  Energy management strategies (EMSs) are the most significant components in
hybrid electric vehicles (HEVs) because they decide the potential of energy
conservation and emission reduction. This work presents a transferred EMS for a
parallel HEV via combining the reinforcement learning method and driving
conditions recognition. First, the Markov decision process (MDP) and the
transition probability matrix are utilized to differentiate the driving
conditions. Then, reinforcement learning algorithms are formulated to achieve
power split controls, in which Q-tables are tuned by current driving
situations. Finally, the proposed transferred framework is estimated and
validated in a parallel hybrid topology. Its advantages in computational
efficiency and fuel economy are summarized and proved.
