  This article proposes a transfer reinforcement learning (RL) based adaptive
energy managing approach for a hybrid electric vehicle (HEV) with parallel
topology. This approach is bi-level. The up-level characterizes how to
transform the Q-value tables in the RL framework via driving cycle
transformation (DCT). Especially, transition probability matrices (TPMs) of
power request are computed for different cycles, and induced matrix norm (IMN)
is employed as a critical criterion to identify the transformation differences
and to determine the alteration of the control strategy. The lower-level
determines how to set the corresponding control strategies with the transformed
Q-value tables and TPMs by using model-free reinforcement learning (RL)
algorithm. Numerical tests illustrate that the transferred performance can be
tuned by IMN value and the transfer RL controller could receive a higher fuel
economy. The comparison demonstrates that the proposed strategy exceeds the
conventional RL approach in both calculation speed and control performance.
