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
01.06.2019
1699549 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vehicle Power Test for Electrified Powertrains
SAE Technical Papers | 2017
Vehicle system simulation for electrified & conventional powertrains
Tema Archiv | 2012
|Vehicle system simulation for electrified & conventional powertrains
British Library Conference Proceedings | 2012
|Electric machine design approaches for electrified powertrains
British Library Conference Proceedings | 2018
|