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

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.