control strategy was installed and actuated. Results of tests using this platform demonstrate an improvement to fuel economy of more than 28% relative to a baseline, non-predictive control strategy.


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    Titel :

    Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems: Predictive Control During Acceleration Events to Improve Fuel Economy


    Beteiligte:
    Kukkala, Vipin Kumar (Herausgeber:in) / Pasricha, Sudeep (Herausgeber:in) / White, Samantha (Autor:in) / Rabinowitz, Aaron (Autor:in) / Ang, Chon Chia (Autor:in) / Trinko, David (Autor:in) / Bradley, Thomas (Autor:in)


    Erscheinungsdatum :

    2023-03-27


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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