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.

Real-Time Optimal Energy Management of Electrified Powertrains with Reinforcement Learning / Biswas, Atriya; Anselma, Pier G.; Emadi, Ali. - (2019), pp. 1-6. (Intervento presentato al convegno 2019 IEEE Transportation Electrification Conference and Expo (ITEC) tenutosi a Detroit, MI, USA, USA nel 19-21 June 2019) [10.1109/ITEC.2019.8790482].

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

Anselma, Pier G.;
2019

Abstract

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.
2019
978-1-5386-9310-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2747887
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