  Real-time applications of energy management strategies (EMSs) in hybrid
electric vehicles (HEVs) are the harshest requirements for researchers and
engineers. Inspired by the excellent problem-solving capabilities of deep
reinforcement learning (DRL), this paper proposes a real-time EMS via
incorporating the DRL method and transfer learning (TL). The related EMSs are
derived from and evaluated on the real-world collected driving cycle dataset
from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is
proximal policy optimization (PPO) belonging to the policy gradient (PG)
techniques. For specification, many source driving cycles are utilized for
training the parameters of deep network based on PPO. The learned parameters
are transformed into the target driving cycles under the TL framework. The EMSs
related to the target driving cycles are estimated and compared in different
training conditions. Simulation results indicate that the presented transfer
DRL-based EMS could effectively reduce time consumption and guarantee control
performance.
