  This paper proposes an adaptive energy management strategy for hybrid
electric vehicles by combining deep reinforcement learning (DRL) and transfer
learning (TL). This work aims to address the defect of DRL in tedious training
time. First, an optimization control modeling of a hybrid tracked vehicle is
built, wherein the elaborate powertrain components are introduced. Then, a
bi-level control framework is constructed to derive the energy management
strategies (EMSs). The upper-level is applying the particular deep
deterministic policy gradient (DDPG) algorithms for EMS training at different
speed intervals. The lower-level is employing the TL method to transform the
pre-trained neural networks for a novel driving cycle. Finally, a series of
experiments are executed to prove the effectiveness of the presented control
framework. The optimality and adaptability of the formulated EMS are
illuminated. The founded DRL and TL-enabled control policy is capable of
enhancing energy efficiency and improving system performance.
