Reinforcement learning (RL)algorithm is employed in solving energy management problem for electrified powertrain in real-world driving scenarios and the application process is streamlined. A near-global optimal control policy is articulated for the energy management system (EMS) using Q-learning algorithm which is real-time implementable. The core of the EMS is an updating optimal control policy in the form of a changing look-up table comprising near-global optimal action value function (Q-values) corresponding to all feasible state-action combinations. Using the updating control policy, the EMS can optimally decide power-split between electric machines (EMs) and internal combustion engine (ICE) in real-world driving situations.


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    Title :

    Real-Time Optimal Energy Management of Electrified Powertrains with Reinforcement Learning


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1699549 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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